diff --git a/.flake8 b/.flake8 index ec41ff9f7..c0c7c7574 100644 --- a/.flake8 +++ b/.flake8 @@ -4,4 +4,4 @@ exclude = .venv docstring-convention = google max-line-length = 88 -extend-ignore = ANN101 +extend-ignore = ANN101, E203 diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index de836f17f..95d1015ba 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -25,6 +25,9 @@ jobs: steps: - name: Checkout code uses: actions/checkout@v3 + with: + ref: ${{ env.BRANCH }} + repository: ${{ env.REPO }} - name: Set up Python uses: actions/setup-python@v3 @@ -35,12 +38,6 @@ jobs: if: matrix.os == 'macos-latest' run: sh install/install_openadapt.sh - - name: Checkout code - uses: actions/checkout@v3 - with: - ref: ${{ env.BRANCH }} - repository: ${{ env.REPO }} - - name: Install poetry uses: snok/install-poetry@v1 with: @@ -63,7 +60,7 @@ jobs: if: steps.cache-deps.outputs.cache-hit == 'true' - name: Check formatting with Black - run: poetry run black --preview --check . + run: poetry run black --preview --check . --exclude '/(alembic|\.venv)/' - name: Run Flake8 - run: poetry run flake8 + run: poetry run flake8 --exclude=alembic,.venv diff --git a/README.md b/README.md index b985ca316..3db408af1 100644 --- a/README.md +++ b/README.md @@ -189,6 +189,11 @@ python -m openadapt.replay NaiveReplayStrategy Other replay strategies include: - [`StatefulReplayStrategy`](https://github.com/OpenAdaptAI/OpenAdapt/blob/main/openadapt/strategies/stateful.py): Proof-of-concept which uses the OpenAI GPT-4 API with prompts constructed via OS-level window data. +- [`VisualReplayStrategy`](https://github.com/OpenAdaptAI/OpenAdapt/blob/main/openadapt/strategies/visual.py): Uses [Fast Segment Anything Model (FastSAM)](https://github.com/CASIA-IVA-Lab/FastSAM) to segment active window. Accepts an "instructions" parameter that is used to modify the recording, e.g.: + +``` +python -m openadapt.replay VisualReplayStrategy --instructions "Multiply 9x5 instead of 6x8" +``` See https://github.com/OpenAdaptAI/OpenAdapt/tree/main/openadapt/strategies for a complete list. More ReplayStrategies coming soon! (see [Contributing](#Contributing)). diff --git a/alembic/versions/30a5ba9d6453_add_active_segment_description_and_.py b/alembic/versions/30a5ba9d6453_add_active_segment_description_and_.py new file mode 100644 index 000000000..1b8d94918 --- /dev/null +++ b/alembic/versions/30a5ba9d6453_add_active_segment_description_and_.py @@ -0,0 +1,34 @@ +"""add active_segment_description and available_segment_descriptions + +Revision ID: 30a5ba9d6453 +Revises: 530f0663324e +Create Date: 2024-04-05 12:02:57.843244 + +""" +from alembic import op +import sqlalchemy as sa + + +# revision identifiers, used by Alembic. +revision = '30a5ba9d6453' +down_revision = '530f0663324e' +branch_labels = None +depends_on = None + + +def upgrade() -> None: + # ### commands auto generated by Alembic - please adjust! ### + with op.batch_alter_table('action_event', schema=None) as batch_op: + batch_op.add_column(sa.Column('active_segment_description', sa.String(), nullable=True)) + batch_op.add_column(sa.Column('available_segment_descriptions', sa.String(), nullable=True)) + + # ### end Alembic commands ### + + +def downgrade() -> None: + # ### commands auto generated by Alembic - please adjust! ### + with op.batch_alter_table('action_event', schema=None) as batch_op: + batch_op.drop_column('available_segment_descriptions') + batch_op.drop_column('active_segment_description') + + # ### end Alembic commands ### diff --git a/openadapt/adapters/__init__.py b/openadapt/adapters/__init__.py new file mode 100644 index 000000000..cc7446303 --- /dev/null +++ b/openadapt/adapters/__init__.py @@ -0,0 +1,40 @@ +"""Adapters for completion and segmentation.""" + +from types import ModuleType + +from openadapt import config +from . import anthropic +from . import openai +from . import replicate +from . import som +from . import ultralytics +from . import google + + +def get_default_prompt_adapter() -> ModuleType: + """Returns the default prompt adapter module. + + Returns: + The module corresponding to the default prompt adapter. + """ + return { + "openai": openai, + "anthropic": anthropic, + "google": google, + }[config.DEFAULT_ADAPTER] + + +def get_default_segmentation_adapter() -> ModuleType: + """Returns the default image segmentation adapter module. + + Returns: + The module corresponding to the default segmentation adapter. + """ + return { + "som": som, + "replicate": replicate, + "ultralytics": ultralytics, + }[config.DEFAULT_SEGMENTATION_ADAPTER] + + +__all__ = ["anthropic", "openai", "replicate", "som", "ultralytics", "google"] diff --git a/openadapt/adapters/anthropic.py b/openadapt/adapters/anthropic.py new file mode 100644 index 000000000..dfc0e35ec --- /dev/null +++ b/openadapt/adapters/anthropic.py @@ -0,0 +1,146 @@ +"""Adapter for Anthropic API with vision support.""" + +from pprint import pprint + +from loguru import logger +import anthropic + +from openadapt import cache, config + + +MAX_TOKENS = 4096 +# from https://docs.anthropic.com/claude/docs/vision +MAX_IMAGES = 20 +MODEL_NAME = "claude-3-opus-20240229" + + +@cache.cache() +def create_payload( + prompt: str, + system_prompt: str | None = None, + base64_images: list[tuple[str, str]] | None = None, + model: str = MODEL_NAME, + max_tokens: int | None = None, +) -> dict: + """Creates the payload for the Anthropic API request with image support.""" + messages = [] + + user_message_content = [] + + max_tokens = max_tokens or MAX_TOKENS + if max_tokens > MAX_TOKENS: + logger.warning(f"{max_tokens=} > {MAX_TOKENS=}") + max_tokens = MAX_TOKENS + + # Add base64 encoded images to the user message content + if base64_images: + for image_data in base64_images: + # Extract media type and base64 data + media_type, base64_str = image_data.split(";base64,", 1) + media_type = media_type.split(":")[-1] # Remove 'data:' prefix + + user_message_content.append( + { + "type": "image", + "source": { + "type": "base64", + "media_type": media_type, + "data": base64_str, + }, + } + ) + + # Add text prompt + user_message_content.append( + { + "type": "text", + "text": prompt, + } + ) + + # Construct user message + messages.append( + { + "role": "user", + "content": user_message_content, + } + ) + + # Prepare the full payload + payload = { + "model": model, + "max_tokens": max_tokens, + "messages": messages, + } + + # Add system_prompt as a top-level parameter if provided + if system_prompt: + payload["system"] = system_prompt + + return payload + + +client = anthropic.Anthropic(api_key=config.ANTHROPIC_API_KEY) + + +@cache.cache() +def get_completion(payload: dict) -> str: + """Sends a request to the Anthropic API and returns the response.""" + try: + response = client.messages.create(**payload) + except Exception as exc: + logger.exception(exc) + import ipdb + + ipdb.set_trace() + """ + Message( + id='msg_01L55ai2A9q92687mmjMSch3', + content=[ + ContentBlock( + text='{ + "action": [ + { + "name": "press", + "key_name": "cmd", + "canonical_key_name": "cmd" + }, + ... + ] + }', + type='text' + ) + ], + model='claude-3-opus-20240229', + role='assistant', + stop_reason='end_turn', + stop_sequence=None, + type='message', + usage=Usage(input_tokens=4379, output_tokens=109)) + """ + texts = [content_block.text for content_block in response.content] + return "\n".join(texts) + + +def prompt( + prompt: str, + system_prompt: str | None = None, + base64_images: list[str] | None = None, + max_tokens: int | None = None, +) -> str: + """Public method to get a response from the Anthropic API with image support.""" + if len(base64_images) > MAX_IMAGES: + # XXX TODO handle this + raise Exception( + f"{len(base64_images)=} > {MAX_IMAGES=}. Use a different adapter." + ) + payload = create_payload( + prompt, + system_prompt, + base64_images, + max_tokens=max_tokens, + ) + # pprint(f"payload=\n{payload}") # Log payload for debugging + result = get_completion(payload) + pprint(f"result=\n{result}") # Log result for debugging + return result diff --git a/openadapt/adapters/google.py b/openadapt/adapters/google.py new file mode 100644 index 000000000..69f1511b3 --- /dev/null +++ b/openadapt/adapters/google.py @@ -0,0 +1,74 @@ +"""Adapter for Google Gemini. + +See https://ai.google.dev/tutorials/python_quickstart for documentation. +""" + +from pprint import pprint + +from PIL import Image +import fire +import google.generativeai as genai + +from openadapt import cache, config, utils + + +MAX_TOKENS = 2**20 # 1048576 +MODEL_NAME = [ + "gemini-pro-vision", + "models/gemini-1.5-pro-latest", +][-1] +# https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/send-multimodal-prompts +MAX_IMAGES = { + "gemini-pro-vision": 16, + "models/gemini-1.5-pro-latest": 3000, +}[MODEL_NAME] + + +@cache.cache() +def prompt( + prompt: str, + system_prompt: str | None = None, + base64_images: list[str] | None = None, + # max_tokens: int | None = None, + model_name: str = MODEL_NAME, +) -> str: + """Public method to get a response from the Google API with image support.""" + full_prompt = "\n\n###\n\n".join([s for s in (system_prompt, prompt) if s]) + # HACK + full_prompt += "\nWhen responding in JSON, you MUST use double quotes around keys." + + # TODO: modify API across all adapters to accept PIL.Image + images = ( + [utils.utf82image(base64_image) for base64_image in base64_images] + if base64_images + else [] + ) + + genai.configure(api_key=config.GOOGLE_API_KEY) + model = genai.GenerativeModel(model_name) + response = model.generate_content([full_prompt] + images) + response.resolve() + pprint(f"response=\n{response}") # Log response for debugging + return response.text + + +def main(text: str, image_path: str | None = None) -> None: + """Prompt Google Gemini with text and a path to an image.""" + if image_path: + with Image.open(image_path) as img: + # Convert image to RGB if it's RGBA (to remove alpha channel) + if img.mode in ("RGBA", "LA") or ( + img.mode == "P" and "transparency" in img.info + ): + img = img.convert("RGB") + base64_image = utils.image2utf8(img) + else: + base64_image = None + + base64_images = [base64_image] if base64_image else None + output = prompt(text, base64_images=base64_images) + print(output) + + +if __name__ == "__main__": + fire.Fire(main) diff --git a/openadapt/adapters/openai.py b/openadapt/adapters/openai.py new file mode 100644 index 000000000..7e9b9b05d --- /dev/null +++ b/openadapt/adapters/openai.py @@ -0,0 +1,177 @@ +"""Adapter for GPT4-V API. + +https://platform.openai.com/docs/guides/vision +""" + +from pprint import pformat +import requests + +from loguru import logger + +from openadapt import cache, config + +MODEL_NAME = [ + "gpt-4-vision-preview", + "gpt-4-turbo-2024-04-09", +][-1] +MAX_TOKENS = 4096 +# TODO XXX undocumented +MAX_IMAGES = None + + +def create_payload( + prompt: str, + system_prompt: str | None = None, + base64_images: list[str] | None = None, + model: str = MODEL_NAME, + detail: str = "high", # "low" or "high" + max_tokens: int | None = None, +) -> dict: + """Create payload for prompting. + + Args: + prompt: the prompt + system_prompt: the system prompt + base64_images: list of base64 encoded images + model: name of OpenAI model + detail: detail level of images, "low" or "high" + max_tokens: maximum number of tokens + + Returns: + dict containing prompt payload + """ + max_tokens = max_tokens or MAX_TOKENS + if max_tokens > MAX_TOKENS: + logger.warning(f"{max_tokens=} {MAX_TOKENS=}") + max_tokens = MAX_TOKENS + + """Creates the payload for the API request.""" + messages = [ + { + "role": "user", + "content": [ + { + "type": "text", + "text": prompt, + }, + ], + }, + ] + + base64_images = base64_images or [] + for base64_image in base64_images: + messages[0]["content"].append( + { + "type": "image_url", + "image_url": { + "url": base64_image, + "detail": detail, + }, + } + ) + + if system_prompt: + messages = [ + { + "role": "system", + "content": [ + { + "type": "text", + "text": system_prompt, + } + ], + } + ] + messages + + rval = { + "model": model, + "messages": messages, + } + if max_tokens: + rval["max_tokens"] = max_tokens + return rval + + +@cache.cache() +def get_response(payload: dict) -> requests.Response: + """Sends a request to the OpenAI API and returns the response. + + Args: + payload: dictionary returned by create_payload + + Returns: + response from OpenAI API + """ + headers = { + "Content-Type": "application/json", + "Authorization": f"Bearer {config.OPENAI_API_KEY}", + } + response = requests.post( + "https://api.openai.com/v1/chat/completions", + headers=headers, + json=payload, + ) + return response + + +def get_completion(payload: dict) -> str: + """Sends a request to the OpenAI API and returns the first message. + + Args: + pyalod: dictionary returned by create_payload + + Returns: + string containing the first message from the response + """ + response = get_response(payload) + result = response.json() + logger.info(f"result=\n{pformat(result)}") + if "error" in result: + error = result["error"] + message = error["message"] + # TODO: fail after maximum number of attempts + if "retry your request" in message: + return get_completion(payload) + else: + import ipdb + + ipdb.set_trace() + # TODO: handle more errors + choices = result["choices"] + choice = choices[0] + message = choice["message"] + content = message["content"] + return content + + +def prompt( + prompt: str, + system_prompt: str | None = None, + base64_images: list[str] | None = None, + max_tokens: int | None = None, + detail: str = "high", +) -> str: + """Get prompt completion from OpenAI. + + Args: + prompt: the prompt + system_prompt: the system prompt + base64_images: list of base64 encoded images + model: name of OpenAI model + detail: detail level of images, "low" or "high" + max_tokens: maximum number of tokens + + Returns: + string containing the first message from the response + """ + payload = create_payload( + prompt, + system_prompt, + base64_images, + max_tokens=max_tokens, + detail=detail, + ) + logger.info(f"payload=\n{pformat(payload)}") + result = get_completion(payload) + logger.info(f"result=\n{pformat(result)}") + return result diff --git a/openadapt/adapters/replicate.py b/openadapt/adapters/replicate.py new file mode 100644 index 000000000..2a2f282f7 --- /dev/null +++ b/openadapt/adapters/replicate.py @@ -0,0 +1,105 @@ +"""Adapter for getting segmentation via Replicate API.""" + +import io +import os + +from loguru import logger +from PIL import Image +import fire +import replicate +import requests + +from openadapt import cache, config, utils + + +@cache.cache() +def _fetch_segmented_image(image_uri: str) -> Image.Image: + """Fetch segmented image. + + For details, see: + https://replicate.com/pablodawson/segment-anything-automatic/api?tab=python#run + + Args: + image_uri: URI of image to segment + + Returns: + PIL Image containing segmented image + """ + os.environ["REPLICATE_API_TOKEN"] = config.REPLICATE_API_TOKEN + logger.info("segmenting image...") + segmented_image_url = replicate.run( + ( + "pablodawson/segment-anything-automatic:" + "14fbb04535964b3d0c7fad03bb4ed272130f15b956cbedb7b2f20b5b8a2dbaa0" + ), + input={ + "image": image_uri, + # "resize_width": 1080, + # "min_mask_region_area": 30 + }, + ) + logger.info(f"{segmented_image_url=}") + image_data = requests.get(segmented_image_url).content + image = Image.open(io.BytesIO(image_data)) + return image + + +def fetch_segmented_image(image: Image, n: int | None = 100) -> Image: + """Fetch a segmented image from Segment Anything on Replicate. + + The image resolution is modified by a percent ratio before the segmentation + request, and the segmented image is resized back to the original + resolution. + + Args: + image: The input image to be segmented. + n: Optional; The percent ratio to adjust the resolution of the input + image before segmentation. Default is 100 (no adjustment). + + Returns: + The segmented image, resized back to the original resolution if it was adjusted. + """ + # Store the original dimensions + original_width, original_height = image.size + + # Adjust resolution if necessary + if n is not None and n != 100: + new_width = int(original_width * n / 100) + new_height = int(original_height * n / 100) + image = image.resize((new_width, new_height), Image.ANTIALIAS) + + # Convert adjusted image to a format suitable for segmentation + image_uri = utils.image2utf8(image) + + # Fetch the segmented image + segmented_image = _fetch_segmented_image(image_uri) + + # Resize the segmented image back to the original dimensions + segmented_image = segmented_image.resize( + (original_width, original_height), Image.NEAREST + ) + + return segmented_image + + +def fetch_segmented_image_from_path(image_path: str, n: int | None = 100) -> None: + """Fetch a segmented image and save it to the same directory. + + '-segmented' is added before the file extension. + + Args: + image_path: string containing path to image + n: Optional; The percent ratio to adjust the resolution of the input + image before segmentation. Default is 100 (no adjustment). + """ + with Image.open(image_path) as image: + segmented_image = fetch_segmented_image(image, n) + # Construct new file name with '-segmented' before the extension + base, ext = os.path.splitext(image_path) + segmented_image_path = f"{base}-segmented{ext}" + segmented_image.save(segmented_image_path) + logger.info(f"Segmented image saved to: {segmented_image_path}") + + +if __name__ == "__main__": + fire.Fire(fetch_segmented_image_from_path) diff --git a/openadapt/adapters/som.py b/openadapt/adapters/som.py new file mode 100644 index 000000000..a0df19ad8 --- /dev/null +++ b/openadapt/adapters/som.py @@ -0,0 +1,100 @@ +"""Adapter for segmenting images via Set-of-Mark server. + +See https://github.com/microsoft/SoM for server implementation. +""" + +import os +import tempfile + +from loguru import logger +from PIL import Image +import fire +import gradio_client + +from openadapt import config + + +def save_image_to_temp_file(img: Image.Image) -> str: + """Save PIL.Image to a temporary file and return the file path. + + Args: + img: the PIL Image to save + + Returns: + Name of temporary file containing saved image. + """ + temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".png") + img.save(temp_file, format="PNG") + temp_file.close() # Important to close the file so others can access it + logger.info(f"Image saved to temporary file: {temp_file.name}") + + return temp_file.name + + +def predict( + file_path: str = None, + server_url: str = config.SOM_SERVER_URL, + granularity: float = 2.7, + segmentation_mode: str = "Automatic", + mask_alpha: float = 0.8, + mark_mode: str = "Number", + annotation_mode: list = ["Mask"], + api_name: str = "/inference", +) -> str: + """Make a prediction using the Gradio client with the provided IP address. + + Args: + server_url (str): The URL of the SoM Gradio server. + file_path (str): File path of temp image (.png) copy of a PIL image object. + granularity (float): Granularity value for the operation. + segmentation_mode (str): Mode of segmentation ('Automatic' or 'Interactive'). + mask_alpha (float): Alpha value for the mask. + mark_mode (str): Mark mode, either 'Number' or 'Alphabet'. + annotation_mode (list): List of annotation modes. + api_name (str): API endpoint name for inference. + + Returns: + Path to segmented image. + """ + assert server_url, server_url + assert server_url.startswith("http"), server_url + client = gradio_client.Client(server_url) + result = client.predict( + { + "background": gradio_client.file(file_path), + }, + granularity, + segmentation_mode, + mask_alpha, + mark_mode, + annotation_mode, + api_name=api_name, + ) + logger.info(result) + + return result + + +def fetch_segmented_image(image: Image.Image) -> Image.Image: + """Process an image using PIL.Image.Image object and predict using Gradio client. + + Args: + image: The PIL Image to segment. + + Returns: + The segmented PIL Image. + """ + raise NotImplementedError( + "SoM server currently compresses the segmented image, " + "resulting in many more colors than masks." + ) + img_temp_path = save_image_to_temp_file(image) # Save the image to a temp file + segmented_image_path = predict(file_path=img_temp_path) # Perform prediction + os.remove(img_temp_path) # Delete the temp file after prediction + image = Image.open(segmented_image_path) + os.remove(segmented_image_path) + return image + + +if __name__ == "__main__": + fire.Fire(predict) diff --git a/openadapt/adapters/ultralytics.py b/openadapt/adapters/ultralytics.py new file mode 100644 index 000000000..d7d44b55d --- /dev/null +++ b/openadapt/adapters/ultralytics.py @@ -0,0 +1,118 @@ +"""Segmentation adapter using Ultralytics Fast Segment Anything. + +See https://docs.ultralytics.com/models/fast-sam/#predict-usage for details. +""" + +from tempfile import TemporaryDirectory +from pathlib import Path +import os + +from loguru import logger +from PIL import Image +from ultralytics import FastSAM +from ultralytics.models.fastsam import FastSAMPrompt +import fire +import numpy as np + +from openadapt import cache + + +MODEL_NAMES = ( + "FastSAM-x.pt", + "FastSAM-s.pt", +) +MODEL_NAME = MODEL_NAMES[0] + + +# TODO: rename +@cache.cache() +def fetch_segmented_image( + image: Image, + model_name: str = MODEL_NAME, + # TODO: inject from config + device: str = "cpu", + retina_masks: bool = True, + imgsz: int = 1024, + conf: float = 0.4, + iou: float = 0.9, +) -> Image: + """Segment a PIL.Image using ultralytics Fast Segment Anything. + + Args: + image: The input image to be segmented. + model_name: The name of the model to use. Must be FastSAM-s.pt or FastSAM-x.pt. + TODO: document remaining params + + Returns: + The segmented image as a PIL Image. + """ + assert model_name in MODEL_NAMES, (model_name, MODEL_NAMES) + + model = FastSAM(model_name) + + # Run inference on image + everything_results = model( + image, + device=device, + retina_masks=retina_masks, + imgsz=imgsz, + conf=conf, + iou=iou, + ) + + # Prepare a Prompt Process object + prompt_process = FastSAMPrompt(image, everything_results, device="cpu") + + # Everything prompt + annotations = prompt_process.everything_prompt() + + # TODO: support other modes: + + # Bbox default shape [0,0,0,0] -> [x1,y1,x2,y2] + # annotations = prompt_process.box_prompt(bbox=[200, 200, 300, 300]) + + # Text prompt + # annotations = prompt_process.text_prompt(text='a photo of a dog') + + # Point prompt + # points default [[0,0]] [[x1,y1],[x2,y2]] + # point_label default [0] [1,0] 0:background, 1:foreground + # annotations = prompt_process.point_prompt(points=[[200, 200]], pointlabel=[1]) + + assert len(annotations) == 1, len(annotations) + annotation = annotations[0] + + # hide original image + annotation.orig_img = np.ones(annotation.orig_img.shape) + + # TODO: in memory, e.g. with prompt_process.fast_show_mask() + with TemporaryDirectory() as tmp_dir: + # Force the output format to PNG to prevent JPEG compression artefacts + annotation.path = annotation.path.replace(".jpg", ".png") + prompt_process.plot( + [annotation], + tmp_dir, + with_contours=False, + retina=False, + ) + result_name = os.path.basename(annotation.path) + logger.info(f"{annotation.path=}") + image_path = Path(tmp_dir) / result_name + image = Image.open(image_path) + os.remove(image_path) + return image + + +def fetch_segmented_image_from_path(image_path: str) -> None: + """Gets a segmented image and displays it. + + Args: + image_path: path to image to segment + """ + with Image.open(image_path) as image: + segmented_image = fetch_segmented_image(image) + segmented_image.show() + + +if __name__ == "__main__": + fire.Fire(fetch_segmented_image_from_path) diff --git a/openadapt/cache.py b/openadapt/cache.py index 91c64ebd0..d0b94626c 100644 --- a/openadapt/cache.py +++ b/openadapt/cache.py @@ -70,6 +70,22 @@ def wrapper(*args: Any, **kwargs: Any) -> Any: rval = fn(*args, **kwargs) duration = time.time() - start_time logger.debug(f"{fn=} {duration=}") + + # workaround for `AttributeError: _min_frame` raised when show()ing an Image + try: + from PIL import Image + except ImportError: + pass + else: + if isinstance(rval, Image.Image): + if not hasattr(rval, "_min_frame"): + logger.debug( + "Image object missing '_min_frame'; refreshing image." + ) + fresh_image = Image.new(rval.mode, rval.size) + fresh_image.paste(rval) + rval = fresh_image + return rval return wrapper diff --git a/openadapt/common.py b/openadapt/common.py index 040f07bd0..200403d9f 100644 --- a/openadapt/common.py +++ b/openadapt/common.py @@ -1,13 +1,4 @@ -"""openadapt.common module. - -This module defines common constants and variables used in OpenAdapt. - -Example usage: - from openadapt.common import ALL_EVENTS - - for event in ALL_EVENTS: - print(event) -""" +"""This module defines common constants and variables used in OpenAdapt.""" MOUSE_EVENTS = ( # raw diff --git a/openadapt/config.py b/openadapt/config.py index 5b0268e85..d3272cbed 100644 --- a/openadapt/config.py +++ b/openadapt/config.py @@ -20,6 +20,16 @@ import sentry_sdk _DEFAULTS = { + "GOOGLE_API_KEY": "", + "SOM_SERVER_URL": "", + "REPLICATE_API_TOKEN": "", + "DEFAULT_ADAPTER": ( + "openai", + "anthropic", + "google", + )[0], + "DEFAULT_SEGMENTATION_ADAPTER": ("som", "replicate", "ultralytics")[-1], + "ANTHROPIC_API_KEY": "", "CACHE_DIR_PATH": ".cache", "CACHE_ENABLED": True, "CACHE_VERBOSITY": 0, diff --git a/openadapt/events.py b/openadapt/events.py index 2f451afa7..19caaa065 100644 --- a/openadapt/events.py +++ b/openadapt/events.py @@ -1,8 +1,5 @@ """This module provides functionality for aggregating events.""" -# TODO: rename this file to folds.py as per -# https://drive.google.com/file/d/1_fYoFncuI0TKghKWiMvP13hHEnIqAHI_/view?usp=drive_link - from pprint import pformat from typing import Any, Callable, Optional import time @@ -15,9 +12,11 @@ from openadapt.db import crud MAX_PROCESS_ITERS = 1 -MOUSE_MOVE_EVENT_MERGE_DISTANCE_THRESHOLD = 1 +MOUSE_MOVE_EVENT_MERGE_CLICK_DISTANCE_THRESHOLD = 5 +MOUSE_MOVE_EVENT_MERGE_DIFF_DISTANCE_THRESHOLD = 1 MOUSE_MOVE_EVENT_MERGE_MIN_IDX_DELTA = 5 KEYBOARD_EVENTS_MERGE_GROUP_NAMED_KEYS = True +USE_SCREENSHOT_DIFFS = False def get_events( @@ -143,11 +142,13 @@ def make_parent_event( extra = extra or {} for key, val in extra.items(): event_dict[key] = val - return models.ActionEvent(**event_dict) + action_event = models.ActionEvent(**event_dict) + return action_event def merge_consecutive_mouse_move_events( - events: list[models.ActionEvent], by_diff_distance: bool = False + events: list[models.ActionEvent], + by_diff_distance: bool = USE_SCREENSHOT_DIFFS, ) -> list[models.ActionEvent]: """Merge consecutive mouse move events into a single move event. @@ -169,7 +170,7 @@ def is_target_event(event: models.ActionEvent, state: dict[str, Any]) -> bool: def get_merged_events( to_merge: list[models.ActionEvent], state: dict[str, Any], - distance_threshold: int = MOUSE_MOVE_EVENT_MERGE_DISTANCE_THRESHOLD, + distance_threshold: int = MOUSE_MOVE_EVENT_MERGE_DIFF_DISTANCE_THRESHOLD, # Minimum number of consecutive events (in which the distance between # the cursor and the nearest non-zero diff pixel is greater than # distance_threshold) in order to result in a separate parent event. @@ -622,6 +623,84 @@ def get_merged_events( ) +# TODO: consolidate with remove_redundant_mouse_move_events above +# these are very similar except for the lines noted below +def remove_move_before_click( + events: list[models.ActionEvent], +) -> list[models.ActionEvent]: + """Remove mouse move move immediately followed by click in the same location.""" + + def is_target_event(event: models.ActionEvent, state: dict[str, Any]) -> bool: + return event.name in ("move", "click", "singleclick", "doubleclick") + + def is_same_pos(e0: models.ActionEvent, e1: models.ActionEvent) -> bool: + if not all([e0, e1]): + return False + for attr in ("mouse_x", "mouse_y"): + val0 = getattr(e0, attr) + val1 = getattr(e1, attr) + # this line is distinct from remove_redundant_mouse_move_events + if abs(val0 - val1) > MOUSE_MOVE_EVENT_MERGE_CLICK_DISTANCE_THRESHOLD: + return False + return True + + def should_discard( + event: models.ActionEvent, + prev_event: models.ActionEvent | None, + next_event: models.ActionEvent | None, + ) -> bool: + # import ipdb; ipdb.set_trace() + return event.name == "move" and ( + # this line is distinct from remove_redundant_mouse_move_events + is_same_pos(event, next_event) + ) + + def get_merged_events( + to_merge: list[models.ActionEvent], state: dict[str, Any] + ) -> list[models.ActionEvent]: + to_merge = [None, *to_merge, None] + merged_events = [] + dts = [] + children = [] + for idx, (prev_event, event, next_event) in enumerate( + zip( + to_merge, + to_merge[1:], + to_merge[2:], + ) + ): + if should_discard(event, prev_event, next_event): + if prev_event: + dt = event.timestamp - prev_event.timestamp + else: + dt = next_event.timestamp - event.timestamp + state["dt"] += dt + children.append(event) + else: + dts.append(state["dt"]) + if children: + event.children = children + children = [] + merged_events.append(event) + + # update timestamps (doing this in the previous loop double counts) + assert len(dts) == len(merged_events), ( + len(dts), + len(merged_events), + ) + for event, dt in zip(merged_events, dts): + event.timestamp -= dt + + return merged_events + + return merge_consecutive_action_events( + "redundant_mouse_move", + events, + is_target_event, + get_merged_events, + ) + + def merge_consecutive_action_events( name: str, events: list[models.ActionEvent], @@ -730,11 +809,12 @@ def process_events( ) process_fns = [ remove_invalid_keyboard_events, + remove_redundant_mouse_move_events, merge_consecutive_keyboard_events, merge_consecutive_mouse_move_events, merge_consecutive_mouse_scroll_events, - remove_redundant_mouse_move_events, merge_consecutive_mouse_click_events, + remove_move_before_click, ] for process_fn in process_fns: action_events = process_fn(action_events) diff --git a/openadapt/models.py b/openadapt/models.py index e893d4364..54f26a7e7 100644 --- a/openadapt/models.py +++ b/openadapt/models.py @@ -1,11 +1,12 @@ """This module defines the models used in the OpenAdapt system.""" -from typing import Union +from typing import Type import io from loguru import logger from oa_pynput import keyboard from PIL import Image, ImageChops +import mss.base import numpy as np import sqlalchemy as sa @@ -79,6 +80,8 @@ class ActionEvent(db.Base): __tablename__ = "action_event" + _segment_description_separator = ";" + id = sa.Column(sa.Integer, primary_key=True) name = sa.Column(sa.String) timestamp = sa.Column(ForceFloat) @@ -89,6 +92,11 @@ class ActionEvent(db.Base): mouse_y = sa.Column(sa.Numeric(asdecimal=False)) mouse_dx = sa.Column(sa.Numeric(asdecimal=False)) mouse_dy = sa.Column(sa.Numeric(asdecimal=False)) + active_segment_description = sa.Column(sa.String) + _available_segment_descriptions = sa.Column( + "available_segment_descriptions", + sa.String, + ) mouse_button_name = sa.Column(sa.String) mouse_pressed = sa.Column(sa.Boolean) key_name = sa.Column(sa.String) @@ -100,6 +108,23 @@ class ActionEvent(db.Base): parent_id = sa.Column(sa.Integer, sa.ForeignKey("action_event.id")) element_state = sa.Column(sa.JSON) + @property + def available_segment_descriptions(self) -> list[str]: + """Gets the available segment descriptions.""" + if self._available_segment_descriptions: + return self._available_segment_descriptions.split( + self._segment_description_separator + ) + else: + return [] + + @available_segment_descriptions.setter + def available_segment_descriptions(self, value: list[str]) -> None: + """Sets the available segment descriptions.""" + self._available_segment_descriptions = self._segment_description_separator.join( + value + ) + children = sa.orm.relationship("ActionEvent") # TODO: replacing the above line with the following two results in an error: # AttributeError: 'list' object has no attribute '_sa_instance_state' @@ -118,7 +143,7 @@ class ActionEvent(db.Base): def _key( self, key_name: str, key_char: str, key_vk: str - ) -> Union[keyboard.Key, keyboard.KeyCode, str, None]: + ) -> keyboard.Key | keyboard.KeyCode | str | None: """Helper method to determine the key attribute based on available data.""" if key_name: key = keyboard.Key[key_name] @@ -132,7 +157,7 @@ def _key( return key @property - def key(self) -> Union[keyboard.Key, keyboard.KeyCode, str, None]: + def key(self) -> keyboard.Key | keyboard.KeyCode | str | None: """Get the key associated with the action event.""" logger.trace(f"{self.name=} {self.key_name=} {self.key_char=} {self.key_vk=}") return self._key( @@ -142,7 +167,7 @@ def key(self) -> Union[keyboard.Key, keyboard.KeyCode, str, None]: ) @property - def canonical_key(self) -> Union[keyboard.Key, keyboard.KeyCode, str, None]: + def canonical_key(self) -> keyboard.Key | keyboard.KeyCode | str | None: """Get the canonical key associated with the action event.""" logger.trace( f"{self.name=} " @@ -223,7 +248,7 @@ def __str__(self) -> str: return rval @classmethod - def from_children(cls: list, children_dicts: list) -> "ActionEvent": + def from_children(cls: Type["ActionEvent"], children_dicts: list) -> "ActionEvent": """Create an ActionEvent instance from a list of child event dictionaries. Args: @@ -231,11 +256,106 @@ def from_children(cls: list, children_dicts: list) -> "ActionEvent": Returns: ActionEvent: An instance of ActionEvent with the specified child events. - """ children = [ActionEvent(**child_dict) for child_dict in children_dicts] return ActionEvent(children=children) + @classmethod + def from_dict( + cls: Type["ActionEvent"], + action_dict: dict, + ) -> "ActionEvent": + """Get an ActionEvent from a dict.""" + sep = config.ACTION_TEXT_SEP + name_prefix = config.ACTION_TEXT_NAME_PREFIX + name_suffix = config.ACTION_TEXT_NAME_SUFFIX + children = [] + release_events = [] + if "text" in action_dict: + # Splitting actions based on whether they are special keys or characters + if action_dict["text"].startswith(name_prefix) and action_dict[ + "text" + ].endswith(name_suffix): + # Handling special keys + sep = "".join([name_suffix, sep, name_prefix]) + prefix_len = len(name_prefix) + suffix_len = len(name_suffix) + key_names = action_dict["text"][prefix_len:-suffix_len].split(sep) + canonical_key_names = action_dict["canonical_text"][ + prefix_len:-suffix_len + ].split(sep) + for key_name, canonical_key_name in zip(key_names, canonical_key_names): + press, release = cls._create_key_events( + key_name, canonical_key_name + ) + children.append(press) + release_events.append( + release + ) # Collect release events to append in reverse order later + else: + # Handling regular character sequences + sep_len = len(sep) + for key_char in action_dict["text"][:: sep_len + 1]: + # Press and release each character one after another + press, release = cls._create_key_events(key_char=key_char) + children.append(press) + children.append(release) + children += release_events[::-1] + return ActionEvent(children=children) + else: + return ActionEvent(**action_dict) + + @classmethod + def _create_key_events( + cls: Type["ActionEvent"], + key_name: str | None = None, + canonical_key_name: str | None = None, + key_char: str | None = None, + canonical_key_char: str | None = None, + ) -> list["ActionEvent"]: + # This helper function creates press and release events for a given key_name + # TODO: remove canonical? + press_event = cls( + name="press", + key_name=key_name, + # canonical_key_name=canonical_key_name, + key_char=key_char, + # canonical_key_char=canonical_key_char, + ) + release_event = cls( + name="release", + key_name=key_name, + # canonical_key_name=canonical_key_name, + key_char=key_char, + # canonical_key_char=canonical_key_char, + ) + return press_event, release_event + + +class WindowEvent(db.Base): + """Class representing a window event in the database.""" + + __tablename__ = "window_event" + + id = sa.Column(sa.Integer, primary_key=True) + recording_timestamp = sa.Column(sa.ForeignKey("recording.timestamp")) + timestamp = sa.Column(ForceFloat) + state = sa.Column(sa.JSON) + title = sa.Column(sa.String) + left = sa.Column(sa.Integer) + top = sa.Column(sa.Integer) + width = sa.Column(sa.Integer) + height = sa.Column(sa.Integer) + window_id = sa.Column(sa.String) + + recording = sa.orm.relationship("Recording", back_populates="window_events") + action_events = sa.orm.relationship("ActionEvent", back_populates="window_event") + + @classmethod + def get_active_window_event(cls: "WindowEvent") -> "WindowEvent": + """Get the active window event.""" + return WindowEvent(**window.get_active_window_data()) + class Screenshot(db.Base): """Class representing a screenshot in the database.""" @@ -252,14 +372,30 @@ class Screenshot(db.Base): recording = sa.orm.relationship("Recording", back_populates="screenshots") action_event = sa.orm.relationship("ActionEvent", back_populates="screenshot") - # TODO: convert to png_data on save - sct_img = None - - # TODO: replace prev with prev_timestamp? - prev = None - _image = None - _diff = None - _diff_mask = None + def __init__( + self, + *args: tuple, + sct_img: mss.base.ScreenShot | None = None, + **kwargs: dict, + ) -> None: + """Initialize.""" + super().__init__(*args, **kwargs) + self.initialize_instance_attributes() + self.sct_img = sct_img + + @sa.orm.reconstructor + def initialize_instance_attributes(self) -> None: + """Initialize attributes for both new and loaded objects.""" + # TODO: convert to png_data on save + self.sct_img = None + + # TODO: replace prev with prev_timestamp? + self.prev = None + self._image = None + self._image_history = [] + self._diff = None + self._diff_mask = None + self._base64 = None @property def image(self) -> Image: @@ -277,6 +413,15 @@ def image(self) -> Image: self._image = self.convert_binary_to_png(self.png_data) return self._image + @property + def base64(self) -> str: + """Return data URI of JPEG encoded base64.""" + if not self._base64: + from openadapt import utils + + self._base64 = utils.image2utf8(self.image) + return self._base64 + @property def diff(self) -> Image: """Get the difference between the current and previous screenshot.""" @@ -326,8 +471,16 @@ def crop_active_window(self, action_event: ActionEvent) -> None: y1 = y0 + window_event.height * height_ratio box = (x0, y0, x1, y1) + self._image_history.append(self.image) self._image = self._image.crop(box) + @property + def original_image(self) -> Image: + """Get the original image (before any cropping).""" + if self._image_history: + return self._image_history[0] + return self.image + def convert_binary_to_png(self, image_binary: bytes) -> Image: """Convert a binary image to a PNG image. @@ -354,31 +507,6 @@ def convert_png_to_binary(self, image: Image) -> bytes: return buffer.getvalue() -class WindowEvent(db.Base): - """Class representing a window event in the database.""" - - __tablename__ = "window_event" - - id = sa.Column(sa.Integer, primary_key=True) - recording_timestamp = sa.Column(sa.ForeignKey("recording.timestamp")) - timestamp = sa.Column(ForceFloat) - state = sa.Column(sa.JSON) - title = sa.Column(sa.String) - left = sa.Column(sa.Integer) - top = sa.Column(sa.Integer) - width = sa.Column(sa.Integer) - height = sa.Column(sa.Integer) - window_id = sa.Column(sa.String) - - recording = sa.orm.relationship("Recording", back_populates="window_events") - action_events = sa.orm.relationship("ActionEvent", back_populates="window_event") - - @classmethod - def get_active_window_event(cls: "WindowEvent") -> "WindowEvent": - """Get the active window event.""" - return WindowEvent(**window.get_active_window_data()) - - class PerformanceStat(db.Base): """Class representing a performance statistic in the database.""" diff --git a/openadapt/playback.py b/openadapt/playback.py index 36a0b6450..6b651ad63 100644 --- a/openadapt/playback.py +++ b/openadapt/playback.py @@ -91,11 +91,23 @@ def play_action_event( Exception: If the event name is not handled. """ # currently we use children to replay type events - if event.children and event.name in KEY_EVENTS: + if event.children: # and event.name in KEY_EVENTS: + # if event.children and event.name != "type": # TODO: hack, remove for child in event.children: play_action_event(child, mouse_controller, keyboard_controller) else: - assert event.name in MOUSE_EVENTS + KEY_EVENTS, event + expected_event_names = MOUSE_EVENTS + KEY_EVENTS + try: + assert event.name in expected_event_names, ( + event.name, + expected_event_names, + ) + except AssertionError as exc: + logger.error(exc) + import ipdb + + ipdb.set_trace() + raise if event.name in MOUSE_EVENTS: play_mouse_event(event, mouse_controller) elif event.name in KEY_EVENTS: diff --git a/openadapt/prompts/apply_replay_instructions.j2 b/openadapt/prompts/apply_replay_instructions.j2 new file mode 100644 index 000000000..f1db052f7 --- /dev/null +++ b/openadapt/prompts/apply_replay_instructions.j2 @@ -0,0 +1,19 @@ +Consider the actions in the recording: + +```json +{{ actions }} +``` + +Consider the user's replay instructions: +```text +{{ replay_instructions }} +``` + +Provide an updated list of actions that are modified such that replaying them will +accomplish the user's replay instructions. + +Do NOT provide available_segment_descriptions in your response. + +Respond with json and nothing else. + +My career depends on this. Lives are at stake. diff --git a/openadapt/prompts/description.j2 b/openadapt/prompts/description.j2 new file mode 100644 index 000000000..3d3438d71 --- /dev/null +++ b/openadapt/prompts/description.j2 @@ -0,0 +1,35 @@ +You are a master GUI understander. I have attached a screenshot of a GUI, and a several +segments of the screenshot. Please describe what GUI element is in each segment, +in the order they are given. +You MUST provide exactly one description per segment. There are {{ num_segments }}, +therefore you must provide {{ num_segments }} descriptions. Include a monotonically +increasing integer so you don't lose track. +Respond in JSON, i.e.: +```json +{ + "descriptions": [ + [1, "..."], + [2, "..."], + ... + ] +} +``` +{% if active_segment_description %} +In particular, we are interested in a segment with the following description +(i.e. the "active segment description"): +```text +{{ active_segment_description }} +``` +If any of the segments EXACTLY match this description, you MUST re-use this +description *VERBATIM*, i.e. without any modifications. +{% endif %} +{% if exceptions %} +Previously when you attempted this, the results generated these exceptions: +{% for exception in exceptions %} +{{ exception }} +{% endfor %} +YOU MUST RE-USE THE PROVIDED ACTIVE SEGMENT DESCRIPTION: +```text +{{ active_segment_description }} +``` +{% endif %} diff --git a/openadapt/prompts/system.j2 b/openadapt/prompts/system.j2 new file mode 100644 index 000000000..ea298f92d --- /dev/null +++ b/openadapt/prompts/system.j2 @@ -0,0 +1,4 @@ +You are the cognitive engine powering OpenAdapt, the world's first and most advanced open-source AI-first process automation system. +Your output directly manipulates a real keyboard/mouse controller on a live system. +Your task is to accurately interpet previously recorded user actions (keyboard and mouse events) and associated screenshots and window states. +Your responses will be parsed by a deterministic software system, and not read by a human (unless it is for debugging purposes). diff --git a/openadapt/record.py b/openadapt/record.py index 42de60b40..01a304e7e 100644 --- a/openadapt/record.py +++ b/openadapt/record.py @@ -169,6 +169,7 @@ def process_events( perf_q: sq.SynchronizedQueue, recording_timestamp: float, terminate_event: multiprocessing.Event, + started_counter: multiprocessing.Value, ) -> None: """Process events from the event queue and write them to write queues. @@ -181,6 +182,7 @@ def process_events( perf_q: A queue for collecting performance data. recording_timestamp: The timestamp of the recording. terminate_event: An event to signal the termination of the process. + started_counter: Value to increment once started. """ utils.set_start_time(recording_timestamp) @@ -192,8 +194,13 @@ def process_events( prev_window_event = None prev_saved_screen_timestamp = 0 prev_saved_window_timestamp = 0 + started = False while not terminate_event.is_set() or not event_q.empty(): event = event_q.get() + if not started: + with started_counter.get_lock(): + started_counter.value += 1 + started = True logger.trace(f"{event=}") assert event.type in EVENT_TYPES, event if prev_event is not None: @@ -320,6 +327,7 @@ def write_events( recording_timestamp: float, terminate_event: multiprocessing.Event, term_pipe: multiprocessing.Pipe, + started_counter: multiprocessing.Value, ) -> None: """Write events of a specific type to the db using the provided write function. @@ -330,8 +338,8 @@ def write_events( perf_q: A queue for collecting performance data. recording_timestamp: The timestamp of the recording. terminate_event: An event to signal the termination of the process. - term_pipe: A pipe for communicating \ - the number of events left to be written. + term_pipe: A pipe for communicating the number of events left to be written. + started_counter: Value to increment once started. """ utils.set_start_time(recording_timestamp) @@ -340,6 +348,7 @@ def write_events( num_left = 0 progress = None + started = False while not terminate_event.is_set() or not write_q.empty(): if term_pipe.poll(): num_left = term_pipe.recv() @@ -353,6 +362,10 @@ def write_events( ) if terminate_event.is_set() and num_left != 0 and progress is not None: progress.update() + if not started: + with started_counter.get_lock(): + started_counter.value += 1 + started = True try: event = write_q.get_nowait() except queue.Empty: @@ -372,6 +385,7 @@ def write_video( video_write_q: sq.SynchronizedQueue, recording_timestamp: float, terminate_event: multiprocessing.Event, + started_counter: multiprocessing.Value, ) -> None: """Writes video frames from a synchronized queue to a video file until termination. @@ -395,6 +409,7 @@ def write_video( video writing should be terminated. The function will continue to process events in the queue until it is empty, even after the terminate event is set. + started_counter: Value to increment once started. Returns: None @@ -415,7 +430,12 @@ def write_video( crud.update_video_start_time(recording_timestamp, video_start_time) last_pts = 0 + started = False while not terminate_event.is_set() or not video_write_q.empty(): + if not started: + with started_counter.get_lock(): + started_counter.value += 1 + started = True try: event = video_write_q.get_nowait() except queue.Empty: @@ -584,6 +604,7 @@ def read_screen_events( event_q: queue.Queue, terminate_event: multiprocessing.Event, recording_timestamp: float, + started_counter: multiprocessing.Value, # TODO: throttle # max_cpu_percent: float = 50.0, # Maximum allowed CPU percent # max_memory_percent: float = 50.0, # Maximum allowed memory percent @@ -595,15 +616,21 @@ def read_screen_events( event_q: A queue for adding screen events. terminate_event: An event to signal the termination of the process. recording_timestamp: The timestamp of the recording. + started_counter: Value to increment once started. """ utils.set_start_time(recording_timestamp) logger.info("Starting") + started = False while not terminate_event.is_set(): screenshot = utils.take_screenshot() if screenshot is None: logger.warning("Screenshot was None") continue + if not started: + with started_counter.get_lock(): + started_counter.value += 1 + started = True event_q.put(Event(utils.get_timestamp(), "screen", screenshot)) logger.info("Done") @@ -613,6 +640,7 @@ def read_window_events( event_q: queue.Queue, terminate_event: multiprocessing.Event, recording_timestamp: float, + started_counter: multiprocessing.Value, ) -> None: """Read window events and add them to the event queue. @@ -620,16 +648,23 @@ def read_window_events( event_q: A queue for adding window events. terminate_event: An event to signal the termination of the process. recording_timestamp: The timestamp of the recording. + started_counter: Value to increment once started. """ utils.set_start_time(recording_timestamp) logger.info("Starting") prev_window_data = {} + started = False while not terminate_event.is_set(): window_data = window.get_active_window_data() if not window_data: continue + if not started: + with started_counter.get_lock(): + started_counter.value += 1 + started = True + if window_data["title"] != prev_window_data.get("title") or window_data[ "window_id" ] != prev_window_data.get("window_id"): @@ -660,6 +695,7 @@ def performance_stats_writer( perf_q: sq.SynchronizedQueue, recording_timestamp: float, terminate_event: multiprocessing.Event, + started_counter: multiprocessing.Value, ) -> None: """Write performance stats to the database. @@ -669,12 +705,18 @@ def performance_stats_writer( perf_q: A queue for collecting performance data. recording_timestamp: The timestamp of the recording. terminate_event: An event to signal the termination of the process. + started_counter: Value to increment once started. """ utils.set_start_time(recording_timestamp) logger.info("Performance stats writer starting") signal.signal(signal.SIGINT, signal.SIG_IGN) + started = False while not terminate_event.is_set() or not perf_q.empty(): + if not started: + with started_counter.get_lock(): + started_counter.value += 1 + started = True try: event_type, start_time, end_time = perf_q.get_nowait() except queue.Empty: @@ -693,6 +735,7 @@ def memory_writer( recording_timestamp: float, terminate_event: multiprocessing.Event, record_pid: int, + started_counter: multiprocessing.Value, ) -> None: """Writes memory usage statistics to the database. @@ -701,6 +744,7 @@ def memory_writer( terminate_event (multiprocessing.Event): The event used to terminate the process. record_pid (int): The process ID to monitor memory usage for. + started_counter: Value to increment once started. Returns: None @@ -711,7 +755,12 @@ def memory_writer( signal.signal(signal.SIGINT, signal.SIG_IGN) process = psutil.Process(record_pid) + started = False while not terminate_event.is_set(): + if not started: + with started_counter.get_lock(): + started_counter.value += 1 + started = True memory_usage_bytes = 0 memory_info = process.memory_info() @@ -772,6 +821,7 @@ def read_keyboard_events( event_q: queue.Queue, terminate_event: multiprocessing.Event, recording_timestamp: float, + started_counter: multiprocessing.Value, ) -> None: """Reads keyboard events and adds them to the event queue. @@ -780,6 +830,7 @@ def read_keyboard_events( terminate_event (multiprocessing.Event): The event to signal termination of event reading. recording_timestamp (float): The timestamp of the recording. + started_counter: Value to increment once started. Returns: None @@ -867,6 +918,12 @@ def on_release( on_release=partial(on_release, event_q), ) keyboard_listener.start() + + # NOTE: listener may not have actually started by now + # TODO: handle race condition, e.g. by sending synthetic events from main thread + with started_counter.get_lock(): + started_counter.value += 1 + terminate_event.wait() keyboard_listener.stop() @@ -875,6 +932,7 @@ def read_mouse_events( event_q: queue.Queue, terminate_event: multiprocessing.Event, recording_timestamp: float, + started_counter: multiprocessing.Value, ) -> None: """Reads mouse events and adds them to the event queue. @@ -882,6 +940,7 @@ def read_mouse_events( event_q: The event queue to add the mouse events to. terminate_event: The event to signal termination of event reading. recording_timestamp: The timestamp of the recording. + started_counter: Value to increment once started. Returns: None @@ -894,6 +953,12 @@ def read_mouse_events( on_scroll=partial(on_scroll, event_q), ) mouse_listener.start() + + # NOTE: listener may not have actually started by now + # TODO: handle race condition, e.g. by sending synthetic events from main thread + with started_counter.get_lock(): + started_counter.value += 1 + terminate_event.wait() mouse_listener.stop() @@ -926,6 +991,8 @@ def record( # TODO: save write times to DB; display performance plot in visualize.py perf_q = sq.SynchronizedQueue() terminate_event = multiprocessing.Event() + started_counter = multiprocessing.Value("i", 0) + expected_starts = 9 ( term_pipe_parent_window, @@ -942,25 +1009,25 @@ def record( window_event_reader = threading.Thread( target=read_window_events, - args=(event_q, terminate_event, recording_timestamp), + args=(event_q, terminate_event, recording_timestamp, started_counter), ) window_event_reader.start() screen_event_reader = threading.Thread( target=read_screen_events, - args=(event_q, terminate_event, recording_timestamp), + args=(event_q, terminate_event, recording_timestamp, started_counter), ) screen_event_reader.start() keyboard_event_reader = threading.Thread( target=read_keyboard_events, - args=(event_q, terminate_event, recording_timestamp), + args=(event_q, terminate_event, recording_timestamp, started_counter), ) keyboard_event_reader.start() mouse_event_reader = threading.Thread( target=read_mouse_events, - args=(event_q, terminate_event, recording_timestamp), + args=(event_q, terminate_event, recording_timestamp, started_counter), ) mouse_event_reader.start() @@ -975,6 +1042,7 @@ def record( perf_q, recording_timestamp, terminate_event, + started_counter, ), ) event_processor.start() @@ -989,6 +1057,7 @@ def record( recording_timestamp, terminate_event, term_pipe_child_screen, + started_counter, ), ) screen_event_writer.start() @@ -1003,6 +1072,7 @@ def record( recording_timestamp, terminate_event, term_pipe_child_action, + started_counter, ), ) action_event_writer.start() @@ -1017,17 +1087,20 @@ def record( recording_timestamp, terminate_event, term_pipe_child_window, + started_counter, ), ) window_event_writer.start() if config.RECORD_VIDEO: + expected_starts += 1 video_writer = multiprocessing.Process( target=write_video, args=( video_write_q, recording_timestamp, terminate_event, + started_counter, ), ) video_writer.start() @@ -1039,11 +1112,13 @@ def record( perf_q, recording_timestamp, terminate_perf_event, + started_counter, ), ) perf_stat_writer.start() if PLOT_PERFORMANCE: + expected_starts += 1 record_pid = os.getpid() mem_plotter = multiprocessing.Process( target=memory_writer, @@ -1051,12 +1126,22 @@ def record( recording_timestamp, terminate_perf_event, record_pid, + started_counter, ), ) mem_plotter.start() # TODO: discard events until everything is ready + # Wait for all to signal they've started + while True: + if started_counter.value >= expected_starts: + break + time.sleep(0.1) # Sleep to reduce busy waiting + for _ in range(5): + logger.info("*" * 40) + logger.info("All readers and writers have started. Waiting for input events...") + collect_stats() global stop_sequence_detected diff --git a/openadapt/replay.py b/openadapt/replay.py index 33805d226..711cfd9dd 100644 --- a/openadapt/replay.py +++ b/openadapt/replay.py @@ -1,15 +1,9 @@ """Replay recorded events. Usage: -python openadapt/replay.py [--timestamp=] - -Arguments: -strategy_name Name of the replay strategy to use. - -Options: ---timestamp= Timestamp of the recording to replay. - + python -m openadapt.replay.py [--timestamp=] """ + from time import sleep from typing import Union import os @@ -30,6 +24,7 @@ def replay( record: bool = False, timestamp: Union[str, None] = None, recording: Recording = None, + instructions: str | None = None, ) -> bool: """Replay recorded events. @@ -38,6 +33,9 @@ def replay( timestamp (str, optional): Timestamp of the recording to replay. recording (Recording, optional): Recording to replay. record (bool, optional): Flag indicating whether to record the replay. + instructions (str, optional): Natural language instructions to the + model, e.g. description of what are the parameters in the recording, and + how the replay should behave as a function of those parameters. Returns: bool: True if replay was successful, None otherwise. @@ -67,7 +65,7 @@ def replay( strategy_class = strategy_class_by_name[strategy_name] logger.info(f"{strategy_class=}") - strategy = strategy_class(recording) + strategy = strategy_class(recording, instructions) logger.info(f"{strategy=}") handler = None diff --git a/openadapt/strategies/__init__.py b/openadapt/strategies/__init__.py index c139efa48..47864d2f2 100644 --- a/openadapt/strategies/__init__.py +++ b/openadapt/strategies/__init__.py @@ -7,5 +7,6 @@ from openadapt.strategies.base import BaseReplayStrategy from openadapt.strategies.demo import DemoReplayStrategy from openadapt.strategies.naive import NaiveReplayStrategy +from openadapt.strategies.visual import VisualReplayStrategy # add more strategies here diff --git a/openadapt/strategies/base.py b/openadapt/strategies/base.py index 9a9c7f4e2..a64e3d905 100644 --- a/openadapt/strategies/base.py +++ b/openadapt/strategies/base.py @@ -1,7 +1,4 @@ -"""Implements the base class for implementing replay strategies. - -Module: base.py -""" +"""Implements the base class for implementing replay strategies.""" from abc import ABC, abstractmethod from pprint import pformat @@ -22,12 +19,15 @@ class BaseReplayStrategy(ABC): def __init__( self, recording: models.Recording, + replay_instructions: str | None = None, max_frame_times: int = MAX_FRAME_TIMES, ) -> None: """Initialize the BaseReplayStrategy. Args: recording (models.Recording): The recording to replay. + replay_instructions (str, optional): Natural language instructions + for how recording should be replayed. max_frame_times (int): The maximum number of frame times to track. """ self.recording = recording @@ -70,18 +70,28 @@ def run(self) -> None: break if self.action_events: prev_action_event = self.action_events[-1] - assert prev_action_event.timestamp <= action_event.timestamp, ( - prev_action_event, - action_event, - ) + if prev_action_event.timestamp and action_event.timestamp: + assert prev_action_event.timestamp <= action_event.timestamp, ( + prev_action_event, + action_event, + ) + else: + logger.warning( + f"{prev_action_event.timestamp=} {action_event.timestamp=}" + ) self.log_fps() if action_event: - self.action_events.append(action_event) action_event_dict = utils.rows2dicts( [action_event], drop_constant=False, )[0] logger.info(f"action_event=\n{pformat(action_event_dict)}") + if not action_event_dict: + action_event = prev_action_event + import ipdb + + ipdb.set_trace() + self.action_events.append(action_event) try: playback.play_action_event( action_event, diff --git a/openadapt/strategies/demo.py b/openadapt/strategies/demo.py index 1ebe787fa..dc038f8fc 100644 --- a/openadapt/strategies/demo.py +++ b/openadapt/strategies/demo.py @@ -33,13 +33,16 @@ class DemoReplayStrategy( def __init__( self, recording: Recording, + replay_instructions: str | None = None, ) -> None: """Initialize the DemoReplayStrategy. Args: recording (Recording): The recording to replay. + replay_instructions (str): Natural language instructions + for how recording should be replayed. """ - super().__init__(recording) + super().__init__(recording, replay_instructions) self.result_history = [] self.screenshots = get_screenshots(recording) self.screenshot_idx = 0 diff --git a/openadapt/strategies/naive.py b/openadapt/strategies/naive.py index 9fb94d305..021ad492e 100644 --- a/openadapt/strategies/naive.py +++ b/openadapt/strategies/naive.py @@ -19,6 +19,7 @@ class NaiveReplayStrategy(strategies.base.BaseReplayStrategy): def __init__( self, recording: models.Recording, + replay_instructions: str | None = None, display_events: bool = DISPLAY_EVENTS, replay_events: bool = REPLAY_EVENTS, process_events: bool = PROCESS_EVENTS, @@ -28,6 +29,8 @@ def __init__( Args: recording (models.Recording): The recording object. + replay_instructions (str): Natural language instructions + for how recording should be replayed. display_events (bool): Flag indicating whether to display the events. Defaults to False. replay_events (bool): Flag indicating whether to replay the events. diff --git a/openadapt/strategies/stateful.py b/openadapt/strategies/stateful.py index 7ad085af7..e714a9648 100644 --- a/openadapt/strategies/stateful.py +++ b/openadapt/strategies/stateful.py @@ -29,13 +29,16 @@ class StatefulReplayStrategy( def __init__( self, recording: models.Recording, + replay_instructions: str | None = None, ) -> None: """Initialize the StatefulReplayStrategy. Args: recording (models.Recording): The recording object. + replay_instructions (str): Natural language instructions + for how recording should be replayed. """ - super().__init__(recording) + super().__init__(recording, replay_instructions) self.recording_window_state_diffs = get_window_state_diffs( recording.processed_action_events ) @@ -146,30 +149,13 @@ def get_next_action_event( "Do not respond with any other text. " ) completion = self.get_completion(prompt, system_message) - active_action_dicts = get_action_dict_from_completion(completion) + active_action_dicts = utils.get_action_dict_from_completion(completion) logger.debug(f"active_action_dicts=\n{pformat(active_action_dicts)}") active_action = models.ActionEvent.from_children(active_action_dicts) self.recording_action_idx += 1 return active_action -def get_action_dict_from_completion(completion: str) -> dict[models.ActionEvent]: - """Convert the completion to a dictionary containing action information. - - Args: - completion (str): The completion provided by the user. - - Returns: - dict: The action dictionary. - """ - try: - action = eval(completion) - except Exception as exc: - logger.warning(f"{exc=}") - else: - return action - - def get_window_state_diffs( action_events: list[models.ActionEvent], ignore_boundary_windows: bool = IGNORE_BOUNDARY_WINDOWS, diff --git a/openadapt/strategies/visual.py b/openadapt/strategies/visual.py new file mode 100644 index 000000000..fb37674c8 --- /dev/null +++ b/openadapt/strategies/visual.py @@ -0,0 +1,486 @@ +"""Large Multimodal Model with replay instructions. + +1. Get active element descriptions + +For each processed event, if it is a mouse event: + - segment the event's active window + - get a natural language description of each element + +2. Modify actions according to replay instructions + +Get a natural language description of what the active element should be given the +replay instructions. + +3. Replay modified events + +For each modified event: + + a. Convert descriptions to coordinates + + If it is a click, scroll, or the last in a sequence of isolated events: + - segment the current active window + - determine the coordinates of the modified active element + + b. Replay modified event + +See prompts for details: +- openadapt/prompts/system.j2 +- openadapt/prompts/description.j2 +- openadapt/prompts/apply_replay_instructions.j2 + +Todo: +- handle tab sequences +- re-use previous segmentations / descriptions +- handle separate UI elements which look identical (e.g. spreadsheet cells) + - e.g. include segment mask in prompt + - e.g. annotate grid positions + + +Usage: + + $ python -m openadapt.replay VisualReplayStrategy --instructions "" +""" + +from copy import deepcopy +from dataclasses import dataclass +import time + +from loguru import logger +from PIL import Image, ImageDraw + +from openadapt import adapters, common, models, strategies, utils, vision + + +DEBUG = False +DEBUG_REPLAY = False + + +@dataclass +class Segmentation: + """A data class to encapsulate segmentation data of images. + + Attributes: + masked_images: A list of PIL Image objects that have been masked based on + segmentation. + descriptions: Descriptions of each segmented region, correlating with each + image in `masked_images`. + bounding_boxes: A list of dictionaries containing bounding box + coordinates for each segmented region. Each dictionary should have the + keys "top", "left", "height", and "width" with float values indicating + the position and size of the box. + centroids: A list of tuples, each containing the x and y coordinates of the + centroid of each segmented region. + """ + + masked_images: list[Image.Image] + descriptions: list[str] + bounding_boxes: list[dict[str, float]] # "top", "left", "height", "width" + centroids: list[tuple[float, float]] + + +def add_active_segment_descriptions(action_events: list[models.ActionEvent]) -> None: + """Set the ActionEvent.active_segment_description where appropriate. + + Args: + action_events: list of ActionEvents to modify in-place. + """ + for action in action_events: + # TODO: handle terminal event + # if action.name in ("click", "doubleclick", "singleclick", "scroll"): + if action.name in common.MOUSE_EVENTS: + window_segmentation = get_window_segmentation(action) + active_segment_idx = get_active_segment(action, window_segmentation) + if not active_segment_idx: + logger.warning(f"{active_segment_idx=}") + active_segment_description = "(None)" + else: + active_segment_description = window_segmentation.descriptions[ + active_segment_idx + ] + action.active_segment_description = active_segment_description + action.available_segment_descriptions = window_segmentation.descriptions + + +def apply_replay_instructions( + action_events: list[models.ActionEvent], + replay_instructions: str, +) -> None: + """Modify the given ActionEvents according to the given replay instructions. + + Args: + action_events: list of action events to be modified in place. + replay_instructions: instructions for how action events should be modified. + """ + action_dicts = [get_action_prompt_dict(action) for action in action_events] + actions_dict = {"actions": action_dicts} + system_prompt = utils.render_template_from_file( + "openadapt/prompts/system.j2", + ) + prompt = utils.render_template_from_file( + "openadapt/prompts/apply_replay_instructions.j2", + actions=actions_dict, + replay_instructions=replay_instructions, + ) + prompt_adapter = adapters.get_default_prompt_adapter() + content = prompt_adapter.prompt( + prompt, + system_prompt, + ) + content_dict = utils.parse_code_snippet(content) + try: + action_dicts = content_dict["actions"] + except TypeError as exc: + logger.warning(exc) + # sometimes OpenAI returns a list of dicts directly, so let it slide + action_dicts = content_dict + modified_actions = [] + for action_dict in action_dicts: + action = models.ActionEvent.from_dict(action_dict) + modified_actions.append(action) + return modified_actions + + +def get_window_prompt_dict(active_window: models.WindowEvent) -> dict: + """Convert a WindowEvent into a dict, excluding unnecessary properties. + + Args: + active_window: the WindowEvent to convert + + Returns: + dictionary containing relevant properties from the given WindowEvent. + """ + active_window_dict = deepcopy( + { + key: val + for key, val in utils.row2dict(active_window, follow=False).items() + if val is not None + and not key.endswith("timestamp") + and not key.endswith("id") + # and not isinstance(getattr(models.WindowEvent, key), property) + } + ) + active_window_dict["state"].pop("data") + active_window_dict["state"].pop("meta") + return active_window_dict + + +def get_action_prompt_dict(action: models.ActionEvent) -> dict: + """Convert an ActionEvent into a dict, excluding unnecessary properties. + + Args: + action: the ActionEvent to convert + + Returns: + dictionary containing relevant properties from the given ActionEvent. + """ + action_dict = deepcopy( + { + key: val + for key, val in utils.row2dict(action, follow=False).items() + if val is not None + and not key.endswith("timestamp") + and not key.endswith("id") + # and not isinstance(getattr(models.ActionEvent, key), property) + } + ) + if action.active_segment_description: + for key in ("mouse_x", "mouse_y", "mouse_dx", "mouse_dy"): + if key in action_dict: + del action_dict[key] + if action.available_segment_descriptions: + action_dict["available_segment_descriptions"] = ( + action.available_segment_descriptions + ) + return action_dict + + +class VisualReplayStrategy( + strategies.base.BaseReplayStrategy, +): + """ReplayStrategy using Large Multimodal Model and replay instructions.""" + + def __init__( + self, + recording: models.Recording, + replay_instructions: str, + ) -> None: + """Initialize the VisualReplayStrategy. + + Args: + recording (models.Recording): The recording object. + replay_instructions (str): Natural language instructions for how recording + should be replayed. + """ + super().__init__(recording) + self.recording_action_idx = 0 + add_active_segment_descriptions(recording.processed_action_events) + self.modified_actions = apply_replay_instructions( + recording.processed_action_events, + replay_instructions, + ) + global DEBUG + DEBUG = DEBUG_REPLAY + + def get_next_action_event( + self, + active_screenshot: models.Screenshot, + active_window: models.WindowEvent, + ) -> models.ActionEvent: + """Get the next ActionEvent for replay. + + Since we have already modified the actions, this function just determines + the appropriate coordinates for the modified actions (where appropriate). + + Args: + active_screenshot (models.Screenshot): The active screenshot object. + active_window (models.WindowEvent): The active window event object. + + Returns: + models.ActionEvent: The next ActionEvent for replay. + """ + logger.debug(f"{self.recording_action_idx=}") + if self.recording_action_idx >= len(self.modified_actions): + raise StopIteration() + + # TODO: hack + time.sleep(1) + active_window = models.WindowEvent.get_active_window_event() + + active_screenshot = models.Screenshot.take_screenshot() + logger.info(f"{active_window=}") + + modified_reference_action = self.modified_actions[self.recording_action_idx] + self.recording_action_idx += 1 + + if modified_reference_action.name in common.MOUSE_EVENTS: + modified_reference_action.screenshot = active_screenshot + modified_reference_action.window_event = active_window + modified_reference_action.recording = self.recording + exceptions = [] + while True: + active_window_segmentation = get_window_segmentation( + modified_reference_action, + exceptions=exceptions, + ) + try: + target_segment_idx = active_window_segmentation.descriptions.index( + modified_reference_action.active_segment_description + ) + except ValueError as exc: + logger.warning(f"{exc=}") + exceptions.append(exc) + else: + break + target_centroid = active_window_segmentation.centroids[target_segment_idx] + # = scale_ratio * + width_ratio, height_ratio = utils.get_scale_ratios( + modified_reference_action + ) + target_mouse_x = target_centroid[0] / width_ratio + active_window.left + target_mouse_y = target_centroid[1] / height_ratio + active_window.top + modified_reference_action.mouse_x = target_mouse_x + modified_reference_action.mouse_y = target_mouse_y + return modified_reference_action + + +def get_active_segment( + action: models.ActionEvent, + window_segmentation: Segmentation, + debug: bool = DEBUG, +) -> int: + """Get the index of the bounding box within the action's mouse coordinates. + + Adjust for the scaling of the cropped window and the action coordinates. + Additionally, visualizes the segments and the mouse position. + + Args: + action: the ActionEvent + window_segmentation: the Segmentation + debug: whether to display images useful in debugging + + Returns: + index of active segment in Segmentation + """ + # Obtain the scale ratios + width_ratio, height_ratio = utils.get_scale_ratios(action) + logger.info(f"{width_ratio=} {height_ratio=}") + + # Adjust action coordinates to be relative to the cropped window's top-left corner + adjusted_mouse_x = (action.mouse_x - action.window_event.left) * width_ratio + adjusted_mouse_y = (action.mouse_y - action.window_event.top) * height_ratio + + active_index = None + + if debug: + # Create an empty image with enough space to display all bounding boxes + width = int( + max( + box["left"] + box["width"] for box in window_segmentation.bounding_boxes + ) + ) + height = int( + max( + box["top"] + box["height"] for box in window_segmentation.bounding_boxes + ) + ) + image = Image.new("RGB", (width, height), "white") + draw = ImageDraw.Draw(image) + + for index, box in enumerate(window_segmentation.bounding_boxes): + box_left = box["left"] + box_top = box["top"] + box_right = box["left"] + box["width"] + box_bottom = box["top"] + box["height"] + + if debug: + # Draw each bounding box as a rectangle + draw.rectangle( + [box_left, box_top, box_right, box_bottom], outline="red", width=1 + ) + + # Check if the adjusted action's coordinates are within the bounding box + if ( + box_left <= adjusted_mouse_x < box_right + and box_top <= adjusted_mouse_y < box_bottom + ): + active_index = index + + if debug: + # Draw the adjusted mouse position + draw.ellipse( + [ + adjusted_mouse_x - 5, + adjusted_mouse_y - 5, + adjusted_mouse_x + 5, + adjusted_mouse_y + 5, + ], + fill="blue", + ) + # Display the image without blocking + image.show() + + return active_index + + +def get_window_segmentation( + action_event: models.ActionEvent, + exceptions: list[Exception] | None = None, +) -> Segmentation: + """Segments the active window from the action event's screenshot. + + Args: + action_event: action event containing the screenshot data. + exceptions: list of exceptions previously raised, added to prompt. + + Returns: + Segmnetation object containing detailed segmentation information. + """ + screenshot = action_event.screenshot + screenshot.crop_active_window(action_event) + original_image = screenshot.image + if DEBUG: + original_image.show() + segmentation_adapter = adapters.get_default_segmentation_adapter() + segmented_image = segmentation_adapter.fetch_segmented_image(original_image) + if DEBUG: + segmented_image.show() + masks = vision.process_image_for_masks(segmented_image) + if DEBUG: + vision.display_binary_images_grid(masks) + refined_masks = vision.refine_masks(masks) + if DEBUG: + vision.display_binary_images_grid(refined_masks) + masked_images = vision.extract_masked_images(original_image, refined_masks) + + original_image_base64 = screenshot.base64 + masked_images_base64 = [ + utils.image2utf8(masked_image) for masked_image in masked_images + ] + descriptions = prompt_for_descriptions( + original_image_base64, + masked_images_base64, + action_event.active_segment_description, + exceptions, + ) + bounding_boxes, centroids = vision.calculate_bounding_boxes(refined_masks) + assert len(bounding_boxes) == len(descriptions) == len(centroids), ( + len(bounding_boxes), + len(descriptions), + len(centroids), + ) + segmentation = Segmentation(masked_images, descriptions, bounding_boxes, centroids) + if DEBUG: + vision.display_images_table_with_titles(masked_images, descriptions) + return segmentation + + +def prompt_for_descriptions( + original_image_base64: str, + masked_images_base64: list[str], + active_segment_description: str | None, + exceptions: list[Exception] | None = None, +) -> list[str]: + """Generates descriptions for given image segments using a prompt adapter. + + Args: + original_image_base64: Base64 encoding of the original image. + masked_images_base64: List of base64 encoded masked images. + active_segment_description: Description of the active segment. + exceptions: List of exceptions previously raised, added to prompts. + + Returns: + list of descriptions for each masked image. + """ + prompt_adapter = adapters.get_default_prompt_adapter() + + # TODO: move inside adapters + # off by one to account for original image + if prompt_adapter.MAX_IMAGES and ( + len(masked_images_base64) + 1 > prompt_adapter.MAX_IMAGES + ): + masked_images_base64_batches = utils.split_list( + masked_images_base64, + prompt_adapter.MAX_IMAGES - 1, + ) + descriptions = [] + for masked_images_base64_batch in masked_images_base64_batches: + descriptions_batch = prompt_for_descriptions( + original_image_base64, + masked_images_base64_batch, + active_segment_description, + exceptions, + ) + descriptions += descriptions_batch + return descriptions + + images = [original_image_base64] + masked_images_base64 + system_prompt = utils.render_template_from_file( + "openadapt/prompts/system.j2", + ) + logger.info(f"system_prompt=\n{system_prompt}") + num_segments = len(masked_images_base64) + prompt = utils.render_template_from_file( + "openadapt/prompts/description.j2", + active_segment_description=active_segment_description, + num_segments=num_segments, + exceptions=exceptions, + ) + logger.info(f"prompt=\n{prompt}") + descriptions_json = prompt_adapter.prompt( + prompt, + system_prompt, + images, + ) + descriptions = utils.parse_code_snippet(descriptions_json)["descriptions"] + logger.info(f"{descriptions=}") + try: + assert len(descriptions) == len(masked_images_base64), ( + len(descriptions), + len(masked_images_base64), + ) + except Exception as exc: + # TODO XXX + raise exc + # remove indexes + descriptions = [desc for idx, desc in descriptions] + return descriptions diff --git a/openadapt/utils.py b/openadapt/utils.py index 5ff352d8c..9dfe23690 100644 --- a/openadapt/utils.py +++ b/openadapt/utils.py @@ -6,14 +6,17 @@ from collections import defaultdict from io import BytesIO from logging import StreamHandler -from typing import Union +from typing import Any +import ast import base64 import inspect +import json import os import sys import threading import time +from jinja2 import Environment, FileSystemLoader from loguru import logger from PIL import Image, ImageDraw, ImageFont import fire @@ -21,6 +24,7 @@ import mss import mss.base import numpy as np +import orjson from openadapt import common, config from openadapt.db import db @@ -69,7 +73,7 @@ def configure_logging(logger: logger, log_level: str) -> None: logger.debug(f"{log_level=}") -def row2dict(row: Union[dict, db.BaseModel], follow: bool = True) -> dict: +def row2dict(row: dict | db.BaseModel, follow: bool = True) -> dict: """Convert a row object to a dictionary. Args: @@ -448,6 +452,16 @@ def draw_rectangle( def get_scale_ratios(action_event: ActionEvent) -> tuple[float, float]: """Get the scale ratios for the action event. + = scale_ratio * , e.g: + + width_ratio, height_ratio = get_scale_ratios(action_event) + x0 = window_event.left * width_ratio + y0 = window_event.top * height_ratio + x1 = x0 + window_event.width * width_ratio + y1 = y0 + window_event.height * height_ratio + x = action_event.mouse_x * width_ratio + y = action_event.mouse_y * height_ratio + Args: action_event (ActionEvent): The action event. @@ -456,7 +470,7 @@ def get_scale_ratios(action_event: ActionEvent) -> tuple[float, float]: float: The height ratio. """ recording = action_event.recording - image = action_event.screenshot.image + image = action_event.screenshot.original_image width_ratio = image.width / recording.monitor_width height_ratio = image.height / recording.monitor_height return width_ratio, height_ratio @@ -563,6 +577,7 @@ def display_event( return image +# TODO: png def image2utf8(image: Image.Image) -> str: """Convert an image to UTF-8 format. @@ -584,6 +599,32 @@ def image2utf8(image: Image.Image) -> str: return image_utf8 +def utf82image(image_utf8: str) -> Image.Image: + """Convert a UTF-8 encoded image back into a PIL image object. + + Inverts utf82image. + + Args: + image_utf8 (str): The UTF-8 encoded image. + + Returns: + PIL.Image.Image: The decoded image as a PIL image object. + """ + if not image_utf8: + return None + + # Remove the base64 image prefix + base64_data = image_utf8.split(",", 1)[1] + + # Decode the base64 string + image_bytes = base64.b64decode(base64_data) + + # Convert bytes to image + image = Image.open(BytesIO(image_bytes)) + + return image + + def set_start_time(value: float = None) -> float: """Set the start time for recordings. Required for accurate process-wide timestamps. @@ -805,5 +846,114 @@ def get_functions(name: str) -> dict: return functions +def get_action_dict_from_completion(completion: str) -> dict[ActionEvent]: + """Convert the completion to a dictionary containing action information. + + Args: + completion (str): The completion provided by the user. + + Returns: + dict: The action dictionary. + """ + try: + action = eval(completion) + except Exception as exc: + logger.warning(f"{exc=}") + else: + return action + + +# copied from https://github.com/OpenAdaptAI/OpenAdapt/pull/560/files +def render_template_from_file(template_relative_path: str, **kwargs: dict) -> str: + """Load a Jinja2 template from a file and interpolate arguments. + + Args: + template_relative_path (str): Relative path to the Jinja2 template file + from the project root. + **kwargs: Arguments to interpolate into the template. + + Returns: + str: Rendered template with interpolated arguments. + """ + + def orjson_to_json(value: Any) -> str: + # orjson.dumps returns bytes, so decode to string + return orjson.dumps(value).decode("utf-8") + + # Construct the full path to the template file + template_path = os.path.join(config.ROOT_DIRPATH, template_relative_path) + + # Extract the directory and template file name + template_dir, template_file = os.path.split(template_path) + logger.info(f"{template_dir=} {template_file=}") + + # Create a Jinja2 environment with the directory + env = Environment(loader=FileSystemLoader(template_dir)) + + # Add custom filters + env.filters["orjson"] = orjson_to_json + env.globals.update(zip=zip) + + # Load the template + template = env.get_template(template_file) + + # Render the template with provided arguments + return template.render(**kwargs) + + +def parse_code_snippet(snippet: str) -> dict: + """Parse a text code snippet into a dict. + + e.g. + ```json + { "foo": true } + ``` + Returns: + + { "foo": True } + + Args: + snippet: text snippet + + Returns: + dict representation of what was in the text snippet + """ + try: + if snippet.startswith("```json"): + # Remove Markdown code block syntax + json_string = ( + snippet.replace("```json\n", "") + .replace("```", "") + .replace("True", "true") + .replace("False", "false") + .strip() + ) + # Parse the JSON string + return json.loads(json_string) + elif snippet.startswith("```python"): + python_code = snippet.replace("```python\n", "").replace("```", "").strip() + return ast.literal_eval(python_code) + else: + msg = "Unsupported {snippet=}" + logger.warning(msg) + return None + except Exception as exc: + # TODO + raise exc + + +def split_list(input_list: list, size: int) -> list[list]: + """Splits a list into a list of lists, where each inner list has a maximum size. + + Args: + input_list: The list to be split. + size: The maximum size of each inner list. + + Returns: + A new list containing inner lists of the given size. + """ + return [input_list[i : i + size] for i in range(0, len(input_list), size)] + + if __name__ == "__main__": fire.Fire(get_functions(__name__)) diff --git a/openadapt/vision.py b/openadapt/vision.py new file mode 100644 index 000000000..bc343e811 --- /dev/null +++ b/openadapt/vision.py @@ -0,0 +1,429 @@ +"""Computer vision module.""" + +from loguru import logger +from PIL import Image, ImageDraw +from scipy.ndimage import binary_fill_holes +import cv2 +import numpy as np + +from openadapt import utils + + +def process_image_for_masks(segmented_image: Image.Image) -> list[np.ndarray]: + """Process the image to find unique masks based on color channels. + + Args: + segmented_image: A PIL.Image object of the segmented image. + + Returns: + A list of numpy.ndarrays, each representing a unique mask. + """ + logger.info("starting...") + segmented_image_np = np.array(segmented_image) + + # Assume the last channel is the alpha channel if the image has 4 channels + if segmented_image_np.shape[2] == 4: + segmented_image_np = segmented_image_np[:, :, :3] + + # Find unique colors in the image, each unique color corresponds to a unique mask + unique_colors = np.unique( + segmented_image_np.reshape(-1, segmented_image_np.shape[2]), axis=0 + ) + logger.info(f"{len(unique_colors)=}") + + masks = [] + for color in unique_colors: + # Create a mask for each unique color + mask = np.all(segmented_image_np == color, axis=-1) + masks.append(mask) + + logger.info(f"{len(masks)=}") + return masks + + +def filter_masks_by_size( + masks: list[np.ndarray], + min_mask_size: tuple[int, int] = (15, 15), +) -> list[np.ndarray]: + """Filter masks based on minimum size using the bounding box of "on" pixels. + + Args: + masks: A list of numpy.ndarrays, each representing a mask. + min_mask_size: A tuple specifying the minimum dimensions (height, width) that + the bounding box of the "on" pixels must have to be retained. + + Returns: + A list of numpy.ndarrays, each representing a mask that meets the size criteria. + """ + size_filtered_masks = [] + for mask in masks: + coords = np.argwhere(mask) # Get coordinates of all "on" pixels + if coords.size > 0: + y_min, x_min = coords.min(axis=0) + y_max, x_max = coords.max(axis=0) + height = y_max - y_min + 1 + width = x_max - x_min + 1 + if height >= min_mask_size[0] and width >= min_mask_size[1]: + size_filtered_masks.append(mask) + return size_filtered_masks + + +def refine_masks(masks: list[np.ndarray]) -> list[np.ndarray]: + """Refine the list of masks. + + - Fill holes of any size. + - Remove masks completely contained within other masks. + - Exclude masks where the convex hull does not meet a specified minimum + size in any dimension. + + Args: + masks: A list of numpy.ndarrays, each representing a mask. + min_mask_size: A tuple specifying the minimum dimensions (height, + width) that the convex hull of a mask must have to be retained. + + Returns: + A list of numpy.ndarrays, each representing a refined mask. + """ + logger.info(f"{len(masks)=}") + + masks = remove_border_masks(masks) + masks = filter_thin_ragged_masks(masks) + + # Fill holes in each mask + filled_masks = [binary_fill_holes(mask).astype(np.uint8) for mask in masks] + + size_filtered_masks = filter_masks_by_size(filled_masks) + + # Remove masks completely contained within other masks + refined_masks = [] + for i, mask_i in enumerate(size_filtered_masks): + contained = False + for j, mask_j in enumerate(size_filtered_masks): + if i != j: + # Check if mask_i is completely contained in mask_j + if np.array_equal(mask_i & mask_j, mask_i): + contained = True + break + if not contained: + refined_masks.append(mask_i) + + logger.info(f"{len(refined_masks)=}") + return refined_masks + + +def filter_thin_ragged_masks( + masks: list[np.ndarray], + kernel_size: int = 3, + iterations: int = 5, +) -> list[np.ndarray]: + """Applies morphological operations to filter out thin and ragged masks. + + Args: + masks: A list of ndarrays, where each ndarray is a binary mask. + kernel_size: Size of the structuring element. + iterations: Number of times the operation is applied. + + Returns: + A list of ndarrays with thin and ragged masks filtered out. + """ + logger.info(f"{len(masks)=}") + kernel = np.ones((kernel_size, kernel_size), np.uint8) + filtered_masks = [] + + for mask in masks: + # Convert boolean mask to uint8 + mask_uint8 = mask.astype(np.uint8) * 255 + # Perform erosion + eroded_mask = cv2.erode(mask_uint8, kernel, iterations=iterations) + # Perform dilation + dilated_mask = cv2.dilate(eroded_mask, kernel, iterations=iterations) + + # Convert back to boolean mask and add to the filtered list + filtered_masks.append(dilated_mask > 0) + + logger.info(f"{len(filtered_masks)=}") + return filtered_masks + + +def remove_border_masks( + masks: list[np.ndarray], + threshold_percent: float = 5.0, +) -> list[np.ndarray]: + """Removes masks whose "on" pixels are close to the mask borders on all four sides. + + Args: + masks: A list of ndarrays, where each ndarray is a binary mask. + threshold_percent: A float indicating how close the "on" pixels can be to + the border, represented as a percentage of the mask's dimensions. + + Returns: + - A list of ndarrays with the border masks removed. + """ + + def is_close_to_all_borders(mask: np.ndarray, threshold: float) -> bool: + # Determine actual threshold in pixels based on the percentage + threshold_rows = int(mask.shape[0] * (threshold_percent / 100)) + threshold_cols = int(mask.shape[1] * (threshold_percent / 100)) + + # Check for "on" pixels close to each border + top = np.any(mask[:threshold_rows, :]) + bottom = np.any(mask[-threshold_rows:, :]) + left = np.any(mask[:, :threshold_cols]) + right = np.any(mask[:, -threshold_cols:]) + + # If "on" pixels are close to all borders, return True + return top and bottom and left and right + + logger.info(f"{len(masks)=}") + + filtered_masks = [] + for mask in masks: + # Only add mask if it is not close to all borders + if not is_close_to_all_borders(mask, threshold_percent): + filtered_masks.append(mask) + + logger.info(f"{len(filtered_masks)=}") + return filtered_masks + + +def extract_masked_images( + original_image: Image.Image, + masks: list[np.ndarray], +) -> list[Image.Image]: + """Apply each mask to the original image. + + Resize the image to fit the mask's bounding box, discarding pixels outside the mask. + + Args: + original_image: A PIL.Image object of the original image. + masks: A list of numpy.ndarrays, each representing a refined mask. + + Returns: + A list of PIL.Image objects, each cropped to the mask's bounding box and + containing the content of the original image within that mask. + """ + logger.info(f"{len(masks)=}") + original_image_np = np.array(original_image) + masked_images = [] + + for mask in masks: + # Find the bounding box of the mask + rows = np.any(mask, axis=1) + cols = np.any(mask, axis=0) + rmin, rmax = np.where(rows)[0][[0, -1]] + cmin, cmax = np.where(cols)[0][[0, -1]] + + # Crop the mask and the image to the bounding box + cropped_mask = mask[rmin : rmax + 1, cmin : cmax + 1] + cropped_image = original_image_np[rmin : rmax + 1, cmin : cmax + 1] + + # Apply the mask + masked_image = np.where(cropped_mask[:, :, None], cropped_image, 0).astype( + np.uint8 + ) + masked_images.append(Image.fromarray(masked_image)) + + logger.info(f"{len(masked_images)=}") + return masked_images + + +def calculate_bounding_boxes( + masks: list[np.ndarray], +) -> tuple[list[dict[str, float]], list[tuple[float, float]]]: + """Calculate bounding boxes and centers for each mask in the list separately. + + Args: + masks: A list of numpy.ndarrays, each representing a mask. + + Returns: + A tuple containing two lists: + - The first list contains dictionaries, each containing the "top", + "left", "height", "width" of the bounding box for each mask. + - The second list contains tuples, each representing the "center" as a + tuple of (x, y) for each mask. + """ + bounding_boxes = [] + centroids = [] + for mask in masks: + # Find all indices where mask is True + rows, cols = np.where(mask) + if len(rows) == 0 or len(cols) == 0: # In case of an empty mask + bounding_boxes.append({}) + centroids.append((float("nan"), float("nan"))) + continue + + # Calculate bounding box + top, left = rows.min(), cols.min() + height, width = rows.max() - top, cols.max() - left + + # Calculate center + center_x, center_y = left + width / 2, top + height / 2 + + # Append data to the lists + bounding_boxes.append( + { + "top": float(top), + "left": float(left), + "height": float(height), + "width": float(width), + } + ) + centroids.append((float(center_x), float(center_y))) + + return bounding_boxes, centroids + + +def display_binary_images_grid( + images: list[np.ndarray], + grid_size: tuple[int, int] | None = None, + margin: int = 10, +) -> None: + """Display binary arrays as images on a grid with separation between grid cells. + + Args: + images: A list of binary numpy.ndarrays. + grid_size: Optional tuple (rows, cols) indicating the grid size. + If not provided, a square grid size will be calculated. + margin: The margin size between images in the grid. + """ + if grid_size is None: + grid_size = (int(np.ceil(np.sqrt(len(images)))),) * 2 + + # Determine max dimensions of images in the list + max_width = max(image.shape[1] for image in images) + margin + max_height = max(image.shape[0] for image in images) + margin + + # Create a new image with a white background + total_width = max_width * grid_size[1] + margin + total_height = max_height * grid_size[0] + margin + grid_image = Image.new("1", (total_width, total_height), 1) + + for index, binary_image in enumerate(images): + # Convert ndarray to PIL Image + img = Image.fromarray(binary_image.astype(np.uint8) * 255, "L").convert("1") + img_with_margin = Image.new("1", (img.width + margin, img.height + margin), 1) + img_with_margin.paste(img, (margin // 2, margin // 2)) + + # Calculate the position on the grid + row, col = divmod(index, grid_size[1]) + x = col * max_width + margin // 2 + y = row * max_height + margin // 2 + + # Paste the image into the grid + grid_image.paste(img_with_margin, (x, y)) + + # Display the grid image + grid_image.show() + + +def display_images_table_with_titles( + images: list[Image.Image], + titles: list[str] | None = None, + margin: int = 10, + fontsize: int = 20, +) -> None: + """Display RGB PIL.Images in a table layout with titles to the right of each image. + + Args: + images: A list of RGB PIL.Images. + titles: An optional list of strings containing titles for each image. + margin: The margin size in pixels between images and their titles. + fontsize: The size of the title font. + """ + if titles is None: + titles = [""] * len(images) + elif len(titles) != len(images): + raise ValueError("The length of titles must match the length of images.") + + font = utils.get_font("Arial.ttf", fontsize) + + # Calculate the width and height required for the composite image + max_image_width = max(image.width for image in images) + total_height = sum(image.height for image in images) + margin * (len(images) - 1) + max_title_height = fontsize + margin # simple approach to calculating title height + max_title_width = max(font.getsize(title)[0] for title in titles) + margin + + composite_image_width = max_image_width + max_title_width + margin * 3 + composite_image_height = max( + total_height, max_title_height * len(images) + margin * (len(images) - 1) + ) + + # Create a new image to composite everything onto + composite_image = Image.new( + "RGB", (composite_image_width, composite_image_height), "white" + ) + draw = ImageDraw.Draw(composite_image) + + current_y = 0 + for image, title in zip(images, titles): + # Paste the image + composite_image.paste(image, (margin, current_y)) + # Draw the title + draw.text( + ( + max_image_width + 2 * margin, + current_y + image.height // 2 - fontsize // 2, + ), + title, + fill="black", + font=font, + ) + current_y += image.height + margin + + composite_image.show() + + +""" +# XXX TODO broken, unused +def filter_ui_components( + masks: list[np.ndarray], + area_threshold: tuple[float, float] | None = None, + aspect_ratio_threshold: tuple[float, float] | None = None, + extent_threshold: float | None = None, + solidity_threshold: float | None = None, +) -> list[np.ndarray]: + filtered_masks = [] + + for mask in masks: + mask_uint8 = mask.astype(np.uint8) * 255 + contours, _ = cv2.findContours( + mask_uint8, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE + ) + + for contour in contours: + area = cv2.contourArea(contour) + + if area_threshold is not None and ( + area < area_threshold[0] or area > area_threshold[1] + ): + continue + + x, y, w, h = cv2.boundingRect(contour) + aspect_ratio = float(w) / h + + if aspect_ratio_threshold is not None and ( + aspect_ratio < aspect_ratio_threshold[0] + or aspect_ratio > aspect_ratio_threshold[1] + ): + continue + + bounding_rect_area = w * h + extent = area / bounding_rect_area if bounding_rect_area > 0 else 0 + + if extent_threshold is not None and extent < extent_threshold: + continue + + hull = cv2.convexHull(contour) + hull_area = cv2.contourArea(hull) + solidity = area / hull_area if hull_area > 0 else 0 + + if solidity_threshold is not None and solidity < solidity_threshold: + continue + + contour_mask = np.zeros(mask.shape, dtype=np.uint8) + cv2.drawContours( + contour_mask, [contour], -1, color=255, thickness=cv2.FILLED + ) + filtered_masks.append(contour_mask) + return filtered_masks +""" diff --git a/poetry.lock b/poetry.lock index 8cb6c3d01..77001e51a 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,10 +1,9 @@ -# This file is automatically @generated by Poetry and should not be changed by hand. +# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. 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By default, this includes the public ICANN TLDs and their exceptions. 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