关于OpenCV for Python入门-DNN模块+MobileNet-SSD实现人脸检测

import cv2 as cv

# 模型路径
model_bin = r"C:\Python\Pycharm\docxprocess\face_detector\ssd\MobileNetSSD_deploy.caffemodel"
config_text = r"C:\Python\Pycharm\docxprocess\face_detector\ssd\MobileNetSSD_deploy.prototxt"
# 类别信息
objName = ["background",
           "aeroplane", "bicycle", "bird", "boat",
           "bottle", "bus", "car", "cat", "chair",
           "cow", "diningtable", "dog", "horse",
           "motorbike", "person", "pottedplant",
           "sheep", "sofa", "train", "tvmonitor"]

# 加载模型
net = cv.dnn.readNetFromCaffe(config_text, model_bin)
# 读取测试图片
imgname = r'C:\Python\Pycharm\docxprocess\picture\other\renwu\juhui1.jpg'
# imgname = r'C:\Python\Pycharm\docxprocess\picture\other\renwu\juhui2.png'
# imgname = r'C:\Python\Pycharm\docxprocess\picture\other\renwu\juhui3.jpg'
# imgname = r'C:\Python\Pycharm\docxprocess\picture\other\renwu\juhui4.png'
# imgname = r'C:\Python\Pycharm\docxprocess\picture\other\renwu\wukelan.jpeg'
confThreshold = 0.5
image = cv.imread(imgname)
h = image.shape[0]
w = image.shape[1]

# 获得所有层名称与索引
layerNames = net.getLayerNames()
lastLayerId = net.getLayerId(layerNames[-1])
lastLayer = net.getLayer(lastLayerId)

# 检测
blobImage = cv.dnn.blobFromImage(image, 0.007843, (300, 300), (127.5, 127.5, 127.5), True, False)
net.setInput(blobImage)
cvOut = net.forward()

for detection in cvOut[0, 0, :, :]:
    score = float(detection[2])
    objIndex = int(detection[1])
    if score > confThreshold and objName[objIndex]=='person':
        left = detection[3] * w
        top = detection[4] * h
        right = detection[5] * w
        bottom = detection[6] * h

        # 绘制
        cv.rectangle(image, (int(left), int(top)), (int(right), int(bottom)), (255, 0, 0), thickness=2)
        cv.putText(image, "score:%.2f, %s" % (score, objName[objIndex]),
                   (int(left) - 10, int(top) - 5), cv.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2, 8);

#  显示
cv.imshow('mobilenet-ssd-demo', image)
cv.waitKey(0)
cv.destroyAllWindows()