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assert self.yolo_model.layers[-1].output_shape[-1] == \
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num_anchors/len(self.yolo_model.output) * (num_classes + 5), \
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'Mismatch between model and given anchor and class sizes'
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print('{} model, anchors, and classes loaded.'.format(model_path))
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# Generate colors for drawing bounding boxes.
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hsv_tuples = [(x / len(self.class_names), 1., 1.)
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for x in range(len(self.class_names))]
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self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
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self.colors = list(
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map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)),
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self.colors))
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np.random.seed(10101) # Fixed seed for consistent colors across runs.
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np.random.shuffle(self.colors) # Shuffle colors to decorrelate adjacent classes.
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np.random.seed(None) # Reset seed to default.
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# Generate output tensor targets for filtered bounding boxes.
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self.input_image_shape = K.placeholder(shape=(2, ))
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if self.gpu_num>=2:
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self.yolo_model = multi_gpu_model(self.yolo_model, gpus=self.gpu_num)
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boxes, scores, classes = yolo_eval(self.yolo_model.output, self.anchors,
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len(self.class_names), self.input_image_shape,
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score_threshold=self.score, iou_threshold=self.iou)
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return boxes, scores, classes
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def detect_image(self, image):
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start = timer()
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if self.model_image_size != (None, None):
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assert self.model_image_size[0]%32 == 0, 'Multiples of 32 required'
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assert self.model_image_size[1]%32 == 0, 'Multiples of 32 required'
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boxed_image = letterbox_image(image, tuple(reversed(self.model_image_size)))
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else:
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new_image_size = (image.width - (image.width % 32),
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image.height - (image.height % 32))
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boxed_image = letterbox_image(image, new_image_size)
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image_data = np.array(boxed_image, dtype='float32')
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print(image_data.shape)
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image_data /= 255.
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image_data = np.expand_dims(image_data, 0) # Add batch dimension.
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out_boxes, out_scores, out_classes = self.sess.run(
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[self.boxes, self.scores, self.classes],
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feed_dict={
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self.yolo_model.input: image_data,
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self.input_image_shape: [image.size[1], image.size[0]],
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K.learning_phase(): 0
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})
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print('Found {} boxes for {}'.format(len(out_boxes), 'img'))
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font = ImageFont.truetype(font='font/FiraMono-Medium.otf',
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size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32'))
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thickness = (image.size[0] + image.size[1]) // 300
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for i, c in reversed(list(enumerate(out_classes))):
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predicted_class = self.class_names[c]
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box = out_boxes[i]
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score = out_scores[i]
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label = '{} {:.2f}'.format(predicted_class, score)
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draw = ImageDraw.Draw(image)
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label_size = draw.textsize(label, font)
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top, left, bottom, right = box
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top = max(0, np.floor(top + 0.5).astype('int32'))
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left = max(0, np.floor(left + 0.5).astype('int32'))
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bottom = min(image.size[1], np.floor(bottom + 0.5).astype('int32'))
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right = min(image.size[0], np.floor(right + 0.5).astype('int32'))
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print(label, (left, top), (right, bottom))
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if top - label_size[1] >= 0:
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text_origin = np.array([left, top - label_size[1]])
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else:
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text_origin = np.array([left, top + 1])
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# My kingdom for a good redistributable image drawing library.
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for i in range(thickness):
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draw.rectangle(
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[left + i, top + i, right - i, bottom - i],
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outline=self.colors[c])
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draw.rectangle(
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[tuple(text_origin), tuple(text_origin + label_size)],
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fill=self.colors[c])
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draw.text(text_origin, label, fill=(0, 0, 0), font=font)
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del draw
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end = timer()
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print(end - start)
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return image
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def close_session(self):
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self.sess.close()
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def detect_video(yolo, video_path, output_path=""):
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import cv2
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vid = cv2.VideoCapture(0)
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if not vid.isOpened():
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