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view python/cvutils.py @ 915:13434f5017dd
work to save trajectory assignment to origin and destinations
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Tue, 04 Jul 2017 17:03:29 -0400 |
parents | 8f60ecfc2f06 |
children | a71455bd8367 |
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#! /usr/bin/env python '''Image/Video utilities''' import utils, moving 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 sys import stdout, maxint from os import listdir from copy import deepcopy from math import floor, log10, ceil 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 matplotlib.mlab import find from matplotlib.pyplot import imread, imsave #import aggdraw # 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 xrange(4): fourcc += unichr((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 xrange(cvmat.rows): for j in xrange(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 arrayToCvMat(a, t = cv2.CV_64FC1): '''Converts a numpy array to an OpenCV CvMat, with default type CV_64FC1.''' print('Deprecated, use new interface') cvmat = cv2.cv.CreateMat(a.shape[0], a.shape[1], t) for i in range(cvmat.rows): for j in range(cvmat.cols): cvmat[i,j] = a[i,j] return cvmat 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)' from cv2 import resize if rescale != 1.: size = (int(round(img.shape[1]*rescale)), int(round(img.shape[0]*rescale))) resizedImg = 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 = deepcopy(intrinsicCameraMatrix) newCameraMatrix[0,2] = newImgSize[0]/2. newCameraMatrix[1,2] = newImgSize[1]/2. return cv2.initUndistortRectifyMap(intrinsicCameraMatrix, array(distortionCoefficients), identity(3), newCameraMatrix, newImgSize, cv2.CV_32FC1) 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 xrange(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 xrange(len(captures)): captures[i].set(cv2.cv.CV_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) if array(rets).all(): if printFrames: print('frame shown {0}'.format(nFramesShown)) for i in xrange(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 xrange(len(captures)): captures.set(cv2.cv.CV_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.cv.CV_CAP_PROP_FORMAT: "format", cv2.cv.CV_CAP_PROP_FOURCC: "codec (fourcc)", cv2.cv.CV_CAP_PROP_FPS: "fps", cv2.cv.CV_CAP_PROP_FRAME_COUNT: "number of frames", cv2.cv.CV_CAP_PROP_FRAME_HEIGHT: "heigh", cv2.cv.CV_CAP_PROP_FRAME_WIDTH: "width", cv2.cv.CV_CAP_PROP_RECTIFICATION: "rectification", cv2.cv.CV_CAP_PROP_SATURATION: "saturation"} capture = cv2.VideoCapture(filename) if capture.isOpened(): for cvprop in [#cv2.cv.CV_CAP_PROP_BRIGHTNESS #cv2.cv.CV_CAP_PROP_CONTRAST #cv2.cv.CV_CAP_PROP_CONVERT_RGB #cv2.cv.CV_CAP_PROP_EXPOSURE cv2.cv.CV_CAP_PROP_FORMAT, cv2.cv.CV_CAP_PROP_FOURCC, cv2.cv.CV_CAP_PROP_FPS, cv2.cv.CV_CAP_PROP_FRAME_COUNT, cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, cv2.cv.CV_CAP_PROP_FRAME_WIDTH, #cv2.cv.CV_CAP_PROP_GAIN, #cv2.cv.CV_CAP_PROP_HUE #cv2.cv.CV_CAP_PROP_MODE #cv2.cv.CV_CAP_PROP_POS_AVI_RATIO #cv2.cv.CV_CAP_PROP_POS_FRAMES #cv2.cv.CV_CAP_PROP_POS_MSEC #cv2.cv.CV_CAP_PROP_RECTIFICATION, #cv2.cv.CV_CAP_PROP_SATURATION ]: prop = capture.get(cvprop) if cvprop == cv2.cv.CV_CAP_PROP_FOURCC and prop > 0: prop = int2FOURCC(int(prop)) print('Video {}: {}'.format(cvPropertyNames[cvprop], prop)) else: print('Video capture for {} failed'.format(filename)) 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.cv.CV_CAP_PROP_FRAME_COUNT) if rawCount < 0: rawCount = lastFrameNum+1 nDigits = int(floor(log10(rawCount)))+1 ret = False capture.set(cv2.cv.CV_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.cv.CV_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.cv.CV_CAP_PROP_FPS) capture.release() return fps else: print('Video capture for {} failed'.format(videoFilename)) return None def imageBoxSize(obj, frameNum, homography, 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): projectedPosition = f.getPositionAtInstant(frameNum).project(homography) x.append(projectedPosition.x) y.append(projectedPosition.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, homography, 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, homography, 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 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.cv.CV_CAP_PROP_FRAME_WIDTH)) height = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT)) windowName = 'frame' if rescale == 1.: cv2.namedWindow(windowName, cv2.WINDOW_NORMAL) if undistort: # setup undistortion [map1, map2] = computeUndistortMaps(width, height, undistortedImageMultiplication, intrinsicCameraMatrix, distortionCoefficients) if capture.isOpened(): key = -1 ret = True frameNum = firstFrameNum capture.set(cv2.cv.CV_CAP_PROP_POS_FRAMES, firstFrameNum) if lastFrameNumArg is None: lastFrameNum = maxint else: lastFrameNum = lastFrameNumArg if nZerosFilenameArg is None: 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'): if homography is not None: obj.projectedPositions = obj.positions.project(homography) else: obj.projectedPositions = obj.positions cvPlot(img, obj.projectedPositions, cvColors[colorType][obj.getNum()], frameNum-obj.getFirstInstant()) if frameNum not in boundingBoxes.keys() 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.keys(): 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.cv.CV_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 cameraIntrinsicCalibration(path, checkerBoardSize=[6,7], secondPassSearch=False, display=False): ''' 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: img = 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: ret, camera_matrix, dist_coeffs, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None) except NameError: return None savetxt('intrinsic-camera.txt', camera_matrix) 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 printCvMat(cvmat, out = stdout): '''Prints the cvmat to out''' print('Deprecated, use new interface') for i in xrange(cvmat.rows): for j in xrange(cvmat.cols): out.write('{0} '.format(cvmat[i,j])) out.write('\n') def projectArray(homography, points): '''Returns the coordinates of the projected points through homography (format: array 2xN points)''' if points.shape[0] != 2: raise Exception('points of dimension {0} {1}'.format(points.shape[0], points.shape[1])) if (homography is not None) and homography.size>0: #alternatively, on could use cv2.convertpointstohomogeneous and other conversion to/from homogeneous coordinates augmentedPoints = append(points,[[1]*points.shape[1]], 0) prod = dot(homography, augmentedPoints) return prod[0:2]/prod[2] else: return points def project(homography, p): '''Returns the coordinates of the projection of the point p with coordinates p[0], p[1] through homography''' return projectArray(homography, array([[p[0]],[p[1]]])) def projectTrajectory(homography, trajectory): '''Projects a series of points in the format [[x1, x2, ...], [y1, y2, ...]]''' return projectArray(homography, array(trajectory)) def invertHomography(homography): '''Returns an inverted homography Unnecessary for reprojection over camera image''' from numpy.linalg import inv invH = inv(homography) invH /= invH[2,2] return invH def undistortTrajectory(invMap1, invMap2, positions): floorPositions = npfloor(positions) #ceilPositions = npceil(positions) undistortedTrajectory = [[],[]] for i in xrange(len(positions[0])): x,y = None, None if positions[0][i]+1 < invMap1.shape[1] and positions[1][i]+1 < invMap1.shape[0]: floorX = invMap1[floorPositions[1][i], floorPositions[0][i]] floorY = invMap2[floorPositions[1][i], floorPositions[0][i]] ceilX = invMap1[floorPositions[1][i]+1, floorPositions[0][i]+1] ceilY = invMap2[floorPositions[1][i]+1, floorPositions[0][i]+1] #ceilX = invMap1[ceilPositions[1][i], ceilPositions[0][i]] #ceilY = invMap2[ceilPositions[1][i], ceilPositions[0][i]] if floorX >=0 and floorY >=0 and ceilX >=0 and ceilY >=0: x = floorX+(positions[0][i]-floorPositions[0][i])*(ceilX-floorX) y = floorY+(positions[1][i]-floorPositions[1][i])*(ceilY-floorY) undistortedTrajectory[0].append(x) undistortedTrajectory[1].append(y) return undistortedTrajectory def projectGInputPoints(homography, points): return projectTrajectory(homography, array(points+[points[0]]).T) 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, normalize=False): bwImg = color.rgb2gray(image) inputImg = transform.resize(bwImg, rescaleSize) features = hog(inputImg, orientations, pixelsPerCell, cellsPerBlock, blockNorm, visualize, normalize) if visualize: from matplotlib.pyplot import imshow, figure, subplot 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, normalize=False): inputData = [] for filename in listdir(imageDirectory): img = imread(imageDirectory+filename) features = HOG(img, rescaleSize, orientations, pixelsPerCell, cellsPerBlock, blockNorm, visualize, normalize) 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")