文字检测模型EAST应用详解二 pb的opencv加载

贴一下opencv加载pb的方法，跟加载其他模型没有区别。
def main(argv=None):
    # import the necessary packages
    from imutils.object_detection import non_max_suppression
    import numpy as np
    import argparse
    import time
    import cv2
    # construct the argument parser and parse the arguments
    ap = argparse.ArgumentParser()
    ap.add_argument("-i", "--image", type=str,
                    help="D:/work/video/EAST/tmp/frame_74.jpg")
    ap.add_argument("-east", "--east", type=str,
                    help="D:/work/video/hand_tracking_no_op/hand_tracking/EAST/east_icdar2015_resnet_v1_50_rbox/out.pb")
    ap.add_argument("-c", "--min-confidence", type=float, default=0.5, 
                    help="minimum probability required to inspect a region")
    ap.add_argument("-w", "--width", type=int, default=320,
                    help="resized image width (should be multiple of 32)")
    ap.add_argument("-e", "--height", type=int, default=320,
                    help="resized image height (should be multiple of 32)")
    args = vars(ap.parse_args())
    layerNames = [
        "feature_fusion/Conv_7/Sigmoid",
        "feature_fusion/concat_3"]
    # load the pre-trained EAST text detector
    print("[INFO] loading EAST text detector...")
    # net = cv2.dnn.readNet(args["east"])
    pbtxt_path='D:/work/video/EAST/protobuf.pbtxt'
    net = cv2.dnn.readNetFromTensorflow(args["east"])

    # load the input image and grab the image dimensions
    for i in range(15):
        image = cv2.imread(args["image"])
        orig = image.copy()
        (H, W) = image.shape[:2]
        # set the new width and height and then determine the ratio in change
        # for both the width and height
        # resize the image and grab the new image dimensions
        # construct a blob from the image and then perform a forward pass of
        # the model to obtain the two output layer sets
        blob = cv2.dnn.blobFromImage(image, 1.0, (W,H), swapRB=True, crop=False)
        net.setInput(blob)
        start = time.time()
        (scores, geometry) = net.forward(layerNames)

        #geometry = net.forward()
        end = time.time()
        # show timing information on text prediction
        print("[INFO] text detection took {:.6f} seconds".format(end - start))
    # grab the number of rows and columns from the scores volume, then
    # initialize our set of bounding box rectangles and corresponding
    # confidence scores

        (numRows, numCols) = scores.shape[2:4]
        print('numRows, numCols:',numRows, numCols)
        rects = []
        confidences = []
        # loop over the number of rows
        for y in range(0, numRows):
            # extract the scores (probabilities), followed by the geometrical
            # data used to derive potential bounding box coordinates that
            # surround text
            scoresData = scores[0, 0, y]
            xData0 = geometry[0, 0, y]
            xData1 = geometry[0, 1, y]
            xData2 = geometry[0, 2, y]
            xData3 = geometry[0, 3, y]
            anglesData = geometry[0, 4, y]

            # loop over the number of columns
            for x in range(0, numCols):
                # if our score does not have sufficient probability, ignore it
                if scoresData[x] < args["min_confidence"]:
                    continue

                (offsetX, offsetY) = (x * 4.0, y * 4.0)
                # extract the rotation angle for the prediction and then
                # compute the sin and cosine
                angle = anglesData[x]
                cos = np.cos(angle)
                sin = np.sin(angle)
                # use the geometry volume to derive the width and height of
                # the bounding box
                h = xData0[x] + xData2[x]
                w = xData1[x] + xData3[x]
                # compute both the starting and ending (x, y)-coordinates for
                # the text prediction bounding box
                endX = int(offsetX + (cos * xData1[x]) + (sin * xData2[x]))
                endY = int(offsetY - (sin * xData1[x]) + (cos * xData2[x]))
                startX = int(endX - w)
                startY = int(endY - h)
                # add the bounding box coordinates and probability score to
                # our respective lists
                rects.append((startX, startY, endX, endY))
                confidences.append(scoresData[x])
                # apply non-maxima suppression to suppress weak, overlapping bounding
                # boxes
            boxes = non_max_suppression(np.array(rects), probs=confidences)
        print(confidences,boxes)
        # loop over the bounding boxes
        for (startX, startY, endX, endY) in boxes:
            # scale the bounding box coordinates based on the respective
            # ratios
            startX = startX
            startY = startY
            endX = endX
            endY = endY
            print(startX, startY, endX, endY)
            # draw the bounding box on the image
            cv2.rectangle(orig, (startX, startY), (endX, endY), (0, 255, 0), 1)
            # show the output image
        cv2.imwrite('D:/work/video/EAST/tmp/0424.jpg',orig)
        cv2.imshow("Text Detection", orig)
        cv2.waitKey(0)
        cv2.destroyAllWindows()

由于opencv加载和tf加载的输出是不一样的，所以预测之后的数据处理也是不一样的，但是都要用nms来找到最终的box。
用opencv加载时间上并没有明显的提速，个人认为是opencv dnn没有支持该框架加速，