[雪峰磁针石博客]计算机视觉opcencv工具深度学习快速实战1人脸识别

使用OpenCV提供的预先训练的深度学习面部检测器模型，可快速，准确的进行人脸识别。

2017年8月OpenCV 3.3正式发布，带来了高改进的“深度神经网络”(dnn deep neural networks)模块。该模块支持许多深度学习框架，包括Caffe，TensorFlow和Torch / PyTorch。

基于Caffe的面部检测器在这里。

需要两组文件：

• 定义模型体系结构的.prototxt文件
• .caffemodel文件，包含实际图层的权重

权重文件不包含在OpenCV示例目录。

OpenCV深度学习面部检测器如何工作？

# 模型下载：https://itbooks.pipipan.com/fs/18113597-320346529 # 代码存放：https://github.com/china-testing/python-api-tesing/tree/master/opencv_crash_deep_learning # 技术支持qq群144081101(代码和模型存放） # USAGE # python detect_faces.py --image rooster.jpg --prototxt deploy.prototxt.txt --model res10_300x300_ssd_iter_140000.caffemodel # import the necessary packages import numpy as np import argparse import cv2 # construct the argument parse and parse the arguments ap = argparse.ArgumentParser() ap.add_argument("-i", "--image", required=True, help="path to input image") ap.add_argument("-p", "--prototxt", required=True, help="path to Caffe 'deploy' prototxt file") ap.add_argument("-m", "--model", required=True, help="path to Caffe pre-trained model") ap.add_argument("-c", "--confidence", type=float, default=0.5, help="minimum probability to filter weak detections") args = vars(ap.parse_args()) # load our serialized model from disk print("[INFO] loading model...") net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"]) # load the input image and construct an input blob for the image # by resizing to a fixed 300x300 pixels and then normalizing it image = cv2.imread(args["image"]) (h, w) = image.shape[:2] blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 1.0, (300, 300), (104.0, 177.0, 123.0)) # pass the blob through the network and obtain the detections and # predictions print("[INFO] computing object detections...") net.setInput(blob) detections = net.forward() # loop over the detections for i in range(0, detections.shape[2]): # extract the confidence (i.e., probability) associated with the # prediction confidence = detections[0, 0, i, 2] # filter out weak detections by ensuring the `confidence` is # greater than the minimum confidence if confidence > args["confidence"]: # compute the (x, y)-coordinates of the bounding box for the # object box = detections[0, 0, i, 3:7] * np.array([w, h, w, h]) (startX, startY, endX, endY) = box.astype("int") # draw the bounding box of the face along with the associated # probability text = "{:.2f}%".format(confidence * 100) y = startY - 10 if startY - 10 > 10 else startY + 10 cv2.rectangle(image, (startX, startY), (endX, endY), (0, 0, 255), 2) cv2.putText(image, text, (startX, y), cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2) # show the output image cv2.imshow("Output", image) cv2.waitKey(0)

执行：

$ python detect_faces.py --image rooster.jpg --prototxt deploy.prototxt.txt --model res10_300x300_ssd_iter_140000.caffemodel

上面的面部有74.30％的置信度。 尽管OpenCV的Haar级联因缺少“直接”角度的面孔，但通过使用OpenCV的深度学习面部探测器，依然能够测到脸部。

再来看三个面孔的示例：

python detect_faces.py --image iron_chic.jpg --prototxt deploy.prototxt.txt --model res10_300x300_ssd_iter_140000.caffemodel

视频，视频流和网络摄像头应用人脸检测

# USAGE # python detect_faces_video.py --prototxt deploy.prototxt.txt --model res10_300x300_ssd_iter_140000.caffemodel # import the necessary packages from imutils.video import VideoStream import numpy as np import argparse import imutils import time import cv2 # construct the argument parse and parse the arguments ap = argparse.ArgumentParser() ap.add_argument("-p", "--prototxt", required=True, help="path to Caffe 'deploy' prototxt file") ap.add_argument("-m", "--model", required=True, help="path to Caffe pre-trained model") ap.add_argument("-c", "--confidence", type=float, default=0.5, help="minimum probability to filter weak detections") args = vars(ap.parse_args()) # load our serialized model from disk print("[INFO] loading model...") net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"]) # initialize the video stream and allow the cammera sensor to warmup print("[INFO] starting video stream...") vs = VideoStream(src=0).start() time.sleep(2.0) # loop over the frames from the video stream while True: # grab the frame from the threaded video stream and resize it # to have a maximum width of 400 pixels frame = vs.read() frame = imutils.resize(frame, width=400) # grab the frame dimensions and convert it to a blob (h, w) = frame.shape[:2] blob = cv2.dnn.blobFromImage(cv2.resize(frame, (300, 300)), 1.0, (300, 300), (104.0, 177.0, 123.0)) # pass the blob through the network and obtain the detections and # predictions net.setInput(blob) detections = net.forward() # loop over the detections for i in range(0, detections.shape[2]): # extract the confidence (i.e., probability) associated with the # prediction confidence = detections[0, 0, i, 2] # filter out weak detections by ensuring the `confidence` is # greater than the minimum confidence if confidence < args["confidence"]: continue # compute the (x, y)-coordinates of the bounding box for the # object box = detections[0, 0, i, 3:7] * np.array([w, h, w, h]) (startX, startY, endX, endY) = box.astype("int") # draw the bounding box of the face along with the associated # probability text = "{:.2f}%".format(confidence * 100) y = startY - 10 if startY - 10 > 10 else startY + 10 cv2.rectangle(frame, (startX, startY), (endX, endY), (0, 0, 255), 2) cv2.putText(frame, text, (startX, y), cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2) # show the output frame cv2.imshow("Frame", frame) key = cv2.waitKey(1) & 0xFF # if the `q` key was pressed, break from the loop if key == ord("q"): break # do a bit of cleanup cv2.destroyAllWindows() vs.stop()

执行：

python detect_faces_video.py --prototxt deploy.prototxt.txt --model res10_300x300_ssd_iter_140000.caffemodel

参考资料

• 本文最新版本地址
• 本文涉及的python测试开发库 谢谢点赞！
• 本文相关海量书籍下载
• 2018最佳人工智能机器学习工具书及下载(持续更新)
• 模型下载：https://itbooks.pipipan.com/fs/18113597-320346529

其他python人脸识别库介绍

python库介绍-face_recognition 人脸识别

可以命令识别人脸框。

$ face_detection --model cnn iron_chic.jpg iron_chic.jpg,79,422,243,258 iron_chic.jpg,146,272,310,108 iron_chic.jpg,194,144,330,7