import cv2 import argparse import numpy as np class yolop(): def __init__(self, confThreshold=0.25, nmsThreshold=0.5, objThreshold=0.45): with open('bdd100k.names', 'rt') as f: self.classes = f.read().rstrip('\n').split('\n') ###这个是在bdd100k数据集上训练的模型做opencv部署的，如果你在自己的数据集上训练出的模型做opencv部署，那么需要修改self.classes num_classes = len(self.classes) anchors = [[3,9,5,11,4,20], [7,18,6,39,12,31], [19,50,38,81,68,157]] self.nl = len(anchors) self.na = len(anchors[0]) // 2 self.no = num_classes + 5 self.stride = np.array([8., 16., 32.]) self.anchor_grid = np.asarray(anchors, dtype=np.float32).reshape(self.nl, -1, 2) self.inpWidth = 640 self.inpHeight = 640 self.generate_grid() self.net = cv2.dnn.readNet('yolop.onnx') self.confThreshold = confThreshold self.nmsThreshold = nmsThreshold self.objThreshold = objThreshold self.mean = np.array([0.485, 0.456, 0.406], dtype=np.float32).reshape(1, 1, 3) self.std = np.array([0.229, 0.224, 0.225], dtype=np.float32).reshape(1, 1, 3) self.keep_ratio = True def generate_grid(self): self.grid = [np.zeros(1)] * self.nl self.length = [] self.areas = [] for i in range(self.nl): h, w = int(self.inpHeight/self.stride[i]), int(self.inpWidth/self.stride[i]) self.length.append(int(self.na * h * w)) self.areas.append(h*w) if self.grid[i].shape[2:4] != (h,w): self.grid[i] = self._make_grid(w, h) def _make_grid(self, nx=20, ny=20): xv, yv = np.meshgrid(np.arange(ny), np.arange(nx)) return np.stack((xv, yv), 2).reshape((-1, 2)).astype(np.float32) def postprocess(self, frame, outs, newh, neww, padh, padw): frameHeight = frame.shape[0] frameWidth = frame.shape[1] ratioh, ratiow = frameHeight / newh, frameWidth / neww # Scan through all the bounding boxes output from the network and keep only the # ones with high confidence scores. Assign the box's class label as the class with the highest score. classIds = [] confidences = [] boxes = [] for detection in outs: scores = detection[5:] classId = np.argmax(scores) confidence = scores[classId] if confidence > self.confThreshold and detection[4] > self.objThreshold: center_x = int((detection[0]-padw) * ratiow) center_y = int((detection[1]-padh) * ratioh) width = int(detection[2] * ratiow) height = int(detection[3] * ratioh) left = int(center_x - width / 2) top = int(center_y - height / 2) classIds.append(classId) confidences.append(float(confidence) * detection[4]) boxes.append([left, top, width, height]) # Perform non maximum suppression to eliminate redundant overlapping boxes with # lower confidences. indices = cv2.dnn.NMSBoxes(boxes, confidences, self.confThreshold, self.nmsThreshold) for i in indices: i = i[0] box = boxes[i] left = box[0] top = box[1] width = box[2] height = box[3] frame = self.drawPred(frame, classIds[i], confidences[i], left, top, left + width, top + height) return frame def drawPred(self, frame, classId, conf, left, top, right, bottom): # Draw a bounding box. cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), thickness=2) label = '%.2f' % conf label = '%s:%s' % (self.classes[classId], label) # Display the label at the top of the bounding box labelSize, baseLine = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1) top = max(top, labelSize[1]) # cv.rectangle(frame, (left, top - round(1.5 * labelSize[1])), (left + round(1.5 * labelSize[0]), top + baseLine), (255,255,255), cv.FILLED) cv2.putText(frame, label, (left, top - 10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), thickness=1) return frame def resize_image(self, srcimg): padh, padw, newh, neww = 0, 0, self.inpHeight, self.inpWidth if self.keep_ratio and srcimg.shape[0] != srcimg.shape[1]: hw_scale = srcimg.shape[0] / srcimg.shape[1] if hw_scale > 1: newh, neww = self.inpHeight, int(self.inpWidth / hw_scale) img = cv2.resize(srcimg, (neww, newh), interpolation=cv2.INTER_AREA) padw = int((self.inpWidth - neww) * 0.5) img = cv2.copyMakeBorder(img, 0, 0, padw, self.inpWidth - neww - padw, cv2.BORDER_CONSTANT, value=0) # add border else: newh, neww = int(self.inpHeight * hw_scale), self.inpWidth img = cv2.resize(srcimg, (neww, newh), interpolation=cv2.INTER_AREA) padh = int((self.inpHeight - newh) * 0.5) img = cv2.copyMakeBorder(img, padh, self.inpHeight - newh - padh, 0, 0, cv2.BORDER_CONSTANT, value=0) else: img = cv2.resize(srcimg, (self.inpWidth, self.inpHeight), interpolation=cv2.INTER_AREA) return img, newh, neww, padh, padw def _normalize(self, img): ### c++: https://blog.csdn.net/wuqingshan2010/article/details/107727909 img = img.astype(np.float32) / 255.0 img = (img - self.mean) / self.std return img def detect(self, srcimg): img, newh, neww, padh, padw = self.resize_image(srcimg) img = self._normalize(img) blob = cv2.dnn.blobFromImage(img) # Sets the input to the network self.net.setInput(blob) # Runs the forward pass to get output of the output layers outs = self.net.forward(self.net.getUnconnectedOutLayersNames()) # inference output outimg = srcimg.copy() drive_area_mask = outs[1][:, padh:(self.inpHeight - padh), padw:(self.inpWidth - padw)] seg_id = np.argmax(drive_area_mask, axis=0).astype(np.uint8) seg_id = cv2.resize(seg_id, (srcimg.shape[1], srcimg.shape[0]), interpolation=cv2.INTER_NEAREST) outimg[seg_id == 1] = [0, 255, 0] lane_line_mask = outs[2][:, padh:(self.inpHeight - padh), padw:(self.inpWidth - padw)] seg_id = np.argmax(lane_line_mask, axis=0).astype(np.uint8) seg_id = cv2.resize(seg_id, (srcimg.shape[1], srcimg.shape[0]), interpolation=cv2.INTER_NEAREST) outimg[seg_id == 1] = [255, 0, 0] det_out = outs[0] row_ind = 0 for i in range(self.nl): det_out[row_ind:row_ind+self.length[i], 0:2] = (det_out[row_ind:row_ind+self.length[i], 0:2] * 2. - 0.5 + np.tile(self.grid[i],(self.na, 1))) * int(self.stride[i]) det_out[row_ind:row_ind+self.length[i], 2:4] = (det_out[row_ind:row_ind+self.length[i], 2:4] * 2) ** 2 * np.repeat(self.anchor_grid[i], self.areas[i], axis=0) row_ind += self.length[i] outimg = self.postprocess(outimg, det_out, newh, neww, padh, padw) return outimg if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--imgpath", type=str, default='images/0ace96c3-48481887.jpg', help="image path") parser.add_argument('--confThreshold', default=0.25, type=float, help='class confidence') parser.add_argument('--nmsThreshold', default=0.45, type=float, help='nms iou thresh') parser.add_argument('--objThreshold', default=0.5, type=float, help='object confidence') args = parser.parse_args() yolonet = yolop(confThreshold=args.confThreshold, nmsThreshold=args.nmsThreshold, objThreshold=args.objThreshold) srcimg = cv2.imread(args.imgpath) outimg = yolonet.detect(srcimg) winName = 'Deep learning object detection in OpenCV' cv2.namedWindow(winName, 0) cv2.imshow(winName, outimg) cv2.waitKey(0) cv2.destroyAllWindows()