Opencv学习笔记(八):如何通过cv2读取视频和摄像头来进行人脸检测(jetson nano)

1.视频

通过视频每一帧的时间戳保存人脸图片
cap.get(cv2.CAP_PROP_POS_MSEC)/1000

vidio_path='/home/lqs/Documents/retinaface_lightweight.pytorch-master/1.mp4'
cap = cv2.VideoCapture(vidio_path) # open video
while True:
# read frame
    ret,frame=cap.read()
    if not ret: # if the camera over return false
        break
    start=time.time()
    boxes, landms, scores = detector(frame)
    if any(scores) and boxes[0][0]:
        end=time.time()
        time1 = round(end - start, 3)  # 保留一位小数 2为两位
        boxes = boxes.astype(np.int)
        landms = landms.astype(np.int)

        for i in range(len(boxes)):
            text = "{:.4f}".format(scores[i])
            faces=[boxes[i][0],boxes[i][1],boxes[i][2],boxes[i][3]]
            face_roi=align_process(frame,faces,landms[i],(112,112))
            a=round(cap.get(cv2.CAP_PROP_POS_MSEC)/1000,2) 
            cv2.imwrite(output1_path+str(a)+'.jpg',face_roi)
            cv2.rectangle(frame, (boxes[i][0], boxes[i][1]), (boxes[i][2], boxes[i][3]), (0, 0, 255), 2)
            cx = boxes[i][0]
            cy = boxes[i][1] + 12
            cv2.putText(frame, text, (cx, cy),
                        cv2.FONT_HERSHEY_DUPLEX, 0.5, (255, 255, 255))
            cv2.putText(frame, 'Cost: {}ms'.format(time1),
                                (10, 25), cv2.FONT_HERSHEY_COMPLEX_SMALL, 0.75, (0, 23, 255), 1)
            cv2.putText(frame,
                                'FPS: {:2.2f}'.format(1 / time1) if time1 > 0 else 'FPS: --',
                                (10, 50), cv2.FONT_HERSHEY_COMPLEX_SMALL, 0.75, (0, 23, 255), 1)
            # landms
            cv2.circle(frame, (landms[i][0][0], landms[i][0][1]), 1, (0, 0, 255), 4)
            cv2.circle(frame, (landms[i][1][0], landms[i][1][1]), 1, (0, 255, 255), 4)
            cv2.circle(frame, (landms[i][2][0], landms[i][2][1]), 1, (255, 0, 255), 4)
            cv2.circle(frame, (landms[i][3][0], landms[i][3][1]), 1, (0, 255, 0), 4)
            cv2.circle(frame, (landms[i][4][0], landms[i][4][1]), 1, (255, 0, 0), 4)
        cv2.imshow('video',frame)
        if ord('q')==cv2.waitKey(40):
            break
    else:
        print('no face')
        continue
# release resource
cv2.destroyAllWindows()
cap.release

----------------------------------------
# the offcial code
import cv2

def vidread(path):
    cap = cv2.VideoCapture(path)
    frames = []
    while(cap.isOpened()):
        ret, frame = cap.read()
        if ret:
            frames.append(frame)
        else:
            break
    cap.release()
    return frames

# save video
def vidwrite(path, frames):
    fourcc = cv2.VideoWriter_fourcc(*'MP4V')
    out = cv2.VideoWriter(path, fourcc, 30.0, (frames[0].shape[1], frames[0].shape[0]))
    for frame in frames:
        out.write(frame)
    out.release()

2.摄像头

通过CSI摄像头每一帧的系统时间保存图片

#import time 获取绝对时间年月日时分秒
#a=time.strftime('%Y%m%d%H%M%S', time.localtime())
#print(a)
import cv2,os,time
import argparse
import numpy as np
from align_faces import align_process
from retinaface import RetinaDetector
#设置gstreamer管道参数
def gstreamer_pipeline(
    capture_width=1280, #摄像头预捕获的图像宽度
    capture_height=720, #摄像头预捕获的图像高度
    display_width=1280, #窗口显示的图像宽度
    display_height=720, #窗口显示的图像高度
    framerate=60,       #捕获帧率
    flip_method=0,      #是否旋转图像
):
    return (
        "nvarguscamerasrc ! "
        "video/x-raw(memory:NVMM), "
        "width=(int)%d, height=(int)%d, "
        "format=(string)NV12, framerate=(fraction)%d/1 ! "
        "nvvidconv flip-method=%d ! "
        "video/x-raw, width=(int)%d, height=(int)%d, format=(string)BGRx ! "
        "videoconvert ! "
        "video/x-raw, format=(string)BGR ! appsink"
        % (
            capture_width,
            capture_height,
            framerate,
            flip_method,
            display_width,
            display_height,
        )
    )

if __name__ == "__main__":
    vidio_path='/home/z/Documents/retinaface_lightweight.pytorch-master/1.mp4'
    output_path = '/home/z/Documents/retinaface_lightweight.pytorch-master/retainmp4_result/'
    output1_path = '/home/z/Documents/retinaface_lightweight.pytorch-master/retainmp4_roi_result/'
    os.makedirs(os.path.dirname(output_path),exist_ok=True)
    os.makedirs(os.path.dirname(output1_path),exist_ok=True)

    capture_width = 1280
    capture_height = 720
    display_width = 1280
    display_height = 720
    framerate = 60
    flip_method = 0

    # 创建管道
    print(gstreamer_pipeline(capture_width,capture_height,display_width,display_height,framerate,flip_method))

    #管道与视频流绑定
    cap = cv2.VideoCapture(gstreamer_pipeline(flip_method=0), cv2.CAP_GSTREAMER)
    detector = RetinaDetector()
    if cap.isOpened():
        window_handle = cv2.namedWindow("CSI Camera", cv2.WINDOW_AUTOSIZE)

        # 逐帧显示
        while cv2.getWindowProperty("CSI Camera", 0) >= 0:
            ret_val, img = cap.read()
            if not ret_val: # if the camera over return false
                break
          # 图像太大需要调整
            height, width = img.shape[0:2]
            print("height=",height,"width=",width)
            if width > 800:
                new_width = 640
                new_height = int(new_width/width*height)
                img = cv2.resize(img, (new_width, new_height))
            print("new_height=",new_height,"new_width=",new_width)

            cv2.imshow("CSI Camera", img)
            #print("img.shape=",img.shape)
            keyCode = cv2.waitKey(30) & 0xFF         
            if keyCode == 27:# ESC键退出
                break
        #print("img.shape=",img.shape)
        #释放资源
        cap.release()
        cv2.destroyAllWindows()
    else:
        print("打开摄像头失败")