[雪峰磁針石部落格]計算機視覺opcencv工具深度學習快速實戰1人臉識別

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正式釋出，帶來了高改進的“深度神經網路”(dnn deep neural networks)模組。該模組支援許多深度學習框架，包括Caffe，TensorFlow和Torch / PyTorch。

基於Caffe的面部檢測器在這裡。

需要兩組檔案：

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

權重檔案不包含在OpenCV示例目錄。

OpenCV深度學習面部檢測器如何工作？

圖片.png

# 模型下載：https://itbooks.pipipan.com/fs/18113597-320346529
# 程式碼存放：https://github.com/china-testing/python-api-tesing/tree/master/opencv_crash_deep_learning
# 技術支援qq群144081101(程式碼和模型存放）
# 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
    # 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)

執行：

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

圖片.png

上面的面部有74.30％的置信度。儘管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

圖片.png

視訊，視訊流和網路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 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()

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
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本文相關海量書籍下載
2018最佳人工智慧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

[雪峰磁針石部落格]計算機視覺opcencv工具深度學習快速實戰1人臉識別

OpenCV深度學習面部檢測器如何工作？

視訊，視訊流和網路攝像頭應用人臉檢測

參考資料

其他python人臉識別庫介紹

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