When I run my code
import cv2
filepath = raw_input("enter the path to the video ")
#cap = cv2.VideoCapture(filepath)
cap = cv2.VideoCapture('filesrc location-/media/kelly/Files/aaaa.mp4 ! decodebin ! appsink', cv2.CAP_GSTREAMER)
if not cap.isOpened():
print("Cannot capture test src. Exiting.")
quit()
The following errors appear
> (python:28088): GStreamer-CRITICAL **:> gst_element_make_from_uri: assertion> 'gst_uri_is_valid (uri)' failed>> (python:28088): GLib-GObject-WARNING> **: invalid cast from 'GstFileSrc' to 'GstBin'>> (python:28088): GStreamer-CRITICAL **:> gst_bin_iterate_elements: assertion> 'GST_IS_BIN (bin)' failed>> (python:28088): GStreamer-CRITICAL **:> gst_iterator_next: assertion 'it !=> NULL' failed>> (python:28088): GStreamer-CRITICAL **:> gst_iterator_free: assertion 'it !=> NULL' failed OpenCV Error: Unspecified> error (GStreamer: cannot find appsink> in manual pipeline ) in> cvCaptureFromCAM_GStreamer, file> /home/kelly/opencv-3.4.0/modules/videoio/src/cap_gstreamer.cpp,> line 805> VIDEOIO(cvCreateCapture_GStreamer> (CV_CAP_GSTREAMER_FILE, filename)):> raised OpenCV exception:>> /home/kelly/opencv-3.4.0/modules/videoio/src/cap_gstreamer.cpp:805:> error: (-2) GStreamer: cannot find> appsink in manual pipeline in> function cvCaptureFromCAM_GStreamer
I tried to find a solution myself. But I did not succeed. How can fix these errors?
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Errors in code execution Gstreamer
↧
A bug causes crash when class inherit from cv2.KeyPoint
Look this code:
import numpy as np
import cv2
class T(cv2.KeyPoint):
def __init__(self, pt):
super().__init__()
self.pt = pt
def calculate_corners(A):
A_gray = cv2.cvtColor(A, cv2.COLOR_BGR2GRAY)
pa = cv2.goodFeaturesToTrack(A_gray, maxCorners=100, qualityLevel=0.01, minDistance=15)
pa = np.squeeze(pa)
kpa = []
for coor in pa:
kpa.append(T(tuple(coor)))
return kpa
cap = cv2.VideoCapture("E:\\video\\test.mp4")
while True:
frame = cap.read()[1]
if frame is None:
break
kpa = calculate_corners(frame)
frame_corner = cv2.drawKeypoints(frame, kpa, outImage=None, color=(255, 0, 125))
cv2.imshow('frame_corner', frame_corner)
cv2.waitKey(1)
cv2.destroyAllWindows()
cap.release()
This code will crash in my system(Windows10, python3.7.3, opencv4.1.0)
After test, I'm sure this is caused by class T. I guess that class T dose not inherit release moudle of cv2.KeyPoint, so it cause memory leak. It's just my conjecture. And I didn't know how to fix it. Could anyone give me some advice? Thanks a lot!
↧
↧
Saving a video from Tello Drone
Hi everyone, totally new with programming and here so bare with me.
Currently, I'm using this guide to help me with my current project "https://github.com/hanyazou/TelloPy/blob/develop-0.7.0/tellopy/examples/video_effect.py"
I managed to get the live video, but in order to continue I need to save that video but unfortunately I have no idea on how to do so.
Then I read on this "https://docs.opencv.org/3.0-beta/doc/py_tutorials/py_gui/py_video_display/py_video_display.html" but I'm not sure where to even start.
Any help would be so helpful!
↧
I found 2 circles, I want to cut and save each circle into a different picture, how to do that in python? my code in link..
[link text](https://textuploader.com/110jd)
↧
Why i get black stripes on crops with feature matching?
Hi all. Im trying to make an featuring match web app for learn opencv, but i actually have problems with the crop.
My code do this:
- Compare two images
- Find feature matchs from one to other
- Warp this featured region
- Crop it
My results:
How you can see i get images like that, with a black stripe on the borders. I execute the code with a bat file, with arguments, ill paste both codes:
- Full code:
# python libraries
import rawpy
import os
import sys
import json
import argparse
import cv2
import numpy as np
import math
from PIL import Image
MIN_MATCH_COUNT = 4
HORIZONTAL = 0
VERTICAL = 1
VERTICAL_HORIZONTAL = -1
FLIPNONE = 2
RATIO = 0.75
RAN_VAL = 8.0
def calculateDistance(p0, p1):
dist = math.sqrt((p1[0][0] - p0[0][0])**2 + (p1[0][1] - p0[0][1])**2)
return dist
def detect_matches(jpguser,raworiginal,flipmode):
if flipmode == FLIPNONE:
raworiginalflip = raworiginal
else:
raworiginalflip = cv2.flip(raworiginal,flipmode)
#Detect Item
detect = {}
gray1 = cv2.cvtColor(jpguser, cv2.COLOR_BGR2GRAY)
gray2 = cv2.cvtColor(raworiginalflip, cv2.COLOR_BGR2GRAY)
minHessian = 100
sift = cv2.xfeatures2d_SURF.create(hessianThreshold=minHessian,nOctaveLayers=6)
## (3) Create flann matcher
matcher = cv2.FlannBasedMatcher(dict(algorithm = 1, trees = 2), {})
## (4) Detect keypoints and compute keypointer descriptors
kpts1, descs1 = sift.detectAndCompute(gray1,None)
kpts2, descs2 = sift.detectAndCompute(gray2,None)
## (5) knnMatch to get Top2
matches = matcher.knnMatch(descs1, descs2, 2)
# Sort by their distance.
matches = sorted(matches, key = lambda x:x[0].distance)
## (6) Ratio test, to get good matches.
ratio_thresh = RATIO
good = []
for m,n in matches:
if m.distance < ratio_thresh * n.distance:
good.append(m)
detect['kpts1'] = kpts1
detect['kpts2'] = kpts2
detect['good'] = good
detect['raworiginal'] = raworiginalflip
detect['flipmode'] = flipmode
if len(detect['good']) >= MIN_MATCH_COUNT:
return detect
else:
if flipmode > VERTICAL_HORIZONTAL:
return detect_matches(jpguser,raworiginal,flipmode-1)
return detect
def find_homography(item,jpguser,raworiginal,detect,matchedfile):
canvas = raworiginal.copy()
## (queryIndex for the small object, trainIndex for the scene )
src_pts = np.float32([detect['kpts1'][m.queryIdx].pt for m in detect['good']]).reshape(-1, 1, 2)
dst_pts = np.float32([detect['kpts2'][m.trainIdx].pt for m in detect['good']]).reshape(-1, 1, 2)
## find homography matrix in cv2.RANSAC using good match points
M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, RAN_VAL)
h,w = jpguser.shape[:2]
pts = np.float32([ [0,0],[0,h-1],[w-1,h-1],[w-1,0] ]).reshape(-1,1,2)
dst = cv2.perspectiveTransform(pts,M)
p1 = dst[0]
p2 = dst[3]
p3 = dst[1]
p4 = dst[2]
wNew = calculateDistance(p1, p2)
hNew = calculateDistance(p1, p3)
a = M[0,0]
b = M[0,1]
c = M[0,2]
d = M[1,0]
e = M[1,1]
f = M[1,2]
p = math.sqrt(a*a + b*b)
r = (a*e - b*d)/(p)
theta = - math.atan2(b, a) * 180 / math.pi
cv2.polylines(canvas,[np.int32(dst)],True,(0,0,255),3, cv2.LINE_AA)
## (8) drawMatches
matched = cv2.drawMatches(jpguser,detect['kpts1'],canvas,detect['kpts2'],detect['good'],None)#,**draw_params)
## (9) Crop the matched region from scene
h,w = jpguser.shape[:2]
pts = np.float32([ [0,0],[0,h-1],[w-1,h-1],[w-1,0] ]).reshape(-1,1,2)
dst = cv2.perspectiveTransform(pts,M)
perspectiveM = cv2.getPerspectiveTransform(np.float32(dst),pts)
found = cv2.warpPerspective(raworiginal,perspectiveM,(w,h))
## (10) % CROP
h2,w2 = raworiginal.shape[:2]
item['CropH'] = (hNew/h2)*100
item['CropW'] = (wNew/w2)*100
item['FlipMode'] = detect['flipmode']
item['Rotation'] = theta
item['ScaleX'] = p*100
item['ScaleY'] = r*100
## (11) Save
cv2.imwrite(item['PathImageJPGOriginal'], found)
if(matchedfile):
cv2.imwrite(matchedfile, matched)
item['Success'] = True
return item
def extract_original(raworiginal, user, original, matchedfile):
# JSON ITEM
item = {}
try:
item['ImageJPGOriginal'] = original.split("\\")[-1]
item['PathImageJPGOriginal'] = original
item['ImageRawOriginal'] = raworiginal.split("\\")[-1]
item['PathImageRawOriginal'] = raworiginal
item['ImageJPGUser'] = user.split("\\")[-1]
item['PathImageJPGUser'] = user
item['Success'] = False
if (not(matchedfile is None)):
item['PathImageMatched'] = matchedfile
item['ImageMatched'] = matchedfile.split("\\")[-1]
if (os.path.exists(item['PathImageJPGUser']) and os.path.exists(item['PathImageRawOriginal'])):
if os.path.exists(item['PathImageJPGOriginal']):
os.remove(item['PathImageJPGOriginal'])
## Preparación de los datos
jpguser = cv2.imread(item['PathImageJPGUser'])
raworiginal = cv2.imread(item['PathImageRawOriginal'])
detect = detect_matches(jpguser,raworiginal,FLIPNONE)
print(len(detect['good']))
if len(detect['good']) > MIN_MATCH_COUNT:
item = find_homography(item,jpguser,detect['raworiginal'],detect,matchedfile)
else:
item['NotMatched'] = True
item['Error'] = "Not enough matches are found - {}/{}".format(len(detect['good']),MIN_MATCH_COUNT)
else:
item['Error'] = "File not exists"
except OSError as e:
item['Success'] = False
item['Error'] = '%s %s %s' % (e.errno, e.strerror, e.filename)
except Exception as e:
item['Success'] = False
item['Error'] = getattr(e, 'message', str(e))
finally:
return item
def main():
parser = argparse.ArgumentParser()
parser.add_argument("-r", "--raworiginal", help="a path that contains the RAW Camera file")
parser.add_argument("-u", "--user", help="a path where the JPGuser file")
parser.add_argument("-o", "--original", help="a path where the JPGoriginal file will be copied"),
parser.add_argument("-m", "--matched", help="a path where the matched file will be copied"),
parser.add_argument("-json", "--json", help="a JSON string with images")
args = parser.parse_args()
if (not ((args.raworiginal and args.user and args.original) or args.json)):
parser.print_help()
if (args.raworiginal and args.user and args.original):
print(json.dumps(extract_original(args.raworiginal, args.user, args.original, args.matched)), end='', flush=True)
else:
if (args.json):
data = []
data_json = json.loads(args.json)
for i in range(len(data_json)):
item = extract_original(data_json[i]["PathImageRawOriginal"], data_json[i]["PathImageJPGUser"], data_json[i]["PathImageJPGOriginal"], data_json[i]["PathImageMatched"])
item["Id"] = data_json[i]["Id"]
data.append(item)
print(json.dumps(data), end='', flush=True)
if __name__ == "__main__":
main()
- bat file:
@echo off
python.exe extract.py -r "Insert image to crop" -u "insert image for find and comare" -o "save path" -m "matched file for see matches of the result image"
pause
How can solve this? i want accuracy in my project but... how can do it? Actually im using python and want more accuracy process, but i dont get good results.
I start thinking that warpTransform apply any rounding and for this i get this results but im newbie on opencv
↧
↧
I want to monitor this python process from openframeworks using C#
Hi, All.
I have executed python process like this -
$ python3 infer.py
And, I want to monitor this python process from openframeworks using C#.net watchdog.
When this process killed, Iit must be restarted by watchdog.
Is there any watchdog Nuget Package which can monitor python?
How can I get this ?
Thanks in advance.
↧
Any implementation of LineSegmentDetector for python ?
I see that cv2.createLineSegmentDetector implementation has been removed due original code license issues in function, any workarounds that don't include downgrading to an older version ?
Thanks a lot!
↧
CV2 installed, but Import cv2 on spyder gives an error
Hi, yesterday I tried different approaches to install OpenCV.
when I go to the following directory:
"C:\Users\Salvo\Anaconda3\Lib\site-packages\cv2"
I can find the package is installed. I have windows 10, python 3.7 installed.
when I open Spyder (or also Jupyter notebook) i receive the same error.
> import cv2 as cv Traceback (most> recent call last):>> File> "", line> 1, in > import cv2 as cv>> File> "C:\Users\Salvo\Anaconda3\lib\site-packages\cv2\__init__.py",> line 3, in > from .cv2 import *>> ImportError: DLL load failed: he> specified module could not be found.
which is weird because all the packages are there and inside the cv2 folder I found __init__.py file.
Thank you for your help
↧
CalcHist gives different results on C++ vs Python
I'm loading a Tiff and running calcHist in python and c++, but I can't get the results to match. Code and output attached.
Thank you!
**Python Implementation:**
import numpy as np
import cv2 as cv
from PIL import Image
import matplotlib.pyplot as plt
im = Image.open('data.tif')
img = np.array(im).astype(np.uint16)
hist = cv.calcHist([img],[0],None,[256],[0,334])
print(hist)
plt.hist(img.ravel(),255,range=(0,1305)); plt.show()
**C++:**
uint16* raster = new uint16[width*length];
for (uint16 y = 0; y < length; ++y) {
for (uint16 x = 0; x < width; ++x) {
raster[y*width + x] = rasterYX[y][x];
}
}
cv::Mat flattened(length, width, CV_16UC1, &raster[0]);
cv::Mat inputHist;
cv::Mat hist;
float range[] = {0,334};
int num_bins = 256;
const float* ranges[] = {range};
cv::calcHist(&flattened, 1, 0, cv::Mat(), hist, 1,&num_bins, ranges, true, false);
std::cout << "Hist = " << std::endl << cv::format(hist, cv::Formatter::FMT_PYTHON);
Here is part of the c++ output followed by the python output at the point where they begin to diverge.
0,
1,
2,
1,
4,
4,
2,
1,
11,
6,
16,
38,
[0.000000e+00]
[1.000000e+00]
[2.000000e+00]
[1.000000e+00]
[5.000000e+00]
[3.000000e+00]
[2.000000e+00]
[6.000000e+00]
[6.000000e+00]
[6.000000e+00]
[3.100000e+01]
[2.300000e+01]
[3.600000e+01]
[2.010000e+02]
[2.160000e+02]
[4.350000e+02]
[9.560000e+02]
↧
↧
Color consistency algorithms python
Hey folks,
I'm working on forgery detection (pixel and edge-based Color Estimation) there are some algorithms which work well.
1) gray world
2) max RGB
3) shades of gray
4) first-order Gray edge
5) Second-order Gray edge
6) Weighted Gray-Edge
7) Illuminant Map
I've implemented the first 2 ( gray world, max RGB). Does anyone know OpenCV implementation of remaining algorithms?
& I'm also confused between the **first-order edge and first-order grey edge** these 2 terms/algo are same?
↧
Predict trajectory of hand based on 3D coordinates
Hi all,
I'm looking into a way to predict the trajectory of a hand that is grabbing towards an object in Python. I already have 3D coordinates of all joints of the hand (I'm using a Charuco board and stereo vision and thus everything is calibrated, beside that I'm using a pose estimation algorithm). I'm basically just looking into an efficient way to **predict or estimate where the hand will be at frame 'x' in the future based on the 3D coordinates of the x number of previous frames**. Which method would you recommend me for this? I'm sure there has to be an 'easy way' of doing this. I already took a look into Kalman filtering, Meanshift and CamShift, but I'm not sure if these are appropriate techniques.
Thanks a lot!
Jérémy
↧
getting select timeout on Linux, quirks solution doesn't work
Hello everyone.
I'm using opencv to capture frames from a USB camera 24/7 for a face recognition project. I've noticed that I get a select timeout error during the early morning hours, when there is no people to be recognized, meaning the frames are pretty much identical (an empty room). How could I prevent this from happening.
I'm using a LI-USB30-ISX017 - Leopard Imaging Inc USB 3 camera mounted on RedHat Enterprise Linux 7.5 with kernel 3.10.0-957.5.1.el7.x86_64. The ML part uses Python 3.6.5 with opencv-python 3.4.2.17 and tensorflow.
I have read many a solution and none have fixed the problem: using **options uvcvideo quirks=640 nodrop=1 timeout=50000** (and 128 along with lower timeouts) has reduced the frequency of the timeouts, but they still happen occasionally.
Any help would be appreciated.
↧
VideoCapture failing for ~25% of videos from IP Camera
I've had a Python (3.7) app using OpenCV (3.4) for a year or so, working fine. I recently reinstalled Debian Buster, and with it upgraded to OpenCV 4.1. Somewhere in that process I've found that videos which previously worked ok now can't be read by OpenCV.
A sample video with the problem is here: https://www.dropbox.com/s/tu4ddegh6yn05nu/ErrorReadingHeader.mp4?dl=0
Basically, this plays fine in most media apps, but my install of OpenCV won't open it - isOpened() returns false. However, the odd thing is that it's not a constant problem - most of the videos from the same IP camera work fine, but around a quarter can't be read. I assume there's some very minor oddity with the files, but I can't see what it is, and it's hard to get the manufacturer to do much about it because they play fine in most media apps.
When I run `cv2.getBuildInformation()` I get the following:
Video I/O:
DC1394: NO
FFMPEG: YES
avcodec: YES (58.35.100)
avformat: YES (58.20.100)
avutil: YES (56.22.100)
swscale: YES (5.3.100)
avresample: NO
GStreamer: YES (1.14.4)
v4l/v4l2: YES (linux/videodev2.h)
After turning on debugging mode (with `export OPENCV_LOG_LEVEL=debug` and `export OPENCV_VIDEOIO_DEBUG=1`) I get the following when I try to do `vc = cv2.VideoCapture('/home/Dave/ErrorReadingHeader.mp4')`:
[ INFO:0] VIDEOIO: Enabled backends(5, sorted by priority): FFMPEG(1000); GSTREAMER(990); V4L2(980); CV_IMAGES(970); CV_MJPEG(960)
[ WARN:0] VIDEOIO(FFMPEG): trying capture filename='/home/Dave/ErrorReadingHeader.mp4' ...
[mov,mp4,m4a,3gp,3g2,mj2 @ 0x2338590] error reading header
[ WARN:0] VIDEOIO(FFMPEG): can't create capture
[ WARN:0] VIDEOIO(GSTREAMER): trying capture filename='/home/Dave/ErrorReadingHeader.mp4' ...
[ WARN:0] VIDEOIO(GSTREAMER): can't create capture
[ WARN:0] VIDEOIO(V4L2): trying capture filename='/home/Dave/ErrorReadingHeader.mp4' ...
VIDIOC_REQBUFS: Inappropriate ioctl for device
[ WARN:0] VIDEOIO(V4L2): can't create capture
[ WARN:0] VIDEOIO(CV_IMAGES): trying capture filename='/home/Dave/ErrorReadingHeader.mp4' ...
[ WARN:0] VIDEOIO(CV_IMAGES): created, isOpened=0
[ WARN:0] VIDEOIO(CV_MJPEG): trying capture filename='/home/Dave/ErrorReadingHeader.mp4' ...
[ WARN:0] VIDEOIO(CV_MJPEG): can't create capture
Any thoughts on how to resolve this issue? Keen to understand if this particular video can be opened in other people's installations of OpenCV 4.1, and if so what your VideoIO configuration is.
Edit: for info, the following are the steps I used to install OpenCV 4.1 on Raspberry Pi 4 / Debian Buster:
Temporarily increase swap file size and gpu memory:
sudo sed -i 's/CONF_SWAPSIZE=100/CONF_SWAPSIZE=2048/g' /etc/dphys-swapfile
sudo /etc/init.d/dphys-swapfile stop
sudo /etc/init.d/dphys-swapfile start
sudo sed -i '$a gpu_mem=128' /boot/config.txt
Install dependencies:
sudo apt-get -y install build-essential cmake pkg-config git
sudo apt-get -y install libjpeg-dev libtiff-dev libjasper-dev libpng12-dev
sudo apt-get -y install libavcodec-dev libavformat-dev libswscale-dev libv4l-dev libxvidcore-dev libx264-dev
sudo apt-get -y install libgtk2.0-dev libgtk-3-dev libatlas-base-dev gfortran
sudo apt-get install libcanberra-gtk* which is an ARM-specific version of GTK
sudo apt install libgstreamer1.0-dev libgstreamer-plugins-base1.0-dev
sudo apt-get -y install python3-dev python3-numpy python3-pip
Download and prepare OpenCV:
wget -O opencv.zip https://github.com/opencv/opencv/archive/4.1.0.zip
wget -O opencv_contrib.zip https://github.com/opencv/opencv_contrib/archive/4.1.0.zip
unzip opencv.zip
unzip opencv_contrib.zip
mv opencv-4.1.0/ opencv
mv opencv_contrib-4.1.0/ opencv_contrib
Build OpenCV:
cd ~/opencv
mkdir build
cd build
cmake -D CMAKE_BUILD_TYPE=RELEASE \
-D CMAKE_INSTALL_PREFIX=/usr/local \
-D OPENCV_EXTRA_MODULES_PATH=~/opencv_contrib/modules \
-D ENABLE_NEON=ON \
-D ENABLE_VFPV3=ON \
-D BUILD_TESTS=OFF \
-D PYTHON_DEFAULT_EXECUTABLE=/usr/bin/python3 \
-D WITH_GSTREAMER=ON \
-D WITH_GSTREAMER_0_10=OFF \
-D OPENCV_ENABLE_NONFREE=ON \
-D INSTALL_PYTHON_EXAMPLES=OFF \
-D BUILD_EXAMPLES=OFF ..
Make and install OpenCV:
make -j4 noting this could take an hour or more
sudo make install
sudo ldconfig
sudo apt-get update
Copy the package for Python:
sudo cp ~/opencv/build/lib/python3/cv2.cpython-37m-arm-linux-gnueabihf.so /usr/local/lib/python3.7/dist-packages/cv2.so
Tidy up:
cd ~
sudo sed -i 's/CONF_SWAPSIZE=2048/CONF_SWAPSIZE=100/g' /etc/dphys-swapfile
sudo /etc/init.d/dphys-swapfile stop
sudo /etc/init.d/dphys-swapfile start
sudo sed -i 's/gpu_mem=128/gpu_mem=16/g' /boot/config.txt
rm opencv.zip
rm opencv_contrib.zip
sudo rm -r opencv
sudo rm -r opencv_contrib
Test that it installed successfully:
python 3, then within python import cv2, then print(cv2.__version__)
Thanks a lot!
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↧
Changing gstreamer pipeline to opencv in python
Trying to change this gstreamer pipline to python for opencv but having a few issues
gst-launch-1.0 -v playbin uri=rtsp://admin:password@192.168.1.65:554/Streaming/Channels/400 uridecodebin0::source::latency=10
This is the script I've referenced and managed to write, however I keep on getting this error
import cv2
import numpy as np
pipe = '"rtspsrc location=\"rtsp://admin:password@192.168.1.65:554/Streaming/Channels/400" latency=10 ! appsink'
cap = cv2.VideoCapture(pipe)
if not cap.isOpened():
print('VideoCapture not opened')
exit(0)
while True:
ret, frame = cap.read()
if not ret:
print('empty frame')
break
cv2.imshow('display', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
Error:
GStreamer-CRITICAL **: 09:06:55.638: gst_element_get_state: assertion 'GST_IS_ELEMENT (element)' failed
↧
How can I get the values of the pixels that are inside a contour?
I'm developing an application that allows the user to load an image, make a selection of a certain region (by drawing a random closed shape) and I want to have the values of the pixels that are inside this region but I'm having trouble doing so. All the functionality of drawing the shape on top of the image I've done it with PyQt and later the image with the draw on it is passed to a function where I want to analyze it with OpenCV.
Here is an example of the input image:
So basically I want to get the values of the pixels inside the red shape. I know OpenCV already has a ROI function, but unfortunately the ROI selection is a square shape and I can't make square selections. They have to be a shape drawn by the user.
↧
in here I get error for roi it is 'inconsistent use of tabs and spaces in indentation '
Code:
import numpy as np
import cv2
face_cascade = cv2.CascadeClassifier('cascades/data/haarcascade_frontalface_alt2.xml')
cap = cv2.VideoCapture(0)
while(True):
ret,frame = cap.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, scaleFactor=1.5, minNeighbors=5)
for (x, y, w, h) in faces:
print(x,y,w,h)
roi
cv2.imshow('frame',frame)
if cv2.waitKey(20) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
↧
4K VideoCapture very slow on Windows compared to MS Camera App
Hi!
I have gotten a Logitech BRIO Webcam, which delivers nice smooth 30 fps of 4k images in the native Windows Camera App. Using OpenCV 4.1.0 in Python, this seems impossible. The stream is very slow at the same exposure level. Here is my code:
import cv2
import time
print(cv2.getBuildInformation())
time_sum = 0
frames = 0
capture = cv2.VideoCapture()
capture.open(1 + cv2.CAP_DSHOW)
fourcc = cv2.VideoWriter_fourcc('M', 'J', 'P', 'G')
capture.set(cv2.CAP_PROP_FOURCC, fourcc)
capture.set(cv2.CAP_PROP_FPS, 30)
capture.set(cv2.CAP_PROP_FRAME_WIDTH, 3840)
capture.set(cv2.CAP_PROP_FRAME_HEIGHT, 2160)
while(1):
before_timer = time.perf_counter()
ret, frame = capture.read()
if frame is None:
print("Frame is empty")
break
else:
cv2.imshow('VIDEO', frame)
after_timer = time.perf_counter() - before_timer
time_sum += after_timer
frames += 1
if frames % 30 == 0:
print("{} per second".format(frames/time_sum))
cv2.waitKey(1)
Disabling the imshow does not have a significant impact on performance. Using CAP_MSMF as backend will deliver 30 FPS, but the video is just an upscaled version of a lower resolution stream, and sadly useless. I've been looking around the capture backend code for quite a while, but I can't seem to figure out where the problem lies.
When disabling the RGB conversion with capture.set(cv2.CAP_PROP_CONVERT_RGB, 0), the FPS also do not improve.
I also tried the same loop in C++, it was just as slow.
The relevant portion of getBuildInformation() looks like this:
Video I/O:
DC1394: NO
FFMPEG: YES (prebuilt binaries)
avcodec: YES (58.35.100)
avformat: YES (58.20.100)
avutil: YES (56.22.100)
swscale: YES (5.3.100)
avresample: YES (4.0.0)
GStreamer: NO
DirectShow: YES
Media Foundation: YES
DXVA: NO
Does anyone have any pointers on how to improve video performance? I am at a loss as to what to do. Hardware acceleration might be an option, but I don't know how to get that into my opencv-python package :(
Setting the DEBUG environment variable does not produce any additional logging info.
Thanks a lot for any help!
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Read image to an already allocated Mat
[modules/imgcodecs/src/loadsave.cpp](https://github.com/opencv/opencv/blob/f663e8f903645a3dd66f6833f63717b86e861d77/modules/imgcodecs/src/loadsave.cpp#L400) defines a function `imread_` that allows the caller to read image directly to some already existing Mat, potentially having an already allocated memory. However, neither the `cv::imread` in the C++ API, nor `cv2.imread` in Python API [seem to allow](https://docs.opencv.org/4.1.0/d4/da8/group__imgcodecs.html#ga288b8b3da0892bd651fce07b3bbd3a56) reading images this way, forcing the user to allocate a new buffer every time. I'm using OpenCV version 4.1.0.
If I am wrong and this is indeed possible, please let me know what should I read up on to get it to work. If not, how complex would it be to implement this functionality? I'm particularly interested in exposing it as a Python binding.
What I'm trying to do is to have a master process doing IO and several worker processes to do some heavy work on the images. To cut on the inter-process communication time, I'd like to allocate some amount of shared memory, where the master would put images it reads, allowing workers to access it. I reckon it would be faster if master could store images *directly* into this shared memory, instead of allocating a new buffer, reading to it, and copying data from this buffer to shared mem.
I've asked a [similar question on StackOverflow](https://stackoverflow.com/q/57161866/6919631).
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[Python] Real time image stabilization with Optical Flow
Hi! I'm new here on this forum, and would love some help with a project I'm working on!
I'm trying to make a small image stabilization programme in Python, but I can't get it to work the way I want.
First, my test programme:
from stabilizer import Stabilizer
import cv2
import sys
from imutils.video import VideoStream
import time
imageCapture = cv2.VideoCapture(0)
imageCapture.open(0)
time.sleep(2.0)
frame=0
counter=0
stabilizer=Stabilizer()
while True:
image=imageCapture.read()
frame, result=stabilizer.stabilize(image, frame)
cv2.imshow("Result", result)
cv2.imshow("Image", image[1])
key = cv2.waitKey(1) & 0xFF
# if the `q` key was pressed, break from the loop
if key == ord("q"):
break
counter+=1
print counter
print("[INFO] cleaning up...")
cv2.destroyAllWindows()
imageCapture.release()
...and this is my actual stabilization programme:
import numpy as np
import imutils
import cv2
class Stabilizer:
def stabilize(self,image, old_frame):
# params for ShiTomasi corner detection
feature_params = dict( maxCorners = 100,qualityLevel = 0.3,minDistance = 7,blockSize = 7 )
# Parameters for lucas kanade optical flow
lk_params = dict( winSize = (15,15),
maxLevel = 2,
criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))
# Create some random colors
color = np.random.randint(0,255,(100,3))
# Take first frame and find corners in it
try:
if old_frame==0:
ret, old_frame = image
except:
print("tull")
old_gray = cv2.cvtColor(old_frame, cv2.COLOR_BGR2GRAY)
p0 = cv2.goodFeaturesToTrack(old_gray, mask = None, **feature_params)
ret,frame = image
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# calculate optical flow
p1, st, err = cv2.calcOpticalFlowPyrLK(old_gray, frame_gray, p0, None, **lk_params)
# Select good points
good_new = p1[st==1]
good_old = p0[st==1]
# Make 3x3 matrix
h=cv2.findHomography(good_old,good_new)
#h=cv2.getPerspectiveTransform(good_old,good_new) #not working
# Now update the previous frame and previous points
#old_gray = frame_gray.copy()
#p0 = good_new.reshape(-1,1,2)
#cv2.destroyAllWindows()
result=cv2.warpPerspective(frame,h[0], (frame.shape[1],frame.shape[0]))
return frame, result
This is what I thought making this:
1. Catch one frame, finding points (p0) to match. The first time the old and new frame will be the same, but the next run it should be two different frames.
2. Calculate "Optical Flow" from these points.
3. Make 3x3 transformation matrix from this "Optical Flow"
4. Apply the transformation to the image
Is there any one who could help me with this one?
Thanks!
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I am getting an error "recognizer.train(x_train, np.array(y_labels)) TypeError: src is not a numpy array, neither a scalar"
program :
import os
from PIL import Image
import numpy as np
import cv2
import pickle
BASE_DIR=os.path.dirname(os.path.abspath(__file__))
image_dir=os.path.join(BASE_DIR,"photos")
face_cascade=cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
recognizer = cv2.face.LBPHFaceRecognizer_create()
current_id=0
label_ids={}
y_labels=[]
x_train=[]
for root,dirs,files in os.walk(image_dir):
for file in files:
if file.endswith("png") or file.endswith("jpg"):
path=os.path.join(root,file)
label=os.path.basename(root).replace(" ","-").lower()
if not label in label_ids:
label_ids[label]=current_id
current_id+=1
id_=label_ids[label]
y_labels.append(label) #some number
x_train.append(path) #verify this image,turn into a NUMPY array,GRAY
pil_image= Image.open(path).convert("L")#grayscale
image_array=np.array(pil_image,"uint8")
faces=face_cascade.detectMultiScale(image_array,1.5, 5)
for (x,y,w,h) in faces:
roi=image_array[y:y+h,x:x+w]
x_train.append(roi)
y_labels.append(id_)
with open("labels.pickle",'wb')as f:
pickle.dump(label_ids, f)
recognizer.train(x_train, np.array(y_labels))
recognizer.save("trainner.yml")
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