人脸检测(SSD模型)

使用opencv 加载训练好的SSD模型

# 文件下载地址
# deploy.prototxt.txt：
#   https://github.com/opencv/opencv/tree/master/samples/dnn/face_detector
# res10_300x300_ssd_iter_140000.caffemodel：
#   https://github.com/Shiva486/facial_recognition/blob/master/res10_300x300_ssd_iter_140000.caffemodel

import cv2
import numpy as np


def cv_show(neme, img):
    cv2.namedWindow(neme, cv2.WINDOW_NORMAL)
    cv2.imshow(neme, img)
    cv2.waitKey(0)
    cv2.destroyAllWindows()


# 读取照片
img = cv2.imread('./images/faces2.jpg')
# 加载模型
face_detector = cv2.dnn.readNetFromCaffe('./weights/deploy.prototxt.txt',
                                         './weights/res10_300x300_ssd_iter_140000.caffemodel')

# 原图尺寸
img_height = img.shape[0]
img_width = img.shape[1]  # 缩放至模型输入尺寸
img_resize = cv2.resize(img, (500, 300))
# 图像转为blob
img_blob = cv2.dnn.blobFromImage(img_resize, 1.0, (500, 300), (104.0, 177.0, 123.0))
# 输入
face_detector.setInput(img_blob)
# 推理
detections = face_detector.forward()
# 查看检测人脸数量
num_of_detections = detections.shape[2]

# 原图复制，一会绘制用
img_copy = img.copy()

for index in range(num_of_detections):
    # 置信度
    detection_confidence = detections[0, 0, index, 2]
    # 挑选置信度
    if detection_confidence > 0.15:
        # 位置
        locations = detections[0, 0, index, 3:7] * np.array([img_width, img_height, img_width, img_height])
        # 打印
        print(detection_confidence * 100)

        lx, ly, rx, ry = locations.astype('int')
        # 绘制
        cv2.rectangle(img_copy, (lx, ly), (rx, ry), (0, 255, 0), 5)

cv_show('neme', img_copy)

最后

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