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?
图片. PNG
- # 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
图片. PNG
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
图片. PNG
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
deep_learning_face_detection_opencv.gif
参考资料
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2018 最佳人工智能机器学习工具书及下载 (持续更新)
来源: http://www.jianshu.com/p/73d154b22a64