Mercurial Hosting > traffic-intelligence
view trafficintelligence/cvutils.py @ 1054:d13f9bfbf3ff
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author | Wendlasida |
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date | Fri, 06 Jul 2018 18:42:58 -0400 |
parents | c6cf75a2ed08 |
children | 956a66096e91 |
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#! /usr/bin/env python '''Image/Video utilities''' from sys import stdout from os import listdir from subprocess import run from math import floor, log10, ceil from time import time from numpy import dot, array, append, float32, loadtxt, savetxt, append, zeros, ones, identity, abs as npabs, logical_and, unravel_index, sum as npsum, isnan, mgrid, median, floor as npfloor, ceil as npceil from numpy.linalg import inv from matplotlib.mlab import find from matplotlib.pyplot import imread, imsave, imshow, figure, subplot try: import cv2 opencvAvailable = True except ImportError: print('OpenCV library could not be loaded (video replay functions will not be available)') # TODO change to logging module opencvAvailable = False try: import skimage skimageAvailable = True except ImportError: print('Scikit-image library could not be loaded (HoG-based classification methods will not be available)') skimageAvailable = False from trafficintelligence import utils, moving videoFilenameExtensions = ['mov', 'avi', 'mp4', 'MOV', 'AVI', 'MP4'] trackerExe = 'feature-based-tracking' #importaggdraw # agg on top of PIL (antialiased drawing) cvRed = {'default': (0,0,255), 'colorblind': (0,114,178)} cvGreen = {'default': (0,255,0), 'colorblind': (0,158,115)} cvBlue = {'default': (255,0,0), 'colorblind': (213,94,0)} cvCyan = {'default': (255, 255, 0), 'colorblind': (240,228,66)} cvYellow = {'default': (0, 255, 255), 'colorblind': (86,180,233)} cvMagenta = {'default': (255, 0, 255), 'colorblind': (204,121,167)} cvWhite = {k: (255, 255, 255) for k in ['default', 'colorblind']} cvBlack = {k: (0,0,0) for k in ['default', 'colorblind']} cvColors3 = {k: utils.PlottingPropertyValues([cvRed[k], cvGreen[k], cvBlue[k]]) for k in ['default', 'colorblind']} cvColors = {k: utils.PlottingPropertyValues([cvRed[k], cvGreen[k], cvBlue[k], cvCyan[k], cvYellow[k], cvMagenta[k], cvWhite[k], cvBlack[k]]) for k in ['default', 'colorblind']} def quitKey(key): return chr(key&255)== 'q' or chr(key&255) == 'Q' def saveKey(key): return chr(key&255) == 's' def int2FOURCC(x): fourcc = '' for i in range(4): fourcc += chr((x >> 8*i)&255) return fourcc def rgb2gray(rgb): return dot(rgb[...,:3], [0.299, 0.587, 0.144]) def matlab2PointCorrespondences(filename): '''Loads and converts the point correspondences saved by the matlab camera calibration tool''' points = loadtxt(filename, delimiter=',') savetxt(utils.removeExtension(filename)+'-point-correspondences.txt',append(points[:,:2].T, points[:,3:].T, axis=0)) def loadPointCorrespondences(filename): '''Loads and returns the corresponding points in world (first 2 lines) and image spaces (last 2 lines)''' points = loadtxt(filename, dtype=float32) return (points[:2,:].T, points[2:,:].T) # (world points, image points) def cvMatToArray(cvmat): '''Converts an OpenCV CvMat to numpy array.''' print('Deprecated, use new interface') a = zeros((cvmat.rows, cvmat.cols))#array([[0.0]*cvmat.width]*cvmat.height) for i in range(cvmat.rows): for j in range(cvmat.cols): a[i,j] = cvmat[i,j] return a def createWhiteImage(height, width, filename): img = ones((height, width, 3), uint8)*255 imsave(filename, img) if opencvAvailable: def computeHomography(srcPoints, dstPoints, method=0, ransacReprojThreshold=3.0): '''Returns the homography matrix mapping from srcPoints to dstPoints (dimension Nx2)''' H, mask = cv2.findHomography(srcPoints, dstPoints, method, ransacReprojThreshold) return H def cvPlot(img, positions, color, lastCoordinate = None, **kwargs): if lastCoordinate is None: last = positions.length()-1 elif lastCoordinate >=0: last = min(positions.length()-1, lastCoordinate) for i in range(0, last): cv2.line(img, positions[i].asint().astuple(), positions[i+1].asint().astuple(), color, **kwargs) def cvImshow(windowName, img, rescale = 1.0): 'Rescales the image (in particular if too large)' if rescale != 1.: size = (int(round(img.shape[1]*rescale)), int(round(img.shape[0]*rescale))) resizedImg = cv2.resize(img, size) cv2.imshow(windowName, resizedImg) else: cv2.imshow(windowName, img) def computeUndistortMaps(width, height, undistortedImageMultiplication, intrinsicCameraMatrix, distortionCoefficients): newImgSize = (int(round(width*undistortedImageMultiplication)), int(round(height*undistortedImageMultiplication))) newCameraMatrix = cv2.getDefaultNewCameraMatrix(intrinsicCameraMatrix, newImgSize, True) return cv2.initUndistortRectifyMap(intrinsicCameraMatrix, array(distortionCoefficients), None, newCameraMatrix, newImgSize, cv2.CV_32FC1), newCameraMatrix def playVideo(filenames, windowNames = None, firstFrameNums = None, frameRate = -1, interactive = False, printFrames = True, text = None, rescale = 1., step = 1, colorBlind = False): '''Plays the video(s)''' if colorBlind: colorType = 'colorblind' else: colorType = 'default' if len(filenames) == 0: print('Empty filename list') return if windowNames is None: windowNames = ['frame{}'.format(i) for i in range(len(filenames))] wait = 5 if rescale == 1.: for windowName in windowNames: cv2.namedWindow(windowName, cv2.WINDOW_NORMAL) if frameRate > 0: wait = int(round(1000./frameRate)) if interactive: wait = 0 captures = [cv2.VideoCapture(fn) for fn in filenames] if array([cap.isOpened() for cap in captures]).all(): key = -1 ret = True nFramesShown = 0 if firstFrameNums is not None: for i in range(len(captures)): captures[i].set(cv2.CAP_PROP_POS_FRAMES, firstFrameNums[i]) while ret and not quitKey(key): rets = [] images = [] for cap in captures: ret, img = cap.read() rets.append(ret) images.append(img) ret = array(rets).all() if ret: if printFrames: print('frame shown {0}'.format(nFramesShown)) for i in range(len(filenames)): if text is not None: cv2.putText(images[i], text, (10,50), cv2.FONT_HERSHEY_PLAIN, 1, cvRed[colorType]) cvImshow(windowNames[i], images[i], rescale) # cv2.imshow('frame', img) key = cv2.waitKey(wait) if saveKey(key): cv2.imwrite('image-{}.png'.format(frameNum), img) nFramesShown += step if step > 1: for i in range(len(captures)): captures[i].set(cv2.CAP_PROP_POS_FRAMES, firstFrameNums[i]+nFramesShown) cv2.destroyAllWindows() else: print('Video captures for {} failed'.format(filenames)) def infoVideo(filename): '''Provides all available info on video ''' cvPropertyNames = {cv2.CAP_PROP_FORMAT: "format", cv2.CAP_PROP_FOURCC: "codec (fourcc)", cv2.CAP_PROP_FPS: "fps", cv2.CAP_PROP_FRAME_COUNT: "number of frames", cv2.CAP_PROP_FRAME_HEIGHT: "heigh", cv2.CAP_PROP_FRAME_WIDTH: "width", cv2.CAP_PROP_RECTIFICATION: "rectification", cv2.CAP_PROP_SATURATION: "saturation"} capture = cv2.VideoCapture(filename) videoProperties = {} if capture.isOpened(): for cvprop in [#cv2.CAP_PROP_BRIGHTNESS #cv2.CAP_PROP_CONTRAST #cv2.CAP_PROP_CONVERT_RGB #cv2.CAP_PROP_EXPOSURE cv2.CAP_PROP_FORMAT, cv2.CAP_PROP_FOURCC, cv2.CAP_PROP_FPS, cv2.CAP_PROP_FRAME_COUNT, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FRAME_WIDTH, #cv2.CAP_PROP_GAIN, #cv2.CAP_PROP_HUE #cv2.CAP_PROP_MODE #cv2.CAP_PROP_POS_AVI_RATIO #cv2.CAP_PROP_POS_FRAMES #cv2.CAP_PROP_POS_MSEC #cv2.CAP_PROP_RECTIFICATION, #cv2.CAP_PROP_SATURATION ]: prop = capture.get(cvprop) if cvprop == cv2.CAP_PROP_FOURCC and prop > 0: prop = int2FOURCC(int(prop)) videoProperties[cvPropertyNames[cvprop]] = prop else: print('Video capture for {} failed'.format(filename)) return videoProperties def getImagesFromVideo(videoFilename, firstFrameNum = 0, lastFrameNum = 1, step = 1, saveImage = False, outputPrefix = 'image'): '''Returns nFrames images from the video sequence''' images = [] capture = cv2.VideoCapture(videoFilename) if capture.isOpened(): rawCount = capture.get(cv2.CAP_PROP_FRAME_COUNT) if rawCount < 0: rawCount = lastFrameNum+1 nDigits = int(floor(log10(rawCount)))+1 ret = False capture.set(cv2.CAP_PROP_POS_FRAMES, firstFrameNum) frameNum = firstFrameNum while frameNum<lastFrameNum and frameNum<rawCount: ret, img = capture.read() i = 0 while not ret and i<10: ret, img = capture.read() i += 1 if img is not None and img.size>0: if saveImage: frameNumStr = format(frameNum, '0{}d'.format(nDigits)) cv2.imwrite(outputPrefix+frameNumStr+'.png', img) else: images.append(img) frameNum +=step if step > 1: capture.set(cv2.CAP_PROP_POS_FRAMES, frameNum) capture.release() else: print('Video capture for {} failed'.format(videoFilename)) return images def getFPS(videoFilename): capture = cv2.VideoCapture(videoFilename) if capture.isOpened(): fps = capture.get(cv2.CAP_PROP_FPS) capture.release() return fps else: print('Video capture for {} failed'.format(videoFilename)) return None def imageBoxSize(obj, frameNum, width, height, px = 0.2, py = 0.2): 'Computes the bounding box size of object at frameNum' x = [] y = [] if obj.hasFeatures(): for f in obj.getFeatures(): if f.existsAtInstant(frameNum): p = f.getPositionAtInstant(frameNum) x.append(p.x) y.append(p.y) xmin = min(x) xmax = max(x) ymin = min(y) ymax = max(y) xMm = px * (xmax - xmin) yMm = py * (ymax - ymin) a = max(ymax - ymin + (2 * yMm), xmax - (xmin + 2 * xMm)) yCropMin = int(max(0, .5 * (ymin + ymax - a))) yCropMax = int(min(height - 1, .5 * (ymin + ymax + a))) xCropMin = int(max(0, .5 * (xmin + xmax - a))) xCropMax = int(min(width - 1, .5 * (xmin + xmax + a))) return yCropMin, yCropMax, xCropMin, xCropMax def imageBox(img, obj, frameNum, width, height, px = 0.2, py = 0.2, minNPixels = 800): 'Computes the bounding box of object at frameNum' yCropMin, yCropMax, xCropMin, xCropMax = imageBoxSize(obj, frameNum, width, height, px, py) if yCropMax != yCropMin and xCropMax != xCropMin and (yCropMax - yCropMin) * (xCropMax - xCropMin) > minNPixels: return img[yCropMin : yCropMax, xCropMin : xCropMax] else: return None def tracking(configFilename, grouping, videoFilename = None, dbFilename = None, homographyFilename = None, maskFilename = None, undistort = False, intrinsicCameraMatrix = None, distortionCoefficients = None, dryRun = False): '''Runs the tracker in a subprocess if grouping is True, it is feature grouping otherwise it is feature tracking''' if grouping: trackingMode = '--gf' else: trackingMode = '--tf' cmd = [trackerExe, configFilename, trackingMode, '--quiet'] if videoFilename is not None: cmd += ['--video-filename', videoFilename] if dbFilename is not None: cmd += ['--database-filename', dbFilename] if homographyFilename is not None: cmd += ['--homography-filename', homographyFilename] if maskFilename is not None: cmd += ['--mask-filename', maskFilename] if undistort: cmd += ['--undistort', 'true'] if intrinsicCameraMatrix is not None: # we currently have to save a file intrinsicCameraFilename = '/tmp/intrinsic-{}.txt'.format(time()) savetxt(intrinsicCameraFilename, intrinsicCameraMatrix) cmd += ['--intrinsic-camera-filename', intrinsicCameraFilename] if distortionCoefficients is not None: cmd += ['--distortion-coefficients '+' '.join([str(x) for x in distortionCoefficients])] if dryRun: print(cmd) else: run(cmd) def displayTrajectories(videoFilename, objects, boundingBoxes = {}, homography = None, firstFrameNum = 0, lastFrameNumArg = None, printFrames = True, rescale = 1., nFramesStep = 1, saveAllImages = False, nZerosFilenameArg = None, undistort = False, intrinsicCameraMatrix = None, distortionCoefficients = None, undistortedImageMultiplication = 1., annotations = [], gtMatches = {}, toMatches = {}, colorBlind = False): '''Displays the objects overlaid frame by frame over the video ''' if colorBlind: colorType = 'colorblind' else: colorType = 'default' capture = cv2.VideoCapture(videoFilename) width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH)) height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT)) windowName = 'frame' if rescale == 1.: cv2.namedWindow(windowName, cv2.WINDOW_NORMAL) if undistort: # setup undistortion [map1, map2], newCameraMatrix = computeUndistortMaps(width, height, undistortedImageMultiplication, intrinsicCameraMatrix, distortionCoefficients) if capture.isOpened(): key = -1 ret = True frameNum = firstFrameNum capture.set(cv2.CAP_PROP_POS_FRAMES, firstFrameNum) if lastFrameNumArg is None: lastFrameNum = float("inf") else: lastFrameNum = lastFrameNumArg if nZerosFilenameArg is None: if lastFrameNumArg is None: nZerosFilename = int(ceil(log10(objects[-1].getLastInstant()))) else: nZerosFilename = int(ceil(log10(lastFrameNum))) else: nZerosFilename = nZerosFilenameArg while ret and not quitKey(key) and frameNum <= lastFrameNum: ret, img = capture.read() if ret: if undistort: img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR) if printFrames: print('frame {0}'.format(frameNum)) # plot objects for obj in objects[:]: if obj.existsAtInstant(frameNum): if not hasattr(obj, 'projectedPositions'): obj.projectedPositions = obj.getPositions().homographyProject(homography) if undistort: obj.projectedPositions = obj.projectedPositions.newCameraProject(newCameraMatrix) cvPlot(img, obj.projectedPositions, cvColors[colorType][obj.getNum()], frameNum-obj.getFirstInstant()) if frameNum not in boundingBoxes and obj.hasFeatures(): yCropMin, yCropMax, xCropMin, xCropMax = imageBoxSize(obj, frameNum, homography, width, height) cv2.rectangle(img, (xCropMin, yCropMin), (xCropMax, yCropMax), cvBlue[colorType], 1) objDescription = '{} '.format(obj.num) if moving.userTypeNames[obj.userType] != 'unknown': objDescription += moving.userTypeNames[obj.userType][0].upper() if len(annotations) > 0: # if we loaded annotations, but there is no match if frameNum not in toMatches[obj.getNum()]: objDescription += " FA" cv2.putText(img, objDescription, obj.projectedPositions[frameNum-obj.getFirstInstant()].asint().astuple(), cv2.FONT_HERSHEY_PLAIN, 1, cvColors[colorType][obj.getNum()]) if obj.getLastInstant() == frameNum: objects.remove(obj) # plot object bounding boxes if frameNum in boundingBoxes: for rect in boundingBoxes[frameNum]: cv2.rectangle(img, rect[0].asint().astuple(), rect[1].asint().astuple(), cvColors[colorType][obj.getNum()]) # plot ground truth if len(annotations) > 0: for gt in annotations: if gt.existsAtInstant(frameNum): if frameNum in gtMatches[gt.getNum()]: color = cvColors[colorType][gtMatches[gt.getNum()][frameNum]] # same color as object else: color = cvRed[colorType] cv2.putText(img, 'Miss', gt.topLeftPositions[frameNum-gt.getFirstInstant()].asint().astuple(), cv2.FONT_HERSHEY_PLAIN, 1, color) cv2.rectangle(img, gt.topLeftPositions[frameNum-gt.getFirstInstant()].asint().astuple(), gt.bottomRightPositions[frameNum-gt.getFirstInstant()].asint().astuple(), color) # saving images and going to next if not saveAllImages: cvImshow(windowName, img, rescale) key = cv2.waitKey() if saveAllImages or saveKey(key): cv2.imwrite('image-{{:0{}}}.png'.format(nZerosFilename).format(frameNum), img) frameNum += nFramesStep if nFramesStep > 1: capture.set(cv2.CAP_PROP_POS_FRAMES, frameNum) cv2.destroyAllWindows() else: print('Cannot load file ' + videoFilename) def computeHomographyFromPDTV(camera): '''Returns the homography matrix at ground level from PDTV camera https://bitbucket.org/hakanardo/pdtv''' # camera = pdtv.load(cameraFilename) srcPoints = [[x,y] for x, y in zip([1.,2.,2.,1.],[1.,1.,2.,2.])] # need floats!! dstPoints = [] for srcPoint in srcPoints: projected = camera.image_to_world(tuple(srcPoint)) dstPoints.append([projected[0], projected[1]]) H, mask = cv2.findHomography(array(srcPoints), array(dstPoints), method = 0) # No need for different methods for finding homography return H def getIntrinsicCameraMatrix(cameraData): return array([[cameraData['f']*cameraData['Sx']/cameraData['dx'], 0, cameraData['Cx']], [0, cameraData['f']/cameraData['dy'], cameraData['Cy']], [0, 0, 1.]]) def getDistortionCoefficients(cameraData): return array([cameraData['k']]+4*[0]) def undistortedCoordinates(map1, map2, x, y, maxDistance = 1.): '''Returns the coordinates of a point in undistorted image map1 and map2 are the mapping functions from undistorted image to distorted (original image) map1(x,y) = originalx, originaly''' distx = npabs(map1-x) disty = npabs(map2-y) indices = logical_and(distx<maxDistance, disty<maxDistance) closeCoordinates = unravel_index(find(indices), distx.shape) # returns i,j, ie y,x xWeights = 1-distx[indices] yWeights = 1-disty[indices] return dot(xWeights, closeCoordinates[1])/npsum(xWeights), dot(yWeights, closeCoordinates[0])/npsum(yWeights) def undistortTrajectoryFromCVMapping(map1, map2, t): '''test 'perfect' inversion''' undistortedTrajectory = moving.Trajectory() for i,p in enumerate(t): res = undistortedCoordinates(map1, map2, p.x,p.y) if not isnan(res).any(): undistortedTrajectory.addPositionXY(res[0], res[1]) else: print('{} {} {}'.format(i,p,res)) return undistortedTrajectory def computeInverseMapping(originalImageSize, map1, map2): 'Computes inverse mapping from maps provided by cv2.initUndistortRectifyMap' invMap1 = -ones(originalImageSize) invMap2 = -ones(originalImageSize) for x in range(0,originalImageSize[1]): for y in range(0,originalImageSize[0]): res = undistortedCoordinates(x,y, map1, map2) if not isnan(res).any(): invMap1[y,x] = res[0] invMap2[y,x] = res[1] return invMap1, invMap2 def intrinsicCameraCalibration(path, checkerBoardSize=[6,7], secondPassSearch=False, display=False, fixK2 = True, fixK3 = True, zeroTangent = True): ''' Camera calibration searches through all the images (jpg or png) located in _path_ for matches to a checkerboard pattern of size checkboardSize. These images should all be of the same camera with the same resolution. For best results, use an asymetric board and ensure that the image has very high contrast, including the background. cherckerBoardSize is the number of internal corners (7x10 squares have 6x9 internal corners) The code below is based off of: https://opencv-python-tutroals.readthedocs.org/en/latest/py_tutorials/py_calib3d/py_calibration/py_calibration.html Modified by Paul St-Aubin ''' import glob, os # termination criteria criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001) # prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0) objp = zeros((checkerBoardSize[0]*checkerBoardSize[1],3), float32) objp[:,:2] = mgrid[0:checkerBoardSize[1],0:checkerBoardSize[0]].T.reshape(-1,2) # Arrays to store object points and image points from all the images. objpoints = [] # 3d point in real world space imgpoints = [] # 2d points in image plane. ## Loop throuhg all images in _path_ images = glob.glob(os.path.join(path,'*.[jJ][pP][gG]'))+glob.glob(os.path.join(path,'*.[jJ][pP][eE][gG]'))+glob.glob(os.path.join(path,'*.[pP][nN][gG]')) for fname in images: img = cv2.imread(fname) gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # Find the chess board corners ret, corners = cv2.findChessboardCorners(gray, (checkerBoardSize[1],checkerBoardSize[0]), None) # If found, add object points, image points (after refining them) if ret: print('Found pattern in '+fname) if secondPassSearch: corners = cv2.cornerSubPix(gray, corners, (11,11), (-1,-1), criteria) objpoints.append(objp) imgpoints.append(corners) # Draw and display the corners if display: cv2.drawChessboardCorners(img, (checkerBoardSize[1],checkerBoardSize[0]), corners, ret) if img is not None: cv2.imshow('img',img) cv2.waitKey(0) else: print('Pattern not found in '+fname) ## Close up image loading and calibrate cv2.destroyAllWindows() if len(objpoints) == 0 or len(imgpoints) == 0: return None try: flags = 0 if fixK2: flags += cv2.CALIB_FIX_K2 if fixK3: flags += cv2.CALIB_FIX_K3 if zeroTangent: flags += cv2.CALIB_ZERO_TANGENT_DIST ret, camera_matrix, dist_coeffs, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None, flags = flags) except NameError: return None savetxt('intrinsic-camera.txt', camera_matrix) print('error: {}'.format(ret)) return camera_matrix, dist_coeffs def undistortImage(img, intrinsicCameraMatrix = None, distortionCoefficients = None, undistortedImageMultiplication = 1., interpolation=cv2.INTER_LINEAR): '''Undistorts the image passed in argument''' width = img.shape[1] height = img.shape[0] [map1, map2] = computeUndistortMaps(width, height, undistortedImageMultiplication, intrinsicCameraMatrix, distortionCoefficients) return cv2.remap(img, map1, map2, interpolation=interpolation) def homographyProject(points, homography, output3D = False): '''Returns the coordinates of the points (2xN array) projected through homography''' if points.shape[0] != 2: raise Exception('points of dimension {}'.format(points.shape)) if homography is not None and homography.size>0: if output3D: outputDim = 3 else: outputDim = 2 augmentedPoints = append(points,[[1]*points.shape[1]], 0) # 3xN prod = dot(homography, augmentedPoints) return prod[:outputDim,:]/prod[2] elif output3D: return append(points,[[1]*points.shape[1]], 0) # 3xN else: return points def imageToWorldProject(points, intrinsicCameraMatrix = None, distortionCoefficients = None, homography = None): '''Projects points (2xN array) from image (video) space to world space 1. through undistorting if provided by intrinsic camera matrix and distortion coefficients 2. through homograph projection (from ideal point (no camera) to world)''' if points.shape[0] != 2: raise Exception('points of dimension {}'.format(points.shape)) if intrinsicCameraMatrix is not None and distortionCoefficients is not None: undistortedPoints = cv2.undistortPoints(points.T.reshape(1,points.shape[1], 2), intrinsicCameraMatrix, distortionCoefficients).reshape(-1,2) return homographyProject(undistortedPoints.T, homography) else: return homographyProject(points, homography) def worldToImageProject(points, intrinsicCameraMatrix = None, distortionCoefficients = None, homography = None): '''Projects points (2xN array) from image (video) space to world space 1. through undistorting if provided by intrinsic camera matrix and distortion coefficients 2. through homograph projection (from ideal point (no camera) to world)''' if points.shape[0] != 2: raise Exception('points of dimension {}'.format(points.shape)) if intrinsicCameraMatrix is not None and distortionCoefficients is not None: projected3D = homographyProject(points, homography, True) projected, jacobian = cv2.projectPoints(projected3D.T, (0.,0.,0.), (0.,0.,0.), intrinsicCameraMatrix, distortionCoefficients) # in: 3xN, out: 2x1xN return projected.reshape(-1,2).T else: return homographyProject(points, homography) def newCameraProject(points, newCameraMatrix): '''Projects points (2xN array) as if seen by camera (or reverse by inverting the camera matrix)''' if points.shape[0] != 2: raise Exception('points of dimension {}'.format(points.shape)) if newCameraMatrix is not None: augmentedPoints = append(points,[[1]*points.shape[1]], 0) # 3xN projected = dot(newCameraMatrix, augmentedPoints) return projected[:2,:] else: return points if opencvAvailable: def computeTranslation(img1, img2, img1Points, maxTranslation2, minNMatches, windowSize = (5,5), level = 5, criteria = (cv2.TERM_CRITERIA_EPS, 0, 0.01)): '''Computes the translation of img2 with respect to img1 (loaded using OpenCV as numpy arrays) img1Points are used to compute the translation TODO add diagnostic if data is all over the place, and it most likely is not a translation (eg zoom, other non linear distortion)''' nextPoints = array([]) (img2Points, status, track_error) = cv2.calcOpticalFlowPyrLK(img1, img2, img1Points, nextPoints, winSize=windowSize, maxLevel=level, criteria=criteria) # calcOpticalFlowPyrLK(prevImg, nextImg, prevPts[, nextPts[, status[, err[, winSize[, maxLevel[, criteria[, derivLambda[, flags]]]]]]]]) -> nextPts, status, err delta = [] for (k, (p1,p2)) in enumerate(zip(img1Points, img2Points)): if status[k] == 1: dp = p2-p1 d = npsum(dp**2) if d < maxTranslation2: delta.append(dp) if len(delta) >= minNMatches: return median(delta, axis=0) else: print(dp) return None if skimageAvailable: from skimage.feature import hog from skimage import color, transform def HOG(image, rescaleSize = (64, 64), orientations = 9, pixelsPerCell = (8,8), cellsPerBlock = (2,2), blockNorm = 'L1', visualize = False, transformSqrt = False): bwImg = color.rgb2gray(image) inputImg = transform.resize(bwImg, rescaleSize) features = hog(inputImg, orientations, pixelsPerCell, cellsPerBlock, blockNorm, visualize, transformSqrt, True) if visualize: hogViz = features[1] features = features[0] figure() subplot(1,2,1) imshow(inputImg) subplot(1,2,2) imshow(hogViz) return float32(features) def createHOGTrainingSet(imageDirectory, classLabel, rescaleSize = (64,64), orientations = 9, pixelsPerCell = (8,8), blockNorm = 'L1', cellsPerBlock = (2, 2), visualize = False, transformSqrt = False): inputData = [] for filename in listdir(imageDirectory): img = imread(imageDirectory+filename) features = HOG(img, rescaleSize, orientations, pixelsPerCell, cellsPerBlock, blockNorm, visualize, transformSqrt) inputData.append(features) nImages = len(inputData) return array(inputData, dtype = float32), array([classLabel]*nImages) ######################### # running tests ######################### if __name__ == "__main__": import doctest import unittest suite = doctest.DocFileSuite('tests/cvutils.txt') #suite = doctest.DocTestSuite() unittest.TextTestRunner().run(suite) #doctest.testmod() #doctest.testfile("example.txt")