I am using IDS camera functions which I need to capture some images whenever the camera receives an external trigger.
"ueye.is_SetExternalTrigger(hcam, ueye.IS_SET_TRIGGER_HI_LO)"
I can able to connect the camera while it was triggering but I could not save the images to my local memory. The documentation explains to us that images were saved to a memory buffer for every trigger change (HI_LO/LO_HI, etc..)
Does anybody can explain how to use "ueye.is_WaitForNextImage" & "ueye.is_CameraStatus" functions.
Can anybody have experience in these modules? just help me to use them.
I am defining the memory pointer as just "mem_ptr = ueye.c_mem_p()". Does anyone know how to define a specific memory pointer in any particular way and how to access it??
Thank You.
I have tried this one and some other ways also, but I cannot write everything here.
hCam = ueye.HIDS(0) #0: first available camera; 1-254: The camera with the specified camera ID
sInfo = ueye.SENSORINFO()
cInfo = ueye.CAMINFO()
pcImageMemory = ueye.c_mem_p()
MemID = ueye.int()
rectAOI = ueye.IS_RECT()
pitch = ueye.INT()
nBitsPerPixel = ueye.INT(24) #24: bits per pixel for color mode; take 8 bits per pixel for monochrome
channels = 3 #3: channels for color mode(RGB); take 1 channel for monochrome
m_nColorMode = ueye.INT() # Y8/RGB16/RGB24/REG32
bytes_per_pixel = int(nBitsPerPixel / 8)
nRet = ueye.is_InitCamera(hCam, None)
nRet = ueye.is_SetExternalTrigger(hCam, ueye.IS_SET_TRIGGER_HI_LO)
nRet = ueye.is_SetDisplayMode(hCam, ueye.IS_SET_DM_DIB)
nRet = ueye.is_AOI(hCam, ueye.IS_AOI_IMAGE_GET_AOI, rectAOI, ueye.sizeof(rectAOI))
width = rectAOI.s32Width
height = rectAOI.s32Height
nRet = ueye.is_AllocImageMem(hCam, width, height, nBitsPerPixel, pcImageMemory, MemID)
nRet = ueye.is_CaptureVideo(hCam, ueye.IS_DONT_WAIT)
d=0
while(nRet == ueye.IS_SUCCESS):
ueye.is_WaitForNextImage(hCam, 500, pcImageMemory, MemID)
array = ueye.get_data(pcImageMemory, width, height, nBitsPerPixel, pitch, copy=False)
frame = np.reshape(array,(height.value, width.value, bytes_per_pixel))
frame = cv2.resize(frame,(0,0),fx=0.5, fy=0.5)
cv2.imshow("SimpleLive_Python_uEye_OpenCV", frame)
filename = "images/file_%d.jpg"%d
cv2.imwrite(filename, img)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cv2.destroyAllWindows()
ret = ueye.is_StopLiveVideo(hcam, ueye.IS_FORCE_VIDEO_STOP)
ret = ueye.is_ExitCamera(hcam)
Related
I am attempting to save the output of the code proposed here: https://www.programmersought.com/article/34317272452/
The output video is created, but when I try to reproduce the result, there is this message: 'an error occurred could not demultiplex stream'. Also, I have tried several forms to save the video; for example, I have used the shape of the frames, but it neither recognizes them. Could anyone help to figure out where I am doing wrong?
def read_video(video):
# Saliency detection algorithm
saliency_algorithm = "BinWangApr2014"
start_frame = 0
if saliency_algorithm is None or video is None:
print('Please set saliency_algorithm and video')
return
cap = cv2.VideoCapture(video)
video = cv2.VideoWriter('out_j.avi',cv2.VideoWriter_fourcc(*'XVID'), 5, (360,360))
# Set the video start frame
cap.set(cv2.CAP_PROP_POS_FRAMES, start_frame)
_, frame = cap.read()
if frame is None:
print('Please set saliency_algorithm and video')
return
image = frame.copy()
if saliency_algorithm.find("BinWangApr2014")==0:
saliency_algorithm = cv2.saliency.MotionSaliencyBinWangApr2014_create()
# set the size of the data structure
saliency_algorithm.setImagesize(image.shape[1], image.shape[0])
# Initialization
saliency_algorithm.init()
paused = False
while True:
if not paused:
_, frame = cap.read()
if frame is None:
break
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
start = cv2.getTickCount()
success, saliencyMap = saliency_algorithm.computeSaliency(frame)
duration = (cv2.getCPUTickCount() - start)/ \
cv2.getTickFrequency()
#print("computeBinaryMap cost time is: {} ms".format(duration * 1000))
video.write(frame)
#cv2.imshow('image', frame)
cv2.imshow('saliencyMap', saliencyMap*255)
c = cv2.waitKey(2)
c = chr(c) if c !=-1 else 0
if c == 'q':
break
if c == 'p':
paused = not paused
#video.write(frame)
cv2.destroyAllWindows()
video.release()
#camera.stop()
return
Thanks in advance
I'm using OpenCV (3.4.6) to capture video from a Raspberry Pi HQ camera, attached to a Raspberry Pi 4 running Raspbian 10. Bounding boxes suggested by an object detection algorithm are then drawn onto the captured frames, and the output saved to a video file.
Everything works great, except when I try to crop the video to my region of interest.
Using the following line successfully crops the image (as confirmed by cv2.imshow):
frame = frame[0:720, 280:1000]
But, it also stops the output being saved by out.write - I just get an empty file, and being new to both Python and OpenCV, I'm struggling to work out why.
I've trimmed down the code to the relevant part below (apologies - it's still a bit long - I'm not entirely sure what might be important):
# begin video capture
try:
vid = cv2.VideoCapture(int(video_path), cv2.CAP_V4L2)
except:
vid = cv2.VideoCapture(video_path)
out = None
# set capture resolution
inputcodec = cv2.VideoWriter_fourcc('M', 'J', 'P', 'G') # forces MJPG codec
vid.set(6, inputcodec) # sets inputcodec for video capture
vid.set(3, 1280) # sets video capture width
vid.set(4, 720) # sets video capture height
if FLAGS.output:
# by default VideoCapture returns float instead of int
width = int(vid.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(vid.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = int(vid.get(cv2.CAP_PROP_FPS))
codec = cv2.VideoWriter_fourcc(*FLAGS.output_format)
out = cv2.VideoWriter(FLAGS.output, codec, fps, (width, height))
while True:
return_value, frame = vid.read()
frame = frame[0:720, 280:1000] # crop video to square region of interest
if return_value:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
image = Image.fromarray(frame)
else:
print('Video has ended or failed, try a different video format!')
break
frame_size = frame.shape[:2]
image_data = cv2.resize(frame, (input_size, input_size))
image_data = image_data / 255.
image_data = image_data[np.newaxis, ...].astype(np.float32)
start_time = time.time()
if FLAGS.framework == 'tflite':
interpreter.set_tensor(input_details[0]['index'], image_data)
interpreter.invoke()
pred = [interpreter.get_tensor(output_details[i]['index']) for i in range(len(output_details))]
if FLAGS.model == 'yolov3' and FLAGS.tiny == True:
boxes, pred_conf = filter_boxes(pred[1], pred[0], score_threshold=0.25,
input_shape=tf.constant([input_size, input_size]))
else:
boxes, pred_conf = filter_boxes(pred[0], pred[1], score_threshold=0.25,
input_shape=tf.constant([input_size, input_size]))
else:
batch_data = tf.constant(image_data)
pred_bbox = infer(batch_data)
for key, value in pred_bbox.items():
boxes = value[:, :, 0:4]
pred_conf = value[:, :, 4:]
boxes, scores, classes, valid_detections = tf.image.combined_non_max_suppression(
boxes=tf.reshape(boxes, (tf.shape(boxes)[0], -1, 1, 4)),
scores=tf.reshape(
pred_conf, (tf.shape(pred_conf)[0], -1, tf.shape(pred_conf)[-1])),
max_output_size_per_class=50,
max_total_size=50,
iou_threshold=FLAGS.iou,
score_threshold=FLAGS.score
)
pred_bbox = [boxes.numpy(), scores.numpy(), classes.numpy(), valid_detections.numpy()]
image = utils.draw_bbox(frame, pred_bbox)
fps = 1.0 / (time.time() - start_time)
print("FPS: %.2f" % fps)
result = np.asarray(image)
cv2.namedWindow("result", cv2.WINDOW_AUTOSIZE)
result = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
if not FLAGS.dont_show:
cv2.imshow("result", result)
if FLAGS.output:
out.write(result)
if cv2.waitKey(1) & 0xFF == ord('q'): break
cv2.destroyAllWindows()
Any suggestions for how to resolve this would be greatly appreciated.
You created the VideoWriter and set its width and height to 1280 by 720. Later you told it to save 720 by 720 crops. That is the problem. frame = frame[0:720, 280:1000] is a 720 by 720 array.
Decide what dimensions your video should have, and use them consistently. A video stream can't change resolution.
When I execute following:
availableBackends = [cv2.videoio_registry.getBackendName(b) for b in cv2.videoio_registry.getBackends()]
print(availableBackends)
I get ['FFMPEG', 'GSTREAMER', 'INTEL_MFX', 'V4L2', 'CV_IMAGES', 'CV_MJPEG'].
If I now try:
print(cv2.CAP_FFMPEG)
print(cv2.CAP_GSTREAMER)
print(cv2.CAP_INTEL_MFX)
print(cv2.CAP_V4L2)
print(cv2.CAP_IMAGES)
print(cv2.CAP_MJPEG)
all work except the last one:
AttributeError: module 'cv2.cv2' has no attribute 'CAP_MJPEG'
How can I explicitly set cv2.CAP_MJPEG backend (cv2.CAP_CV_MJPEG does not work either)?
You can see all the flags here.
It looks like cv2.CAP_OPENCV_MJPEG is what you are looking for.
The following test creates MJPEG synthetic AVI video file, and reads the video using cv2.CAP_OPENCV_MJPEG backend:
import numpy as np
import cv2
#availableBackends = [cv2.videoio_registry.getBackendName(b) for b in cv2.videoio_registry.getBackends()]
#print(availableBackends)
print('cv2.CAP_OPENCV_MJPEG = ' + str(cv2.CAP_OPENCV_MJPEG))
intput_filename = 'vid.avi'
# Generate synthetic video files to be used as input:
###############################################################################
width, height, n_frames = 640, 480, 100 # 100 frames, resolution 640x480
# Use motion JPEG codec (for testing)
synthetic_out = cv2.VideoWriter(intput_filename, cv2.VideoWriter_fourcc(*'MJPG'), 25, (width, height))
for i in range(n_frames):
img = np.full((height, width, 3), 60, np.uint8)
cv2.putText(img, str(i+1), (width//2-100*len(str(i+1)), height//2+100), cv2.FONT_HERSHEY_DUPLEX, 10, (30, 255, 30), 20) # Green number
synthetic_out.write(img)
synthetic_out.release()
###############################################################################
# Read the video using CV_MJPEG backend
###############################################################################
cap = cv2.VideoCapture(intput_filename, cv2.CAP_OPENCV_MJPEG)
# Keep iterating break
while True:
ret, frame = cap.read() # Read frame from first video
if ret:
cv2.imshow('frame', frame) # Display frame for testing
cv2.waitKey(100) #Wait 100msec (for debugging)
else:
break
cap.release() #Release must be inside the outer loop
###############################################################################
I am working with a 2h-long 4K MP4 video shot at 25 fps. I am using openCV in python 3. From the video, I need to extract 3 circular ROIs.
Because of the large number of frames (212831), processing the whole video frame by frame with the code below takes over 24h on a 64 GB RAM workstation. Is there a way to speed up the processing or a workaround that does something slightly different to a similar effect?
Here is the code.
import numpy as np
import cv2
import sys
import time
# name of source video and paths
video = 'C0023_2hanalysis'
input_vidpath = 'path_to_video/' + video + '.MP4'
output_vidpath = 'path_to_video/' + video + '-withROI.MP4'
codec = 'avc1'
# set ROI coordinates extrapolated from last video frame as well as fixed parameters for analysis (radius)
x = 1188 # in pixels
y = 1204 # in pixels
radius = 75
# set parameters for output video as identical to input
fps = 25.0
scaling = 1.0 # output vs input video speed (?)
## import video
cap = cv2.VideoCapture(input_vidpath)
if cap.isOpened() == False:
sys.exit('Video file cannot be read! Please check input_vidpath to ensure it is correctly pointing to the video file')
## Video writer class to output video
fourcc = cv2.VideoWriter_fourcc(*codec) # concatenate the 4 chars to a fourcc code, i.e. the 4-char name of the codec used to compress the frames
# adjust output frame size to scaling if any is applied
#(frame shape is given as height,width , so the output needs to be re-ordered to match VideoWriter arguments)
o_height = cap.read()[1].shape[0]
o_width = cap.read()[1].shape[1]
output_framesize = (int(o_width*scaling),int(o_height*scaling))
out = cv2.VideoWriter(filename = output_vidpath, fourcc = 0x7634706d, fps = fps, frameSize = output_framesize, isColor = True)
## apply ROI frame by frame and thread them back into output video
start = time.time()
f = -1
last = 0
while(True):
# Capture frame-by-frame
ret, frame = cap.read() #'return' value (T/F) and frame
this = cap.get(1) # get 'CV_CAP_PROP_POS_FRAMES'
if ret == True:
#frame = cv2.resize(frame, None, fx = scaling, fy = scaling, interpolation = cv2.INTER_LINEAR) # no need to resize in this case
# Apply mask to area of interest
mask = np.zeros((o_height,o_width), np.uint8)
mask = cv2.circle(mask,(x,y),radius,255,thickness=-1) #image, row and column coord of centre of circle, radius, color (black), thickness
frame[mask == 0] = 0
out.write(frame)
key = cv2.waitKey(1) & 0xFF
# if the `q` key was pressed, break from the loop
if key == ord("q"):
break
f += 1
if f%1000==0:
print(f)
if last == this:
break
last = this
## When everything done, release the capture
cap.release()
out.release()
cv2.destroyAllWindows()
cv2.waitKey(1)
## End time and duration
end = time.time()
duration = end - start
print("--- %s seconds ---" %duration)
This is a common mistake. You shouldn't call waitKey(1) when you want to process the frames as fast possible. That function is basically add a short sleep after processing each frame, and that sleep time is much longer that processing time.
You just need to remove that, and still you can just kill the process if want to stop in the middle.
import numpy as np
import cv2
import sys
import time
# name of source video and paths
video = 'C0023_2hanalysis'
input_vidpath = 'path_to_video/' + video + '.MP4'
output_vidpath = 'path_to_video/' + video + '-withROI.MP4'
codec = 'avc1'
# set ROI coordinates extrapolated from last video frame as well as fixed parameters for analysis (radius)
x = 1188 # in pixels
y = 1204 # in pixels
radius = 75
# set parameters for output video as identical to input
fps = 25.0
scaling = 1.0 # output vs input video speed (?)
## import video
cap = cv2.VideoCapture(input_vidpath)
if cap.isOpened() == False:
sys.exit('Video file cannot be read! Please check input_vidpath to ensure it is correctly pointing to the video file')
## Video writer class to output video
fourcc = cv2.VideoWriter_fourcc(*codec) # concatenate the 4 chars to a fourcc code, i.e. the 4-char name of the codec used to compress the frames
# adjust output frame size to scaling if any is applied
#(frame shape is given as height,width , so the output needs to be re-ordered to match VideoWriter arguments)
o_height = cap.read()[1].shape[0]
o_width = cap.read()[1].shape[1]
output_framesize = (int(o_width*scaling),int(o_height*scaling))
out = cv2.VideoWriter(filename = output_vidpath, fourcc = 0x7634706d, fps = fps, frameSize = output_framesize, isColor = True)
## apply ROI frame by frame and thread them back into output video
start = time.time()
f = -1
last = 0
while(True):
# Capture frame-by-frame
ret, frame = cap.read() #'return' value (T/F) and frame
this = cap.get(1) # get 'CV_CAP_PROP_POS_FRAMES'
if ret == True:
#frame = cv2.resize(frame, None, fx = scaling, fy = scaling, interpolation = cv2.INTER_LINEAR) # no need to resize in this case
# Apply mask to area of interest
mask = np.zeros((o_height,o_width), np.uint8)
mask = cv2.circle(mask,(x,y),radius,255,thickness=-1) #image, row and column coord of centre of circle, radius, color (black), thickness
frame[mask == 0] = 0
out.write(frame)
f += 1
if f%1000==0:
print(f)
if last == this:
break
last = this
## When everything done, release the capture
cap.release()
out.release()
cv2.destroyAllWindows()
cv2.waitKey(1)
## End time and duration
end = time.time()
duration = end - start
print("--- %s seconds ---" %duration)
I have a project which i should implement a video stabilizer using fft and filters (lpf or hpf)
Here is some part of the code that i want to modify it:
import cv2
import numpy as np
# Create a VideoCapture object and read from input file
cap = cv2.VideoCapture('Vibrated2.avi')
# load data
data = np.loadtxt('Vibrated2.txt', delimiter=',')
# Define the codec and create VideoWriter object
frame_width = int(cap.get(3))
frame_height = int(cap.get(4))
out = cv2.VideoWriter('Stabilized.avi',cv2.VideoWriter_fourcc('M','J','P','G'), 25, (frame_width,frame_height))
# Check if camera opened successfully
if (cap.isOpened()== False):
print("Error opening video stream or file")
def transform(frame, param):
ti = cv2.getRotationMatrix2D((0,0), 0, 1)
ti[0,2] += param[0]
ti[1,2] += param[1]
frame = cv2.warpAffine(frame, ti, frame.shape[1:-4:-1])
return frame
num = 0
# Read until video is completed
while(cap.isOpened()):
# Capture frame-by-frame
ret, frame = cap.read()
if ret == True:
# apply transformation
frame = transform(frame, data[num])
print (frame, "dn")
num+=1
# Display the resulting frame
cv2.imshow('Frame',frame)
# Write the frame into the file 'output.avi'
out.write(frame)
# Press Q on keyboard to exit
if cv2.waitKey(25) & 0xFF == ord('q'):
break
# Break the loop
else:
break
# When everything done, release the video capture object
cap.release()
# Closes all the frames
cv2.destroyAllWindows()
There is a video named Vibrated1.avi
And a text file which includes frames difference named Virated1.txt and it is looked like this:
0.341486, -0.258215
0.121945, 1.27605
-0.0811261, 0.78985
,...
I don't know how and where should i add some filters to this code to remove video vibration
Could anyone help me ?
I can write short pseudocode with C++ parts:
cv::Mat homoFiltered = cv::Mat::eye(3, 3, CV_32F);
const double alpha = 0.9;
cv::Mat a1 = cv::Mat::eye(3, 3, CV_32F) * alpha;
cv::Mat a2 = cv::Mat::eye(3, 3, CV_32F) * (1. - alpha);
while cap >> frame:
cv::Mat homo = CalcHomography(frame)
homoFiltered = a1 * (homoFiltered * homo) + a2 * homo;
cv::warpPerspective(..., homoFiltered, ...)
alpha - low pass smoothing coefficient in [0; 1]
a1 and a2 - matrices for applying alpha and (1 - alpha) to the transformation matrix
homoFiltered - filtered transformation matrix
homo - current matrix between Frame(t-1) and Frame(t)