Submission-Tenacious

Tenacious/main.py

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+import numpy as np
+
+from PIL import Image
+import cv2
+
+import torch
+
+import ultralytics
+from ultralytics import YOLO
+from diffusers import StableDiffusionInpaintPipeline
+
+model = YOLO('yolov8m-seg.pt')
+
+img= cv2.imread('car.jpg')
+image = cv2.resize(img,(640,384))
+
+results = model.predict(source=img.copy(), save=True, save_txt=False, stream=True)
+
+for result in results:
+    # get array results
+    masks = result.masks.data
+    boxes = result.boxes.data
+    # extract classes
+    clss = boxes[:, 5]
+    # get indices of results where class is 0 (people in COCO)
+    car_indices = torch.where(clss == 2)
+    # use these indices to extract the relevant masks
+    car_masks = masks[car_indices]
+    # scale for visualizing results
+    car_mask = torch.any(car_masks, dim=0).int() * 255
+
+    mask_image = car_mask.cpu().numpy()
+
+image = Image.fromarray(image.astype('uint8'), 'RGB')
+mask_image = Image.fromarray(cv2.bitwise_not(mask_image).astype('uint8'))
+
+pipe = StableDiffusionInpaintPipeline.from_pretrained(
+    "runwayml/stable-diffusion-inpainting",
+    revision="fp16",
+    torch_dtype=torch.float32,
+)
+
+prompt = str(input("Enter the prompt: ")) #"high resolution, car on beach"
+
+out_image = pipe(prompt=prompt, image=image, mask_image=mask_image).images[0]
+
+output_image = np.array(out_image)
+output_image = cv2.resize(output_image,image.size)
+
+cv2.imshow("Product Image",ouput_image)
+cv2.waitkey(0)
+

Tenacious/readme.md

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+**Team Name** - Tenacious<br>
+**Problem Statement** - Revolutionary AI-Infused Retail Platform<br>
+**Team Leader Email** - [email protected]<br>
+# A Brief of the Prototype
+GenAI Product Shots, will generate professional studio equivalent images of products.
+GenAI Product Shots will cut the photography time, cost and resource.
+We will mask the object from the image, then original image and masked image will be given as an input.
+Stable diffusion will produce desirable results based on the input and prompt.
+Multiple variant of these images with different colours and backgrounds can be used for product catalogue.
+
+![Architecture Diagram](architecture_diagram.png)
+
+
+# Tech Stack
+- Intel Extension for PyTorch
+- PyTorch-GPU
+- OpenCV
+- Numpy
+- Pillow
+- YOLO
+- Stable Diffusion
+
+# Step-by-Step Code Execution Instructions
+
+``git clone https://github.com/akkmr1996/oneAPI-GenAI-Hackathon-2023.git``
+
+``cd Tenacious``
+
+``pip install -r requirements.txt``
+
+``python main.py``
+<br>
+
+# Future Scope
+- We will develop a user friendly web application using Flask.
+- In this we have only used a car as a product, but in future we will use different product like food product, beauty and fashion products.
+- Then we need to train a segmentation model for these products and then deployed it on Intel Cloud and hence use the Intel OneAPI Toolkit.
+

Tenacious/requirements.txt

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