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.')