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160 lines
7.4 KiB
Python
160 lines
7.4 KiB
Python
# Copyright (C) 2020 Intel Corporation
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#
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# SPDX-License-Identifier: MIT
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import os
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from math import exp
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from model_loader import ModelLoader
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class YoloParams:
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# ------------------------------------------- Extracting layer parameters ------------------------------------------
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# Magic numbers are copied from yolo samples
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def __init__(self, param, side):
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self.num = 3 if 'num' not in param else int(param['num'])
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self.coords = 4 if 'coords' not in param else int(param['coords'])
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self.classes = 80 if 'classes' not in param else int(param['classes'])
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self.side = side
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self.anchors = [10.0, 13.0, 16.0, 30.0, 33.0, 23.0, 30.0, 61.0, 62.0, 45.0, 59.0, 119.0, 116.0, 90.0, 156.0,
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198.0,
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373.0, 326.0] if 'anchors' not in param else [float(a) for a in param['anchors'].split(',')]
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self.isYoloV3 = False
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if param.get('mask'):
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mask = [int(idx) for idx in param['mask'].split(',')]
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self.num = len(mask)
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maskedAnchors = []
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for idx in mask:
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maskedAnchors += [self.anchors[idx * 2], self.anchors[idx * 2 + 1]]
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self.anchors = maskedAnchors
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self.isYoloV3 = True # Weak way to determine but the only one.
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def entry_index(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 scale_bbox(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 parse_yolo_region(blob, resized_image_shape, original_im_shape, params, threshold):
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# ------------------------------------------ Validating output parameters ------------------------------------------
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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_im_shape
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resized_image_h, resized_image_w = resized_image_shape
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objects = list()
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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(params.num):
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obj_index = entry_index(params.side, params.coords, params.classes, n * side_square + i, params.coords)
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scale = predictions[obj_index]
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if scale < threshold:
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continue
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box_index = entry_index(params.side, params.coords, params.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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# Depends on topology we need to normalize sizes by feature maps (up to YOLOv3) or by input shape (YOLOv3)
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w = w_exp * params.anchors[2 * n] / (resized_image_w if params.isYoloV3 else params.side)
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h = h_exp * params.anchors[2 * n + 1] / (resized_image_h if params.isYoloV3 else params.side)
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for j in range(params.classes):
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class_index = entry_index(params.side, params.coords, params.classes, n * side_square + i,
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params.coords + 1 + j)
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confidence = scale * predictions[class_index]
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if confidence < threshold:
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continue
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objects.append(scale_bbox(x=x, y=y, h=h, w=w, class_id=j, confidence=confidence,
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h_scale=orig_im_h, w_scale=orig_im_w))
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return objects
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def intersection_over_union(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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class ModelHandler:
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def __init__(self, labels):
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base_dir = os.path.abspath(os.environ.get("MODEL_PATH",
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"/opt/nuclio/open_model_zoo/public/yolo-v3-tf/FP32"))
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model_xml = os.path.join(base_dir, "yolo-v3-tf.xml")
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model_bin = os.path.join(base_dir, "yolo-v3-tf.bin")
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self.model = ModelLoader(model_xml, model_bin)
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self.labels = labels
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def infer(self, image, threshold):
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output_layer = self.model.infer(image)
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# Collecting object detection results
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objects = []
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origin_im_size = (image.height, image.width)
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for layer_name, out_blob in output_layer.items():
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out_blob = out_blob.reshape(self.model.layers[self.model.layers[layer_name].parents[0]].shape)
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layer_params = YoloParams(self.model.layers[layer_name].params, out_blob.shape[2])
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objects += parse_yolo_region(out_blob, self.model.input_size(),
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origin_im_size, layer_params, threshold)
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# Filtering overlapping boxes (non-maximum supression)
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IOU_THRESHOLD = 0.4
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objects = sorted(objects, key=lambda obj : obj['confidence'], reverse=True)
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for i, obj in enumerate(objects):
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if obj['confidence'] == 0:
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continue
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for j in range(i + 1, len(objects)):
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if intersection_over_union(obj, objects[j]) > IOU_THRESHOLD:
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objects[j]['confidence'] = 0
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results = []
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for obj in objects:
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if obj['confidence'] >= threshold:
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xtl = max(obj['xmin'], 0)
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ytl = max(obj['ymin'], 0)
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xbr = min(obj['xmax'], image.width)
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ybr = min(obj['ymax'], image.height)
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obj_class = int(obj['class_id'])
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results.append({
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"confidence": str(obj['confidence']),
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"label": self.labels.get(obj_class, "unknown"),
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"points": [xtl, ytl, xbr, ybr],
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"type": "rectangle",
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})
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return results |