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@[email protected]: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@[email protected]: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@[email protected]: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
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print(cv2.getBuildInformation())
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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()