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