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stream_to_stdout.py
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stream_to_stdout.py
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# -*- coding: UTF-8 -*-
import numpy as np
import cv2
import sys
def deal_stream():
# cap = cv2.VideoCapture(0)
cap = cv2.VideoCapture("rtsp://admin:[email protected]/cam/realmonitor?channel=1&subtype=0")
# 打印视频相关参数,帧率,宽高
if cap.isOpened():
print cap.get(cv2.CAP_PROP_FPS)
print cap.get(cv2.CAP_PROP_FRAME_WIDTH)
print cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')
while cap.isOpened():
# Capture frame-by-frame
ret, frame = cap.read()
# cv2.imshow('get stream', frame)
# Our operations on the frame come here
# opencv读取的图片格式为bgr24 转为灰度图
frame_temp = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# 直方图均匀化(改善图像的对比度和亮度)
frame_temp = cv2.equalizeHist(frame_temp)
# 获取该图片中的各个人脸的坐标
faces = face_cascade.detectMultiScale(frame_temp, 1.3, 5)
# 人脸画框
for (x, y, w, h) in faces:
frame = cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)
roi_gray = frame[y:y + h, x:x + w]
roi_color = frame[y:y + h, x:x + w]
eyes = eye_cascade.detectMultiScale(roi_gray)
for (ex, ey, ew, eh) in eyes:
cv2.rectangle(roi_color, (ex, ey), (ex + ew, ey + eh), (0, 255, 0), 2)
# Display the resulting frame
# cv2.imshow('process stream', frame)
sys.stdout.write(frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# When everything done, release the capture
cap.release()
cv2.destroyAllWindows()
if __name__ == "__main__":
deal_stream()