202 lines
6.5 KiB
Python
202 lines
6.5 KiB
Python
import numpy as np
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import cv2
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import os
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import urllib.request
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NUM_CLS = 80
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MAX_BOXES = 500
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OBJ_THRESH = 0.5
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NMS_THRESH = 0.6
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CLASSES = ("person", "bicycle", "car","motorbike ","aeroplane ","bus ","train","truck ","boat","traffic light",
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"fire hydrant","stop sign ","parking meter","bench","bird","cat","dog ","horse ","sheep","cow","elephant",
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"bear","zebra ","giraffe","backpack","umbrella","handbag","tie","suitcase","frisbee","skis","snowboard","sports ball","kite",
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"baseball bat","baseball glove","skateboard","surfboard","tennis racket","bottle","wine glass","cup","fork","knife ",
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"spoon","bowl","banana","apple","sandwich","orange","broccoli","carrot","hot dog","pizza ","donut","cake","chair","sofa",
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"pottedplant","bed","diningtable","toilet ","tvmonitor","laptop ","mouse ","remote ","keyboard ","cell phone","microwave ",
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"oven ","toaster","sink","refrigerator ","book","clock","vase","scissors ","teddy bear ","hair drier", "toothbrush ")
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def sigmoid(x):
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return 1 / (1 + np.exp(-x))
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def process(input, mask, anchors):
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anchors = [anchors[i] for i in mask]
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grid_h, grid_w = map(int, input.shape[0:2])
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box_confidence = sigmoid(input[..., 4])
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box_confidence = np.expand_dims(box_confidence, axis=-1)
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box_class_probs = sigmoid(input[..., 5:])
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box_xy = sigmoid(input[..., :2])
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box_wh = np.exp(input[..., 2:4])
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box_wh = box_wh * anchors
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col = np.tile(np.arange(0, grid_w), grid_w).reshape(-1, grid_w)
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row = np.tile(np.arange(0, grid_h).reshape(-1, 1), grid_h)
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col = col.reshape(grid_h, grid_w, 1, 1).repeat(3, axis=-2)
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row = row.reshape(grid_h, grid_w, 1, 1).repeat(3, axis=-2)
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grid = np.concatenate((col, row), axis=-1)
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box_xy += grid
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box_xy /= (grid_w, grid_h)
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box_wh /= (416, 416)
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box_xy -= (box_wh / 2.)
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box = np.concatenate((box_xy, box_wh), axis=-1)
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return box, box_confidence, box_class_probs
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def filter_boxes(boxes, box_confidences, box_class_probs):
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"""Filter boxes with object threshold.
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# Arguments
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boxes: ndarray, boxes of objects.
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box_confidences: ndarray, confidences of objects.
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box_class_probs: ndarray, class_probs of objects.
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# Returns
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boxes: ndarray, filtered boxes.
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classes: ndarray, classes for boxes.
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scores: ndarray, scores for boxes.
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"""
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box_scores = box_confidences * box_class_probs
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box_classes = np.argmax(box_scores, axis=-1)
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box_class_scores = np.max(box_scores, axis=-1)
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pos = np.where(box_class_scores >= OBJ_THRESH)
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boxes = boxes[pos]
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classes = box_classes[pos]
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scores = box_class_scores[pos]
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return boxes, classes, scores
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def nms_boxes(boxes, scores):
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"""Suppress non-maximal boxes.
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# Arguments
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boxes: ndarray, boxes of objects.
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scores: ndarray, scores of objects.
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# Returns
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keep: ndarray, index of effective boxes.
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"""
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x = boxes[:, 0]
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y = boxes[:, 1]
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w = boxes[:, 2]
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h = boxes[:, 3]
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areas = w * h
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order = scores.argsort()[::-1]
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keep = []
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while order.size > 0:
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i = order[0]
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keep.append(i)
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xx1 = np.maximum(x[i], x[order[1:]])
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yy1 = np.maximum(y[i], y[order[1:]])
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xx2 = np.minimum(x[i] + w[i], x[order[1:]] + w[order[1:]])
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yy2 = np.minimum(y[i] + h[i], y[order[1:]] + h[order[1:]])
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w1 = np.maximum(0.0, xx2 - xx1 + 0.00001)
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h1 = np.maximum(0.0, yy2 - yy1 + 0.00001)
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inter = w1 * h1
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ovr = inter / (areas[i] + areas[order[1:]] - inter)
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inds = np.where(ovr <= NMS_THRESH)[0]
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order = order[inds + 1]
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keep = np.array(keep)
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return keep
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def yolov3_post_process(input_data):
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# yolov3
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masks = [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
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anchors = [[10, 13], [16, 30], [33, 23], [30, 61], [62, 45],
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[59, 119], [116, 90], [156, 198], [373, 326]]
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# yolov3-tiny
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# masks = [[3, 4, 5], [0, 1, 2]]
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# anchors = [[10, 14], [23, 27], [37, 58], [81, 82], [135, 169], [344, 319]]
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boxes, classes, scores = [], [], []
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for input,mask in zip(input_data, masks):
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b, c, s = process(input, mask, anchors)
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b, c, s = filter_boxes(b, c, s)
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boxes.append(b)
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classes.append(c)
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scores.append(s)
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boxes = np.concatenate(boxes)
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classes = np.concatenate(classes)
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scores = np.concatenate(scores)
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nboxes, nclasses, nscores = [], [], []
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for c in set(classes):
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inds = np.where(classes == c)
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b = boxes[inds]
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c = classes[inds]
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s = scores[inds]
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keep = nms_boxes(b, s)
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nboxes.append(b[keep])
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nclasses.append(c[keep])
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nscores.append(s[keep])
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if not nclasses and not nscores:
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return None, None, None
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boxes = np.concatenate(nboxes)
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classes = np.concatenate(nclasses)
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scores = np.concatenate(nscores)
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return boxes, classes, scores
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def draw(image, boxes, scores, classes):
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"""Draw the boxes on the image.
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# Argument:
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image: original image.
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boxes: ndarray, boxes of objects.
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classes: ndarray, classes of objects.
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scores: ndarray, scores of objects.
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all_classes: all classes name.
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"""
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for box, score, cl in zip(boxes, scores, classes):
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x, y, w, h = box
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print('class: {}, score: {}'.format(CLASSES[cl], score))
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print('box coordinate left,top,right,down: [{}, {}, {}, {}]'.format(x, y, x+w, y+h))
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x *= image.shape[1]
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y *= image.shape[0]
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w *= image.shape[1]
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h *= image.shape[0]
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top = max(0, np.floor(x + 0.5).astype(int))
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left = max(0, np.floor(y + 0.5).astype(int))
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right = min(image.shape[1], np.floor(x + w + 0.5).astype(int))
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bottom = min(image.shape[0], np.floor(y + h + 0.5).astype(int))
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cv2.rectangle(image, (top, left), (right, bottom), (255, 0, 0), 2)
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cv2.putText(image, '{0} {1:.2f}'.format(CLASSES[cl], score),
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(top, left - 6),
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cv2.FONT_HERSHEY_SIMPLEX,
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0.6, (0, 0, 255), 2)
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def download_yolov3_weight(dst_path):
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if os.path.exists(dst_path):
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print('yolov3.weight exist.')
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return
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print('Downloading yolov3.weights...')
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url = 'https://pjreddie.com/media/files/yolov3.weights'
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try:
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urllib.request.urlretrieve(url, dst_path)
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except urllib.error.HTTPError as e:
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print('HTTPError code: ', e.code)
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print('HTTPError reason: ', e.reason)
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exit(-1)
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except urllib.error.URLError as e:
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print('URLError reason: ', e.reason)
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else:
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print('Download yolov3.weight success.')
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