added in yolo auto annotation sciprt (#794)
Ben Hoff
6 years ago
committed by
Nikita Manovich
parent
a435b410ed
commit
a0f083d274
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# Object Detection YOLO V3 Python Demo, Async API Performance Showcase
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See [these instructions][1] for converting the yolo weights to the OpenVino format.
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As of OpenVINO 2019 R3, only tensorflow 1.13 and NetworkX 2.3.
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These can be explicitly installed using the following command.
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```bash
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$ pip3 install tensorflow==1.13 networkx==2.3
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```
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Additionally, at the time of writing, the model optimizer required an input shape.
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``` bash
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$ python3 mo_tf.py \
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--input_model /path/to/yolo_v3.pb \
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--tensorflow_use_custom_operations_config $MO_ROOT/extensions/front/tf/yolo_v3.json \
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--input_shape [1,416,416,3]
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```
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[1]: https://docs.openvinotoolkit.org/latest/_docs_MO_DG_prepare_model_convert_model_tf_specific_Convert_YOLO_From_Tensorflow.html
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from math import exp
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class Parser:
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IOU_THRESHOLD = 0.4
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PROB_THRESHOLD = 0.5
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def __init__(self):
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self.objects = []
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def scale_bbox(self, x, y, h, w, class_id, confidence, h_scale, w_scale):
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xmin = int((x - w / 2) * w_scale)
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ymin = int((y - h / 2) * h_scale)
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xmax = int(xmin + w * w_scale)
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ymax = int(ymin + h * h_scale)
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return dict(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, class_id=class_id, confidence=confidence)
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def entry_index(self, side, coord, classes, location, entry):
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side_power_2 = side ** 2
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n = location // side_power_2
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loc = location % side_power_2
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return int(side_power_2 * (n * (coord + classes + 1) + entry) + loc)
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def intersection_over_union(self, box_1, box_2):
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width_of_overlap_area = min(box_1['xmax'], box_2['xmax']) - max(box_1['xmin'], box_2['xmin'])
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height_of_overlap_area = min(box_1['ymax'], box_2['ymax']) - max(box_1['ymin'], box_2['ymin'])
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if width_of_overlap_area < 0 or height_of_overlap_area < 0:
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area_of_overlap = 0
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else:
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area_of_overlap = width_of_overlap_area * height_of_overlap_area
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box_1_area = (box_1['ymax'] - box_1['ymin']) * (box_1['xmax'] - box_1['xmin'])
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box_2_area = (box_2['ymax'] - box_2['ymin']) * (box_2['xmax'] - box_2['xmin'])
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area_of_union = box_1_area + box_2_area - area_of_overlap
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if area_of_union == 0:
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return 0
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return area_of_overlap / area_of_union
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def sort_objects(self):
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self.objects = sorted(self.objects, key=lambda obj : obj['confidence'], reverse=True)
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for i in range(len(self.objects)):
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if self.objects[i]['confidence'] == 0:
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continue
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for j in range(i + 1, len(self.objects)):
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if self.intersection_over_union(self.objects[i], self.objects[j]) > self.IOU_THRESHOLD:
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self.objects[j]['confidence'] = 0
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def parse_yolo_region(self, blob: 'np.ndarray', original_shape: list, params: dict) -> list:
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# YOLO magic numbers
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# See: https://github.com/opencv/open_model_zoo/blob/acf297c73db8cb3f68791ae1fad4a7cc4a6039e5/demos/python_demos/object_detection_demo_yolov3_async/object_detection_demo_yolov3_async.py#L61
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num = 3
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coords = 4
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classes = 80
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# -----------------
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_, _, out_blob_h, out_blob_w = blob.shape
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assert out_blob_w == out_blob_h, "Invalid size of output blob. It sould be in NCHW layout and height should " \
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"be equal to width. Current height = {}, current width = {}" \
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"".format(out_blob_h, out_blob_w)
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# ------ Extracting layer parameters --
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orig_im_h, orig_im_w = original_shape
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predictions = blob.flatten()
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side_square = params['side'] * params['side']
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# ------ Parsing YOLO Region output --
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for i in range(side_square):
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row = i // params['side']
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col = i % params['side']
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for n in range(num):
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# -----entry index calcs------
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obj_index = self.entry_index(params['side'], coords, classes, n * side_square + i, coords)
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# -----entry index calcs------
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scale = predictions[obj_index]
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if scale < self.PROB_THRESHOLD:
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continue
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box_index = self.entry_index(params['side'], coords, classes, n * side_square + i, 0)
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# Network produces location predictions in absolute coordinates of feature maps.
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# Scale it to relative coordinates.
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x = (col + predictions[box_index + 0 * side_square]) / params['side']
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y = (row + predictions[box_index + 1 * side_square]) / params['side']
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# Value for exp is very big number in some cases so following construction is using here
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try:
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w_exp = exp(predictions[box_index + 2 * side_square])
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h_exp = exp(predictions[box_index + 3 * side_square])
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except OverflowError:
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continue
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w = w_exp * params['anchors'][2 * n] / 416
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h = h_exp * params['anchors'][2 * n + 1] / 416
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for j in range(classes):
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class_index = self.entry_index(params['side'], coords, classes, n * side_square + i,
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coords + 1 + j)
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confidence = scale * predictions[class_index]
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if confidence < self.PROB_THRESHOLD:
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continue
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self.objects.append(self.scale_bbox(x=x,
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y=y,
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h=h,
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w=w,
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class_id=j,
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confidence=confidence,
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h_scale=orig_im_h,
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w_scale=orig_im_w))
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for detection in detections:
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frame_number = detection['frame_id']
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height = detection['frame_height']
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width = detection['frame_width']
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detection = detection['detections']
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original_shape = (width, height)
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resized_width = width / 416
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resized_height = height / 416
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resized_shape = (resized_width, resized_height)
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# https://github.com/opencv/open_model_zoo/blob/master/demos/python_demos/object_detection_demo_yolov3_async/object_detection_demo_yolov3_async.py#L72
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anchors = [10,13,16,30,33,23,30,61,62,45,59,119,116,90,156,198,373,326]
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conv_6 = {'side': 13, 'mask': [6,7,8]}
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conv_14 = {'side': 26, 'mask': [3,4,5]}
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conv_22 = {'side': 52, 'mask': [0,1,2]}
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yolo_params = {'detector/yolo-v3/Conv_6/BiasAdd/YoloRegion': conv_6,
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'detector/yolo-v3/Conv_14/BiasAdd/YoloRegion': conv_14,
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'detector/yolo-v3/Conv_22/BiasAdd/YoloRegion': conv_22}
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for conv_net in yolo_params.values():
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mask = conv_net['mask']
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masked_anchors = []
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for idx in mask:
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masked_anchors += [anchors[idx * 2], anchors[idx * 2 + 1]]
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conv_net['anchors'] = masked_anchors
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parser = Parser()
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for name, blob in detection.items():
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parser.parse_yolo_region(blob, original_shape, yolo_params[name])
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parser.sort_objects()
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objects = []
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for obj in parser.objects:
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if obj['confidence'] >= parser.PROB_THRESHOLD:
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label = obj['class_id']
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xmin = obj['xmin']
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xmax = obj['xmax']
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ymin = obj['ymin']
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ymax = obj['ymax']
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results.add_box(xmax, ymax, xmin, ymin, label, frame_number)
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@ -0,0 +1,84 @@
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{
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"label_map": {
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"1": "person",
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"2": "bicycle",
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"3": "car",
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"4": "motorbike",
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"5": "aeroplane",
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"6": "bus",
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"7": "train",
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"8": "truck",
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"9": "boat",
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"10": "traffic light",
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"11": "fire hydrant",
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"12": "stop sign",
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"13": "parking meter",
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"14": "bench",
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"15": "bird",
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"16": "cat",
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"17": "dog",
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"18": "horse",
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"19": "sheep",
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"20": "cow",
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"21": "elephant",
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"22": "bear",
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"23": "zebra",
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"24": "giraffe",
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"25": "backpack",
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"26": "umbrella",
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"27": "handbag",
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"28": "tie",
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"29": "suitcase",
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"30": "frisbee",
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"31": "skis",
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"32": "snowboard",
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"33": "sports ball",
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"34": "kite",
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"35": "baseball bat",
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"36": "baseball glove",
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"37": "skateboard",
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"38": "surfboard",
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"39": "tennis racket",
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"40": "bottle",
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"41": "wine glass",
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"42": "cup",
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"43": "fork",
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"44": "knife",
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"45": "spoon",
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"46": "bowl",
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"47": "banana",
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"48": "apple",
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"49": "sandwich",
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"50": "orange",
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"51": "broccoli",
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"52": "carrot",
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"53": "hot dog",
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"54": "pizza",
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"55": "donut",
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"56": "cake",
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"57": "chair",
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"58": "sofa",
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"59": "pottedplant",
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"60": "bed",
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"61": "diningtable",
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"62": "toilet",
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"63": "tvmonitor",
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"64": "laptop",
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"65": "mouse",
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"66": "remote",
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"67": "keyboard",
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"68": "cell phone",
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"69": "microwave",
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"70": "oven",
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"71": "toaster",
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"72": "sink",
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"73": "refrigerator",
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"74": "book",
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"75": "clock",
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"76": "vase",
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"77": "scissors",
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"78": "teddy bear",
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"79": "hair drier",
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"80": "toothbrush"
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}
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}
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