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pickle error with multiprocessing and cv2

I'm trying to implement a code that takes a video and divides it into frames while also filtering the frames and saving them to a different directory.
I am running into an error that says:
TypeError: cannot pickle 'cv2.VideoCapture' object
I have tried to understand why this problem occurs but I'm still unsure why.
here is my code:
import cv2
import os
import time
import matplotlib.image as pltim
from multiprocessing import Process, Lock
import matplotlib.pyplot as plt
def saveFramesUnfiltered(vid, lock):
currentFrame = 0 # counter for frames (to organize them by order)
while True:
success, frame = vid.read()
lock.acquire()
cv2.imwrite("./framesBefore/frame" + str(currentFrame) + '.jpg',
frame) # save unfiltered frame to folder and show the video (using the frames)
lock.release()
currentFrame = currentFrame + 1
if cv2.waitKey(1) & 0xFF == ord('q'):
break # end loop when finished
time.sleep(0.01)
def saveFramesFiltered(lock):
currentFrame = 0
framemax = 215
while currentFrame < framemax:
if os.path.exists("framesBefore/frame" + str(currentFrame) + '.jpg'):
lock.acquire()
image = pltim.imread("./framesBefore/frame" + str(currentFrame) + '.jpg')
lock.release()
r, g, b = image[:, :, 0], image[:, :, 1], image[:, :, 2]
grayImage = 0.299 * r + 0.587 * g + 0.114 * b
plt.plot(grayImage, cmap="gray")
plt.axis("off")
lock.acquire()
plt.savefig("./framesAfter/grayImage" + str(currentFrame) + ".jpg", bbox_inches='tight', pad_inches=0)
lock.release()
time.sleep(0.01)
def main():
if not os.path.exists('framesBefore'):
os.makedirs('framesBefore') # create a folder for the unfiltered frames
if not os.path.exists('framesAfter'):
os.makedirs('framesAfter') # create a folder for the filtered frames
lock = Lock()
vid = cv2.VideoCapture("maxwell cat.mp4") # getting the video
unfiltered_process = Process(target=saveFramesUnfiltered, args=(vid, lock))
filtered_process = Process(target=saveFramesFiltered, args=lock)
unfiltered_process.start()
filtered_process.start()
unfiltered_process.join()
filtered_process.join()
vid.release()
cv2.destroyAllWindows() # clear memory
if __name__ == '__main__':
main()
I am also new at using threads in python and in general so I would like to know if the way I implemented it is correct.
thanks!

Python multiprocessing queue using a lot of resources with opencv

I am using multiprocessing to get frames of a video using Opencv in python.
My class looks like this :-
import cv2
from multiprocessing import Process, Queue
class StreamVideos:
def __init__(self):
self.image_data = Queue()
def start_proces(self):
p = Process(target=self.echo)
p.start()
def echo(self):
cap = cv2.VideoCapture('videoplayback.mp4')
while cap.isOpened():
ret,frame = cap.read()
self.image_data.put(frame)
# print("frame")
I start the process "echo" using :-
p = Process(target=self.echo)
p.start()
the echo function looks like this :-
def echo(self):
cap = cv2.VideoCapture('videoplayback.mp4')
while cap.isOpened():
ret,frame = cap.read()
self.image_data.put(frame)
in which i am using queue where i put these frames
self.image_data.put(frame)
and then in another process I start reviving these frames
self.obj = StreamVideos()
def start_process(self):
self.obj.start_proces()
p = Process(target=self.stream_videos)
p.start()
def stream_videos(self):
while True:
self.img = self.obj.image_data.get()
print(self.img)
but as soon as I start putting frames to queue, the ram gets filled very quickly and the system gets stuck. The video I am using is just 25 fps and 39mb in size, so it does not make any sense.
One thing I noticed is that the "echo" process is putting a lot of frames in the queue before the "stream_videos" process retrives it.
What could be the root of this problem?
Thanks in advance.
Expectations: -
Able to retrieve the frames continuosly.
Tried :-
Not putting frames in queue, in which case the ram is not filled.

The following is a general purpose single producer/multiple consumer implementation. The producer (class StreamVideos) creates a shared memory array whose size is the number of bytes in the video frame. One or more consumers (you specify the number of consumers to StreamVideos) can then call StreamVideos.get_next_frame() to retrieve the next frame. This method converts the shared array back into a numpy array for subsequent processing. The producer will only read the next frame into the shared array after all consumers have called get_next_frame:
#!/usr/bin/env python3
import multiprocessing
import numpy as np
import ctypes
import cv2
class StreamVideos:
def __init__(self, path, n_consumers):
"""
path is the path to the video:
n_consumers is the number of tasks to which we will be sreaming this.
"""
self._path = path
self._event = multiprocessing.Event()
self._barrier = multiprocessing.Barrier(n_consumers + 1, self._reset_event)
# Discover how large a framesize is by getting the first frame
cap = cv2.VideoCapture(self._path)
ret, frame = cap.read()
if ret:
self._shape = frame.shape
frame_size = self._shape[0] * self._shape[1] * self._shape[2]
self._arr = multiprocessing.RawArray(ctypes.c_ubyte, frame_size)
else:
self._arr = None
cap.release()
def _reset_event(self):
self._event.clear()
def start_streaming(self):
cap = cv2.VideoCapture(self._path)
while True:
self._barrier.wait()
ret, frame = cap.read()
if not ret:
# No more readable frames:
break
# Store frame into shared array:
temp = np.frombuffer(self._arr, dtype=frame.dtype)
temp[:] = frame.flatten(order='C')
self._event.set()
cap.release()
self._arr = None
self._event.set()
def get_next_frame(self):
# Tell producer that this consumer is through with the previous frame:
self._barrier.wait()
# Wait for next frame to be read by the producer:
self._event.wait()
if self._arr is None:
return None
# Return shared array as a numpy array:
return np.ctypeslib.as_array(self._arr).reshape(self._shape)
def consumer(producer, id):
frame_name = f'Frame - {id}'
while True:
frame = producer.get_next_frame()
if frame is None:
break
cv2.imshow(frame_name, frame)
cv2.waitKey(1)
cv2.destroyAllWindows()
def main():
producer = StreamVideos('videoplayback.mp4', 2)
consumer1 = multiprocessing.Process(target=consumer, args=(producer, 1))
consumer1.start()
consumer2 = multiprocessing.Process(target=consumer, args=(producer, 2))
consumer2.start()
"""
# Run as a child process:
producer_process = multiprocessing.Process(target=producer.start_streaming)
producer_process.start()
producer_process.join()
"""
# Run in main process:
producer.start_streaming()
consumer1.join()
consumer2.join()
if __name__ == '__main__':
main()

how to run multiple camera in threading using python

below is the code i used to play multiple videos in parallel using multi threading pool. but only one video is playing for each input. i want each video to open separately. not combined
import concurrent.futures
RTSP_URL = "rtsp://wowzaec2demo.streamlock.net/vod/mp4:BigBuckBunny_115k.mp4"
RTSP_List = [RTSP_URL, RTSP_URL, RTSP_URL, RTSP_URL]
def url_to_video(url):
video = cv2.VideoCapture(url)
while True:
_, frame = video.read()
cv2.imshow("RTSP", frame)
k = cv2.waitKey(1)
if k == ord('q'):
break
video.release()
cv2.destroyAllWindows()
while True:
with concurrent.futures.ThreadPoolExecutor() as executor:
executor.map(url_to_video, RTSP_List)```
how to play each video separately.

you just need each thread to use a different name for the window in cv2.imshow, so that each thread will generate a different window, and you should place them somewhere distinct so that they aren't appearing one over the other, i just added in index to them so that each distinct index will have a position on screen and different title, also you shouldn't destroy all windows when one is done ...
import concurrent.futures
import cv2
RTSP_URL = "rtsp://wowzaec2demo.streamlock.net/vod/mp4:BigBuckBunny_115k.mp4"
RTSP_List = [(RTSP_URL,0), (RTSP_URL,1), (RTSP_URL,2), (RTSP_URL,3)]
def url_to_video(tup):
url,index = tup
video = cv2.VideoCapture(url)
while True:
_, frame = video.read()
cv2.imshow(f"RTSP {index}", frame)
cv2.moveWindow(f"RTSP {index}", index*300, 0)
k = cv2.waitKey(1)
if k == ord('q'):
break
video.release()
while True:
with concurrent.futures.ThreadPoolExecutor() as executor:
executor.map(url_to_video, RTSP_List)
cv2.destroyAllWindows()

process.join() hangs when using Queue based multi process

The following code is intended to create a process having images data til the process finish. Also these processes are intended to be terminated after the input queue q becomes empty.
import multiprocessing
import numpy as np
def run_mp(images, f_work, n_worker):
q = multiprocessing.Queue()
for img_idx in range(len(images)):
q.put(img_idx)
result_q = multiprocessing.Queue()
def f_worker(worker_index, data, q, result_q):
print("worker {} started".format(worker_index))
while not q.empty():
image_idx = q.get(timeout=1)
print('processing image idx {}'.format(image_idx))
image_out = f_work(data, image_idx)
result_q.put((image_out, image_idx))
print("worker {} finished".format(worker_index))
return
processes = list()
for i in range(n_worker):
process = multiprocessing.Process(target=f_worker, args=(i, images, q, result_q))
process.daemon = True
process.start()
processes.append(process)
for process in processes:
process.join()
images = [np.random.randn(100, 100) for _ in range(20)]
f = lambda image_list, idx: image_list[idx] + np.random.randn()
run_mp(images, f, 2)
After running code, from the embedded print functions, I confirmed that all 20 images are processed and two process were terminated. However, the programs hangs. When I hit ctrl-c I got the following message, from which hang occurs at os.waitpid.
How can I fix this problem?
worker 0 started
processing image idx 0
processing image idx 1
worker 1 started
processing image idx 2
processing image idx 3
processing image idx 4
processing image idx 5
processing image idx 6
processing image idx 7
processing image idx 8
processing image idx 9
processing image idx 10
processing image idx 11
processing image idx 12
processing image idx 13
processing image idx 14
processing image idx 15
processing image idx 16
processing image idx 17
processing image idx 18
processing image idx 19
worker 1 finished
^CTraceback (most recent call last):
File "example_queue.py", line 35, in <module>
run_mp(images, f, 2)
File "example_queue.py", line 29, in run_mp
process.join()
File "/usr/lib/python3.8/multiprocessing/process.py", line 149, in join
res = self._popen.wait(timeout)
File "/usr/lib/python3.8/multiprocessing/popen_fork.py", line 47, in wait
return self.poll(os.WNOHANG if timeout == 0.0 else 0)
File "/usr/lib/python3.8/multiprocessing/popen_fork.py", line 27, in poll
pid, sts = os.waitpid(self.pid, flag)
KeyboardInterrupt

First, be aware that your code will only work on platforms that use OS fork to create new processes because:
The code that creates new processes is not contained within a if __name__ == '__main__': block.
Your worker function, f_worker, is not at global scope.
If you read the documentation on multiprocessing.Queue, you will find that the call to q.empty() is totally unreliable and, fortunately, unnecessary because there is a solution that makes this call unnecessary. Also, if you are interested in the actual results that are being put to the result_q, be aware that you must get those results in the main process before the main process attempts to join the subprocesses that have done the putting to the queue. The documentation explains that, too.
The solution that avoids having to use the undependable call to q.empty() is to write to the "task queue", q in this case, following the actually items that need to be processed by your subprocesses, N sentinel items that cannot be mistaken for actual items to be processed but are instead special signals to the subprocesses that there are no more items to be processed. N is, of course, just the number of subprocesses that are reading from the task queue so as each subprocess reads a sentinel item it terminates and there are enough sentinel items to signal each subprocess. In this case None serves perfectly as a suitable sentinel item:
import multiprocessing
import numpy as np
def run_mp(images, f_work, n_worker):
def f_worker(worker_index, data, q, result_q):
print("worker {} started".format(worker_index))
while True:
image_idx = q.get() # Blocking get
if image_idx is None: # Sentinel?
break # We are done!
print('processing image idx {}'.format(image_idx))
image_out = f_work(data, image_idx)
result_q.put((image_out, image_idx))
print("worker {} finished".format(worker_index))
return
q = multiprocessing.Queue()
for img_idx in range(len(images)):
q.put(img_idx)
# Add sentinels:
for _ in range(n_worker):
q.put(None)
result_q = multiprocessing.Queue()
processes = list()
for i in range(n_worker):
process = multiprocessing.Process(target=f_worker, args=(i, images, q, result_q))
# We do not need daemon processes now:
#process.daemon = True
process.start()
processes.append(process)
# If we are interested in the results, we must process the result queue
# before joining the processes. We are expecting 20 results, so:
results = [result_q.get() for _ in range(20)]
print(results)
for process in processes:
process.join()
images = [np.random.randn(100, 100) for _ in range(20)]
f = lambda image_list, idx: image_list[idx] + np.random.randn()
run_mp(images, f, 2)

OpenCV / Python : multi-threading for live facial recognition

I'm using OpenCv and Dlib to execute facial recognition w/ landmarks, live from the webcam stream. The language is Python. It works fine on my macbook laptop, but I need it to run from a desktop computer 24/7. The computer is a PC Intel® Core™2 Quad CPU Q6600 # 2.40GHz 32bit running Debian Jessie. The drop in performance is drastic : there is a 10 seconds delay due to processing !
I therefore looked into multi-threading to gain performance :
I first tried the sample code by OpenCv, and the result is great! All four cores hit 100%, and the performance is much better.
I then replaced the frame processing code with my code, and it doesn't improve performance at all ! Only one core hits the 100%, the other ones stay very low. I even think it's worse with multi-threading on.
I got the facial landmark code from the dlib sample code. I know it can probably be optimized, but I want to understand why am I not able to use my (old) computer's full power with multi-threading ?
I'll drop my code below, thanks a lot for reading :)
from __future__ import print_function
import numpy as np
import cv2
import dlib
from multiprocessing.pool import ThreadPool
from collections import deque
from common import clock, draw_str, StatValue
import video
class DummyTask:
def __init__(self, data):
self.data = data
def ready(self):
return True
def get(self):
return self.data
if __name__ == '__main__':
import sys
print(__doc__)
try:
fn = sys.argv[1]
except:
fn = 0
cap = video.create_capture(fn)
#Face detector
detector = dlib.get_frontal_face_detector()
#Landmarks shape predictor
predictor = dlib.shape_predictor("landmarks/shape_predictor_68_face_landmarks.dat")
# This is where the facial detection takes place
def process_frame(frame, t0, detector, predictor):
# some intensive computation...
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
clahe_image = clahe.apply(gray)
detections = detector(clahe_image, 1)
for k,d in enumerate(detections):
shape = predictor(clahe_image, d)
for i in range(1,68): #There are 68 landmark points on each face
cv2.circle(frame, (shape.part(i).x, shape.part(i).y), 1, (0,0,255), thickness=2)
return frame, t0
threadn = cv2.getNumberOfCPUs()
pool = ThreadPool(processes = threadn)
pending = deque()
threaded_mode = True
latency = StatValue()
frame_interval = StatValue()
last_frame_time = clock()
while True:
while len(pending) > 0 and pending[0].ready():
res, t0 = pending.popleft().get()
latency.update(clock() - t0)
draw_str(res, (20, 20), "threaded : " + str(threaded_mode))
draw_str(res, (20, 40), "latency : %.1f ms" % (latency.value*1000))
draw_str(res, (20, 60), "frame interval : %.1f ms" % (frame_interval.value*1000))
cv2.imshow('threaded video', res)
if len(pending) < threadn:
ret, frame = cap.read()
t = clock()
frame_interval.update(t - last_frame_time)
last_frame_time = t
if threaded_mode:
task = pool.apply_async(process_frame, (frame.copy(), t, detector, predictor))
else:
task = DummyTask(process_frame(frame, t, detector, predictor))
pending.append(task)
ch = cv2.waitKey(1)
if ch == ord(' '):
threaded_mode = not threaded_mode
if ch == 27:
break
cv2.destroyAllWindows()

Performance issue was due to a bad compilation of dlib. Do not use pip install dlib which runs very very slowly for some reason compared to the proper compilation. I went from almost 10 seconds lag to about 2 seconds this way. So finally I didn't need multi-threading/processing, but I'm working on it to enhance the speed even more. Thanks for the help :)

i tried a simplified approach like P.Ro mentioned in his answer with processes writing to an output queue but somehow the queue got locked most of the time because all the processes wrote to it at the same time. (just my guess) i probably did something wrong.
in the end i ended up using pipes.
the code is nasty. but if i was me a few hours ago. i would still be glad to find an example that actually runs without effort.
from multiprocessing import Process, Queue, Manager,Pipe
import multiprocessing
import face_recognition as fik
import cv2
import time
video_input = 0
obama_image = fik.load_image_file("obama.png")
obama_face_encoding = fik.face_encodings(obama_image)[0]
quality = 0.7
def f(id,fi,fl):
import face_recognition as fok
while True:
small_frame = fi.get()
print("running thread"+str(id))
face_locations = fok.face_locations(small_frame)
if(len(face_locations)>0):
print(face_locations)
for (top7, right7, bottom7, left7) in face_locations:
small_frame_c = small_frame[top7:bottom7, left7:right7]
fl.send(small_frame_c)
fps_var =0
if __name__ == '__main__':
multiprocessing.set_start_method('spawn')
# global megaman
with Manager() as manager:
video_capture = cv2.VideoCapture(video_input)
fi = Queue(maxsize=14)
threads = 8
proc = []
parent_p = []
thread_p = []
# procids = range(0,threads)
for t in range(0,threads):
p_t,c_t = Pipe()
parent_p.append(p_t)
thread_p.append(c_t)
print(t)
proc.append(Process(target=f, args=(t,fi,thread_p[t])))
proc[t].start()
useframe = False
frame_id = 0
while True:
# Grab a single frame of video
ret, frame = video_capture.read()
effheight, effwidth = frame.shape[:2]
if effwidth < 20:
break
# Resize frame of video to 1/4 size for faster face recognition processing
xxx = 930
yyy = 10/16 #0.4234375
small_frame = cv2.resize(frame, (xxx, int(xxx*yyy)))
if frame_id%2 == 0:
if not fi.full():
fi.put(small_frame)
print(frame_id)
cv2.imshow('Video', small_frame)
print("FPS: ", int(1.0 / (time.time() - fps_var)))
fps_var = time.time()
#GET ALL DETECTIONS
for t in range(0,threads):
if parent_p[t].poll():
small_frame_c = parent_p[t].recv()
cv2.imshow('recc', small_frame_c)
height34, width34 = small_frame_c.shape[:2]
# print fsizeee
if(width34<20):
print("face 2 small")
print(width34)
break
face_encodings_cam = fik.face_encodings(small_frame_c,[(0, width34, height34, 0)])
match = fik.compare_faces([obama_face_encoding], face_encodings_cam[0])
name = "Unknown"
if match[0]:
name = "Barack"
print(name)
break
frame_id += 1
# Hit 'q' on the keyboard to quit!
if cv2.waitKey(1) & 0xFF == ord('q'):
break

Do not have much experience with using ThreadPool, but I always just use Process like shown below. You should be able to easily edit this code to fit your needs. I wrote this with your implementation in mind.
This code will get the number of cores and start however many worker processes that will all be implementing the desired function in parallel. They all share a Queue of frames for input and all put to the same output Queue for the main to get and show. Each Queue has a maximum size, in this case 5. This ensures that despite the CPU time it takes to process, it will always be relatively live time.
import numpy as np
import cv2
from multiprocessing import Process, Queue
import time
#from common import clock, draw_str, StatValue
#import video
class Canny_Process(Process):
def __init__(self,frame_queue,output_queue):
Process.__init__(self)
self.frame_queue = frame_queue
self.output_queue = output_queue
self.stop = False
#Initialize your face detectors here
def get_frame(self):
if not self.frame_queue.empty():
return True, self.frame_queue.get()
else:
return False, None
def stopProcess(self):
self.stop = True
def canny_frame(self,frame):
# some intensive computation...
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(gray, 50, 100)
#To simulate CPU Time
#############################
for i in range(1000000):
x = 546*546
res = x/(i+1)
#############################
'REPLACE WITH FACE DETECT CODE HERE'
if self.output_queue.full():
self.output_queue.get_nowait()
self.output_queue.put(edges)
def run(self):
while not self.stop:
ret, frame = self.get_frame()
if ret:
self.canny_frame(frame)
if __name__ == '__main__':
frame_sum = 0
init_time = time.time()
def put_frame(frame):
if Input_Queue.full():
Input_Queue.get_nowait()
Input_Queue.put(frame)
def cap_read(cv2_cap):
ret, frame = cv2_cap.read()
if ret:
put_frame(frame)
cap = cv2.VideoCapture(0)
threadn = cv2.getNumberOfCPUs()
threaded_mode = True
process_list = []
Input_Queue = Queue(maxsize = 5)
Output_Queue = Queue(maxsize = 5)
for x in range((threadn -1)):
canny_process = Canny_Process(frame_queue = Input_Queue,output_queue = Output_Queue)
canny_process.daemon = True
canny_process.start()
process_list.append(canny_process)
ch = cv2.waitKey(1)
cv2.namedWindow('Threaded Video', cv2.WINDOW_NORMAL)
while True:
cap_read(cap)
if not Output_Queue.empty():
result = Output_Queue.get()
cv2.imshow('Threaded Video', result)
ch = cv2.waitKey(5)
if ch == ord(' '):
threaded_mode = not threaded_mode
if ch == 27:
break
cv2.destroyAllWindows()
This should do the trick just change my canny function to do your face detection. I wrote this from your code and compared the two. This is significantly faster. I am using multiprocessing.Process here. In python processes are truly parallel and threads are not quite because of the GIL. I am using 2 queues to send data back and forth between the main and the processes. Queues are both Thread and Process safe.

you may use this, multithreaded:
from imutils.video import VideoStream
# Initialize multithreading the video stream.
videostream = "rtsp://192.168.x.y/user=admin=xxxxxxx_channel=vvvv=1.sdp?params"
vs = VideoStream(src=videostream, resolution=frameSize,
framerate=32).start()
frame = vs.read()