如何从0开始学习opencv并完成类似于人脸检测的设置？

5. saebyn / munkres-cppGithub

6. Munkres-opencv

PS: 请记住在阅读示例时阅读API参考，慢慢向外扩展opencv 人脸检测，并尝试尽快编写自己的程序和反复试验.

发布于2016-01-10

python作业毕业设计书搜索下载

18个人同意答案

opencv 3.3+具有内置的人脸识别功能.

您可以轻松完成相关的毕业设计.

计算机视觉opcencv工具中的相关代码深度学习快速战斗1人脸识别

参考:

雪峰电磁针: 计算机视觉opcencv工具深度学习快速战斗1人脸识别zhuanlan.zhihu.com 2018最佳人工智能图像处理工具OpenCV图书下载China- testing.github.io（a: 1: b: 9: 3: 8: f: b: e: f: 3: f: 9: e: 8: 7: 6: 4: 3: 7: 9: 1: 7: c: 4: 7: 7: 6: 2: 2: d: a: d}

使用OpenCV提供的经过预先训练的深度学习人脸检测器模型，可以快速而准确地执行人脸识别. 2018最佳人工智能图像处理工具OpenCV书籍下载使用OpenCV提供的经过预先训练的深度学习人脸检测器模型可以快速准确地执行人脸识别.

OpenCV 3.3于2017年8月正式发布，带来了高度改进的“ dnn深度神经网络”模块. 该模块支持许多深度学习框架，包括Caffeopencv 人脸检测，TensorFlow和Torch / PyTorch.

基于咖啡的面部检测器在这里.

需要两套文件:

重量文件不包含在OpenCV示例目录中.

# 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630011153 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

上图的置信度为74.30％. 尽管OpenCV的Haar级联缺少“直角”直角人脸，但它仍可以使用OpenCV的深度学习人脸检测器来检测人脸.

看三个面孔的例子:

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

# 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

Python库-face_recognition人脸识别

简介

您可以命令识别脸部框架.

$ 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

欢迎喜欢并关注:

雪峰电磁针（6: 6: 4: 1: 8: e: 4: 1: e: 6: d: 9: 4: 9: b: 0: a: 1: 3: 9: b: 9 : 1: e: f: f: d: b: d: e: 3: 8}

谢谢！

于2018-12-05编辑

本文来自电脑杂谈，转载请注明本文网址：
http://www.pc-fly.com/a/jisuanjixue/article-148038-1.html