opencv3.0

I am writing a simple code of cropping images and saving it. But the problem is that the number of images is about 150,000+ and I want to improve the speed. So, at first I wrote a code with simple for loops, like the following: import cv2 import numpy import sys textfile=sys.argv[1] file_list=open(textfile) files=file_list.read().split('\n') idx=0 for eachfile in files: image=cv2.imread(eachfile) idx+=1 if image is None: pass outName=eachfile.replace('/data','/changed_data') if image.shape[0]==256: image1=image[120:170,120:170] elif image.shape[0]==50: image1=image cv2.imwrite(outName,image1)

When I try to import cv2 in IPython , I am getting this error- ImportError: dynamic module does not define module export function (PyInit_cv2) I have opencv 3.0, ubuntu 14.04, python 3.4. I searched online but couldn't get any answer. Julian Python3 libraries usually can be found in: /usr/lib/python3/dist-packages I found out that the library cv2.cpython-34m.so is needed for Python3.4. But this file wasn't be contained in my default library directory. In my environment, this library was stored in /usr/ local /lib/python3.4/dist-packages/ So I created a symbolic link. Try it with: cd /usr/lib

I am attempting to use the cv2 module to create a live scatter plot for each frame of a video. The code below does exactly that. However with more than 8 minute of footage containing more than 60000 frames to process, the code is not efficient and does take much longer than necessary to get the desired output. vidcap = cv2.VideoCapture(filepath) fig, ax = plt.subplots(1) plt.ion() x=df["time"][7:100] y=df["force"][7:100] for i in range(len(x)): vidcap.set(1,590) ret, image = vidcap.read() frameId = vidcap.get(1) plt.imshow(image,extent=[0,200,0,100], aspect='auto') plt.subplot(221) plt.plot(x

OpenCV3 introduced its T-API (Transparent API) which gives the user the possibility to use functions which are GPU (or other OpenCL enabled device) accelerated, I'm struggling to find how to tap into that with Python. With C++ there are calls like ocl::setUseOpenCL(true); that enable OpenCL acceleration when you use UMat instead of Mat objects. However I found no documentation whatsoever for Python. Does anybody have any sample code, links or guides on how to achieve OpenCL acceleration with OpenCV3 in Python? UPDATE: After some further digging I've found this in modules/core/include/opencv2

I'm trying to prepare images for OCR, and so far here is what I've done using info from Extracting text OpenCV From the resulting image I use the contours that have been filtered to make a mask as follow: //this is the mask of all the text Mat maskF = Mat::zeros(rgb.rows, rgb.cols, CV_8UC1); // CV_FILLED fills the connected components found - CV_FILLED to fill drawContours(maskF, letters, -1, Scalar(255), CV_FILLED); cv::imwrite("noise2-Mask.png", maskF); the resulting img is promising: considering this was my original img: Unfortunately running Tesseract on it yields some issues, I think the

I get the following error when I try to calibrate camera using cv2.calibrateCamera: rms, camera_matrix, dist_coefs, rvecs, tvecs = cv2.calibrateCamera(pts3d, pts2d, self.imgsize, None, None) cv2.error: /home/sarkar/opencv/opencv/modules/calib3d/src/calibration.cpp:2976: error: (-210) objectPoints should contain vector of vectors of points of type Point3f in function collectCalibrationData I initially had nx3 and nx2 array for pts3d and pts2d. I then tried to reshape pts3d and pts2d in the following form as the function takes vectors of vector point3d (and correspondingly pts2d) as input: [1 x