代码收藏家 python篇—python读取rtsp流，并消耗（多种方式）
文章目录
python篇—python读取rtsp流，并消耗（多种方式） 1.python读取rtsp流，并消耗（用线程） 2.python读取rtsp流，并消耗（用进程） 3.python读取rtsp流，并消耗（普通） 4. 验证 本机 是否支持python rtsp 的GPU 加速 5. 代码：python rtsp 的GPU加速
python篇—python读取rtsp流，并消耗（多种方式）
1.python读取rtsp流，并消耗（用线程）
import os
import cv2
import gc
import time
import threading
import numpy as np
from PIL import Image
top = 100
stack = []
# 向共享缓冲栈中写入数据:
def write(stack, cam, top: int) -> None:
"""
:param cam: 摄像头参数
:param stack: list对象
:param top: 缓冲栈容量
:return: None
"""
print('Process to write: %s' % os.getpid())
cap = cv2.VideoCapture(cam)
while True:
_, img = cap.read()
if _:
stack.append(img)
print(stack)
# 每到一定容量清空一次缓冲栈
# 利用gc库，手动清理内存垃圾，防止内存溢出
if len(stack) >= top:
del stack[:]
gc.collect()
# 在缓冲栈中读取数据:
def read(stack) -> None:
print('Process to read: %s' % os.getpid())
# 开始时间
t1 = time.time()
# 图片计数
count = 0
while True:
if len(stack) != 0:
# 开始图片消耗
print("stack的长度", len(stack))
if len(stack) != 100 and len(stack) != 0:
value = stack.pop()
else:
pass
if len(stack) >= top:
del stack[:]
gc.collect()
# 格式转变，BGRtoRGB
frame = cv2.cvtColor(value, cv2.COLOR_BGR2RGB)
# # 转变成Image
frame = Image.fromarray(np.uint8(frame))
print("*" * 100)
count += 1
print("数量为：", count)
t2 = time.time()
print("时间差：", int(t2 - t1))
if int(t2 - t1) == 600:
# 记录 消耗的图片数量
with open('count.txt', 'ab') as f:
f.write(str(count).encode() + "\n".encode())
f.flush()
# count = 0 # 不要置零，计算总数
t1 = t2
if __name__ == '__main__':
for i in range(1):
thread_pro = threading.Thread(target=write, args=(stack, "rtsp://admin:xxxx@123@192.168.0.65:554/Streaming/Channels/1 ", top,))
thread_pro.start()
for j in range(3):
thread_con = threading.Thread(target=read, args=(stack,))
thread_con.start()
2.python读取rtsp流，并消耗（用进程）
注：该代码适合在本机跑，不适合在jetson的小盒子上运行
import os
import cv2
import gc
import time
import numpy as np
from PIL import Image
import multiprocessing
from multiprocessing import Process, Manager
top = 100
# 向共享缓冲栈中写入数据:
def write(stack, cam, top: int) -> None:
"""
:param cam: 摄像头参数
:param stack: Manager.list对象
:param top: 缓冲栈容量
:return: None
"""
print('Process to write: %s' % os.getpid())
cap = cv2.VideoCapture(cam)
while True:
_, img = cap.read()
if _:
stack.append(img)
# 每到一定容量清空一次缓冲栈
# 利用gc库，手动清理内存垃圾，防止内存溢出
if len(stack) >= top:
del stack[:]
gc.collect()
# 在缓冲栈中读取数据:
def read(stack) -> None:
print('Process to read: %s' % os.getpid())
# 开始时间
t1 = time.time()
# 图片计数
count = 0
while True:
if len(stack) != 0:
# 开始图片消耗
print("stack的长度", len(stack))
if len(stack) != 100 and len(stack) != 0:
value = stack.pop()
else:
pass
if len(stack) >= top:
del stack[:]
gc.collect()
# 格式转变，BGRtoRGB
frame = cv2.cvtColor(value, cv2.COLOR_BGR2RGB)
# # 转变成Image
frame = Image.fromarray(np.uint8(frame))
print("*" * 100)
count += 1
print("数量为：", count)
t2 = time.time()
print("时间差：", int(t2 - t1))
if int(t2 - t1) == 600:
# 记录 消耗的图片数量
with open('count.txt', 'ab') as f:
f.write(str(count).encode() + "\n".encode())
f.flush()
# count = 0 # 不要置零，计算总数
t1 = t2
if __name__ == '__main__':
multiprocessing.set_start_method('forkserver', force=True)
# 父进程创建缓冲栈，并传给各个子进程：
q = Manager().list()
pw = Process(target=write, args=(q, "rtsp://admin:xxxx@123@192.168.0.65:554/Streaming/Channels/1", top))
pr = Process(target=read, args=(q,))
# 启动子进程pw，写入:
pw.start()
# 启动子进程pr，读取:
pr.start()
# 等待pr结束:
pr.join()
# pw进程里是死循环，无法等待其结束，只能强行终止:
pw.terminate()
3.python读取rtsp流，并消耗（普通）
import os
import cv2
import gc
import time
import numpy as np
from PIL import Image
top = 100
stack = []
# 向共享缓冲栈中写入数据:
def write(stack, cam, top: int) -> None:
"""
:param cam: 摄像头参数
:param stack: list对象
:param top: 缓冲栈容量
:return: None
"""
print('Process to write: %s' % os.getpid())
cap = cv2.VideoCapture(cam)
# 开始时间
t1 = time.time()
# 图片计数
count = 0
while True:
_, img = cap.read()
if _:
stack.append(img)
# print(stack)
# 在缓冲栈中读取数据:
if len(stack) != 0:
# 开始图片消耗
print("stack的长度", len(stack))
if len(stack) != 100 and len(stack) != 0:
value = stack.pop()
else:
pass
# 格式转变，BGRtoRGB
frame = cv2.cvtColor(value, cv2.COLOR_BGR2RGB)
# # 转变成Image
frame = Image.fromarray(np.uint8(frame))
print("*" * 100)
count += 1
print("数量为：", count)
t2 = time.time()
print("时间差：", int(t2 - t1))
if int(t2 - t1) == 600:
# 记录 消耗的图片数量
with open('count.txt', 'ab') as f:
f.write(str(count).encode() + "\n".encode())
f.flush()
t1 = t2
# 每到一定容量清空一次缓冲栈
# 利用gc库，手动清理内存垃圾，防止内存溢出
if len(stack) >= top:
del stack[:]
gc.collect()
if __name__ == '__main__':
write(stack, "rtsp://admin:xxxx@123@192.168.0.65:554/Streaming/Channels/1", top)
4. 验证 本机 是否支持python rtsp 的GPU 加速
安装以下命令
pip install opencv-contrib-python
print(cv2.getBuildInformation())
5. 代码：python rtsp 的GPU加速
该代码还有问题，后续继续更新
import cv2
pipeline = "rtspsrc location=\"rtsp://login:password@host:port/\" ! rtph264depay ! h264parse ! omxh264dec ! nvvidconv ! video/x-raw, format=(string)BGRx! videoconvert ! appsink"
capture = cv2.VideoCapture(pipeline, cv2.CAP_GSTREAMER)
while capture.isOpened():
res, frame = capture.read()
cv2.imshow("Video", frame)
key = cv2.waitKey(1) & 0xFF
if key == ord("q"):
break
capture.release()
cv2.destroyAllWindows()
来源：心惠天意
