时间戳

首页 > 解决方案 > 如何测试 OpenCV DNN 模块的准确性？它不能预测 YOLOv3 的正确检测。而暗网检测器可以正确检测

opencv - 如何测试 OpenCV DNN 模块的准确性？它不能预测 YOLOv3 的正确检测。而暗网检测器可以正确检测

问题描述

OpenCV DNN 模块无法预测 YOLOv3 的正确检测。而暗网检测器检测正确。

系统信息（版本）

我使用从源代码编译的 OpenCV 测试结果，我也尝试使用预构建的 opencv-python，但 OpenCV DNN 检测到错误的对象。

而暗网检测器检测正确。

使用暗网检测器正确检测：

OpenCV DNN 模块的错误检测：

YOLOv3 网络和模型权重来自https://github.com/AlexeyAB/darknet

详细说明

请在下面附加的链接中查看输出图像。（使用暗网检测器进行正确检测）与错误检测（使用OpenCV DNN进行比较）

此Google Drive 链接中提供的输出图像。

上面的链接包括测试图像也用于测试步骤

    # The following code is partial to demonstrate steps 
    net = cv.dnn.readNetFromDarknet(modelConfiguration, modelWeights)
    layerNames = net.getLayerNames()
    layerNames = [layerNames[i[0] - 1] for i in net.getUnconnectedOutLayers()]
    
    # construct a blob from the input frame and then perform a forward pass of the YOLO object detector, 
    # giving us our bounding boxes and associated probabilities
    blob = cv2.dnn.blobFromImage(frame, 1 / 255.0, (416, 416),
        swapRB=True, crop=False)
    net.setInput(blob)
    layerOutputs = net.forward(layerNames)
    
    # initialize our lists of detected bounding boxes, confidences,
    # and class IDs, respectively
    boxes = []
    confidences = []
    classIDs = []
 
    # loop over each of the layer outputs
    for output in layerOutputs:
        # loop over each of the detections
        for detection in output:
            # extract the class ID and confidence (i.e., probability)
            # of the current object detection
            scores = detection[5:]
            classID = np.argmax(scores)
            confidence = scores[classID]
 
            # filter out weak predictions by ensuring the detected
            # probability is greater than the minimum probability
            if confidence > args["confidence"]:
                # scale the bounding box coordinates back relative to
                # the size of the image, keeping in mind that YOLO
                # actually returns the center (x, y)-coordinates of
                # the bounding box followed by the boxes' width and
                # height
                box = detection[0:4] * np.array([W, H, W, H])
                (centerX, centerY, width, height) = box.astype("int")
 
                # use the center (x, y)-coordinates to derive the top
                # and and left corner of the bounding box
                x = int(centerX - (width / 2))
                y = int(centerY - (height / 2))
 
                # update our list of bounding box coordinates,
                # confidences, and class IDs
                boxes.append([x, y, int(width), int(height)])
                confidences.append(float(confidence))
                classIDs.append(classID)
                
 
    # apply non-maxima suppression to suppress weak, overlapping
    # bounding boxes
    idxs = cv2.dnn.NMSBoxes(boxes, confidences, args["confidence"], args["threshold"])
    
    dets = []
    if len(idxs) > 0:
        # loop over the indexes we are keeping
        for i in idxs.flatten():
            (x, y) = (boxes[i][0], boxes[i][1])
            (w, h) = (boxes[i][2], boxes[i][3])
            dets.append([x, y, x+w, y+h, confidences[i]])
    
    if len(boxes) > 0:
        i = int(0)
        for box in boxes:
            # extract the bounding box coordinates
            (x, y) = (int(box[0]), int(box[1]))
            (w, h) = (int(box[2]), int(box[3]))
 
            # draw a bounding box rectangle and label on the image
            # color = [int(c) for c in COLORS[classIDs[i]]]
            # cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
            color = [int(c) for c in COLORS[indexIDs[i] % len(COLORS)]]
            cv2.rectangle(frame, (x, y), (w, h), color, 2)
     
            cv2.putText(frame, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.75, color, 2)# 1.0 0.5, color, 2)
            i += 1
 
    cv2.imwrite("detection-output.jpg", frame)

标签： opencvmachine-learningneural-networkyolo

解决方案

我认为你的检测是正确的，因为你所有的标签都是汽车，问题是你在这一行的文字：

cv2.putText(frame, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.75, color, 2)

您应该将类​​名放在文本中，但我找不到text定义的位置。你的代码应该是这样的：

cv2.putText(frame, classes[class_ids[index]], (x + 5, y + 20), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, colors,2)

但根据我的经验，darknet 比 opencv dnn 有更好的检测。

推荐阅读