使用OpenCV提供的預先訓練的深度學習面部檢測器模型，可快速，準確的進行人臉識別。

2017年8月OpenCV 3.3正式發布，帶來了高改進的“深度神經網絡”(dnn deep neural networks)模塊。該模塊支持許多深度學習框架，包括Caffe，TensorFlow和Torch / PyTorch。

基于Caffe的面部檢測器在這里。

需要兩組文件：

• 定義模型體系結構的.prototxt文件
• .caffemodel文件，包含實際圖層的權重

權重文件不包含在OpenCV示例目錄。

OpenCV深度學習面部檢測器如何工作？

# 模型下載：https://itbooks.pipipan.com/fs/18113597-320346529 # 代碼存放：https://github.com/china-testing/python-api-tesing/tree/master/opencv_crash_deep_learning # 技術支持qq群144081101(代碼和模型存放） # 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# predictionconfidence = detections[0, 0, i, 2]# filter out weak detections by ensuring the `confidence` is# greater than the minimum confidenceif confidence > args["confidence"]:# compute the (x, y)-coordinates of the bounding box for the# objectbox = 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# probabilitytext = "{:.2f}%".format(confidence * 100)y = startY - 10 if startY - 10 > 10 else startY + 10cv2.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)

執行：

$ 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。 盡管OpenCV的Haar級聯因缺少“直接”角度的面孔，但通過使用OpenCV的深度學習面部探測器，依然能夠測到臉部。

再來看三個面孔的示例：

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

視頻，視頻流和網絡攝像頭應用人臉檢測

# 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 pixelsframe = 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# predictionsnet.setInput(blob)detections = net.forward()# loop over the detectionsfor i in range(0, detections.shape[2]):# extract the confidence (i.e., probability) associated with the# predictionconfidence = detections[0, 0, i, 2]# filter out weak detections by ensuring the `confidence` is# greater than the minimum confidenceif confidence < args["confidence"]:continue# compute the (x, y)-coordinates of the bounding box for the# objectbox = 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# probabilitytext = "{:.2f}%".format(confidence * 100)y = startY - 10 if startY - 10 > 10 else startY + 10cv2.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 framecv2.imshow("Frame", frame)key = cv2.waitKey(1) & 0xFF# if the `q` key was pressed, break from the loopif key == ord("q"):break# do a bit of cleanup cv2.destroyAllWindows() vs.stop()

執行：

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

參考資料

• 本文最新版本地址
• 本文涉及的python測試開發庫 謝謝點贊！
• 本文相關海量書籍下載
• 2018最佳人工智能機器學習工具書及下載(持續更新)
• 模型下載：https://itbooks.pipipan.com/fs/18113597-320346529

其他python人臉識別庫介紹

python庫介紹-face_recognition 人臉識別

可以命令識別人臉框。

$ 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

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