YOLOP-opencv-dnn/main.py

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()