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demo.py
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demo.py
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import tensorflow as tf
import numpy as np
from utils import label_map_util
from utils import visualization_utils_original as vis_util
import cv2
import time
from skvideo.io import FFmpegWriter
import helper
from keras.preprocessing import image
from keras.applications.resnet50 import ResNet50
from keras.layers import Dense, Activation, Flatten, Dropout
from keras import backend as K
def load_image_into_numpy_array(image):
(im_width, im_height) = image.size
return np.array(image.getdata()).reshape(
(im_height, im_width, 3)).astype(np.uint8)
def run_inference_for_single_image(image, graph):
with graph.as_default():
with tf.Session() as sess:
# Get handles to input and output tensors
ops = tf.get_default_graph().get_operations()
all_tensor_names = {output.name for op in ops for output in op.outputs}
tensor_dict = {}
for key in [
'num_detections', 'detection_boxes', 'detection_scores',
'detection_classes', 'detection_masks'
]:
tensor_name = key + ':0'
if tensor_name in all_tensor_names:
tensor_dict[key] = tf.get_default_graph().get_tensor_by_name(
tensor_name)
if 'detection_masks' in tensor_dict:
# The following processing is only for single image
detection_boxes = tf.squeeze(tensor_dict['detection_boxes'], [0])
detection_masks = tf.squeeze(tensor_dict['detection_masks'], [0])
# Reframe is required to translate mask from box coordinates to image coordinates and fit the image size.
real_num_detection = tf.cast(tensor_dict['num_detections'][0], tf.int32)
detection_boxes = tf.slice(detection_boxes, [0, 0], [real_num_detection, -1])
detection_masks = tf.slice(detection_masks, [0, 0, 0], [real_num_detection, -1, -1])
detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(
detection_masks, detection_boxes, image.shape[0], image.shape[1])
detection_masks_reframed = tf.cast(
tf.greater(detection_masks_reframed, 0.5), tf.uint8)
# Follow the convention by adding back the batch dimension
tensor_dict['detection_masks'] = tf.expand_dims(
detection_masks_reframed, 0)
image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')
# Run inference
output_dict = sess.run(tensor_dict,
feed_dict={image_tensor: np.expand_dims(image, 0)})
# all outputs are float32 numpy arrays, so convert types as appropriate
output_dict['num_detections'] = int(output_dict['num_detections'][0])
output_dict['detection_classes'] = output_dict[
'detection_classes'][0].astype(np.uint8)
output_dict['detection_boxes'] = output_dict['detection_boxes'][0]
output_dict['detection_scores'] = output_dict['detection_scores'][0]
if 'detection_masks' in output_dict:
output_dict['detection_masks'] = output_dict['detection_masks'][0]
return output_dict
PATH_TO_FROZEN_GRAPH = '/Users/schen/hat-detector/output_inference_graph_v1.pb/frozen_inference_graph.pb'
PATH_TO_LABELS = '/Users/schen/hat-detector/label_map.pbtxt'
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(PATH_TO_FROZEN_GRAPH, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True)
# cap = cv2.VideoCapture('test.avi')
output_video = FFmpegWriter("output_final.mp4")
class_list_file = "./classifier/checkpoints/ResNet50_data_class_list.txt"
class_list = helper.load_class_list(class_list_file)
from keras.applications.resnet50 import preprocess_input
preprocessing_function = preprocess_input
WIDTH = 300
HEIGHT = 300
base_model = ResNet50(weights='imagenet', include_top=False, input_shape=(HEIGHT, WIDTH, 3))
finetune_model = helper.build_finetune_model(base_model, 0.003, [1024, 1024], len(class_list))
finetune_model.load_weights("./classifier/checkpoints/ResNet50_model_weights.h5")
cap = cv2.VideoCapture(0)
while(True):
# while(cap.isOpened()):
# Capture frame-by-frame
ret, image_np = cap.read()
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
image_np_expanded = np.expand_dims(image_np, axis=0)
# Actual detection.
start_time = time.time()
output_dict = run_inference_for_single_image(image_np, detection_graph)
elapsed_time = time.time() - start_time
print('Inference time cost: {}'.format(elapsed_time))
# Our operations on the frame come here
# Display the resulting frame
vis_util.visualize_boxes_and_labels_on_image_array(
image_np,
output_dict['detection_boxes'],
output_dict['detection_classes'],
output_dict['detection_scores'],
category_index,
instance_masks=output_dict.get('detection_masks'),
use_normalized_coordinates=True,
line_thickness=8)
# if coords:
# target = coords[0]
# img = image_np[target[2]:target[3], target[0]:target[1]]
# height, width = img
# img = preprocess_input(img.reshape(1, HEIGHT, WIDTH, 3))
# st = time.time()
# out = finetune_model.predict(img)
# confidence = out[0]
# class_prediction = list(out[0]).index(max(out[0]))
# class_name = class_list[class_prediction]
# run_time = time.time()-st
# print("Predicted class = ", class_name)
# print("Confidence = ", confidence)
# print("Run time = ", run_time)
# cv2.imwrite("preds/" + class_name[0] + ".jpg", image_np)
output_video.writeFrame(np.flip(image_np, 2))
cv2.imshow('frame',image_np)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# When everything done, release the capture
cap.release()
cv2.destroyAllWindows()