Mercurial Hosting > traffic-intelligence
view python/cvutils.py @ 302:9d88a4d97473
corrected bug in compute-homography
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Mon, 18 Mar 2013 23:37:45 -0400 |
parents | aba9711b3149 |
children | 514f6b98cd8c |
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#! /usr/bin/env python '''Image/Video utilities''' import Image, ImageDraw # PIL try: import cv2 opencvExists = True except ImportError: print('OpenCV library could not be loaded') opencvExists = False from sys import stdout import utils #import aggdraw # agg on top of PIL (antialiased drawing) #import utils __metaclass__ = type cvRed = (0,0,255) cvGreen = (0,255,0) cvBlue = (255,0,0) cvColors = utils.PlottingPropertyValues([cvRed, cvGreen, cvBlue]) cvKeyNumbers = {'a':1048673, 'n': 1048686, 'y': 1048697} def drawLines(filename, origins, destinations, w = 1, resultFilename='image.png'): '''Draws lines over the image ''' img = Image.open(filename) draw = ImageDraw.Draw(img) #draw = aggdraw.Draw(img) #pen = aggdraw.Pen("red", width) for p1, p2 in zip(origins, destinations): draw.line([p1.x, p1.y, p2.x, p2.y], width = w, fill = (256,0,0)) #draw.line([p1.x, p1.y, p2.x, p2.y], pen) del draw #out = utils.openCheck(resultFilename) img.save(resultFilename) def matlab2PointCorrespondences(filename): '''Loads and converts the point correspondences saved by the matlab camera calibration tool''' from numpy.lib.io import loadtxt, savetxt from numpy.lib.function_base import append 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)''' from numpy.lib.npyio import loadtxt from numpy import float32 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.''' from numpy.core.multiarray import zeros 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 if opencvExists: def computeHomography(srcPoints, dstPoints, method=0, ransacReprojThreshold=0.0): '''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.CV_64FC1): '''Converts a numpy array to an OpenCV CvMat, with default type CV_64FC1.''' 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 draw(img, positions, color, lastCoordinate = None): last = lastCoordinate+1 if lastCoordinate != None and lastCoordinate >=0: last = min(positions.length()-1, lastCoordinate) for i in range(0, last-1): cv2.line(img, positions[i].asint().astuple(), positions[i+1].asint().astuple(), color) def playVideo(filename, firstFrameNum = 0): '''Plays the video''' capture = cv2.VideoCapture(filename) if capture.isOpened(): key = -1 ret = True frameNum = firstFrameNum capture.set(cv2.cv.CV_CAP_PROP_POS_FRAMES, firstFrameNum) while ret and key!= 113: # 'q' ret, img = capture.read() if ret: print('frame {0}'.format(frameNum)) frameNum+=1 cv2.imshow('frame', img) key = cv2.waitKey(5) def getImagesFromVideo(filename, nImages = 1, saveImage = False): '''Returns nImages images from the video sequence''' images = [] capture = cv2.VideoCapture(filename) if capture.isOpened(): ret = False numImg = 0 while numImg<nImages: ret, img = capture.read() i = 0 while not ret and i<10: ret, img = capture.read() i += 1 if img.size>0: numImg +=1 if saveImage: cv2.imwrite('image{0:04d}.png'.format(numImg), img) else: images.append(img) return images def displayTrajectories(videoFilename, objects, homography = None, firstFrameNum = 0, lastFrameNumArg = None): '''Displays the objects overlaid frame by frame over the video ''' capture = cv2.VideoCapture(videoFilename) if capture.isOpened(): key = -1 ret = True frameNum = firstFrameNum capture.set(cv2.cv.CV_CAP_PROP_POS_FRAMES, firstFrameNum) if not lastFrameNumArg: from sys import maxint lastFrameNum = maxint else: lastFrameNum = lastFrameNumArg while ret and key!= 113 and frameNum < lastFrameNum: # 'q' ret, img = capture.read() if ret: print('frame {0}'.format(frameNum)) for obj in objects: if obj.existsAtInstant(frameNum): if not hasattr(obj, 'projectedPositions'): if homography != None: obj.projectedPositions = obj.positions.project(homography) else: obj.projectedPositions = obj.positions draw(img, obj.projectedPositions, cvRed, frameNum-obj.getFirstInstant()) cv2.putText(img, '{0}'.format(obj.num), obj.projectedPositions[frameNum-obj.getFirstInstant()].asint().astuple(), cv2.FONT_HERSHEY_PLAIN, 1, cvRed) cv2.imshow('frame', img) key = cv2.waitKey() if key == 115: cv2.imwrite('image.png', img) frameNum += 1 def printCvMat(cvmat, out = stdout): '''Prints the cvmat to out''' 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 (format 2xN points) through homography''' from numpy.core._dotblas import dot from numpy.core.multiarray import array from numpy.lib.function_base import append if points.shape[0] != 2: raise Exception('points of dimension {0} {1}'.format(points.shape[0], points.shape[1])) if (homography!=None) and homography.size>0: augmentedPoints = append(points,[[1]*points.shape[1]], 0) prod = dot(homography, augmentedPoints) return prod[0:2]/prod[2] else: return p def project(homography, p): '''Returns the coordinates of the projection of the point p through homography''' from numpy import array return projectArray(homography, array([[p[0]],[p[1]]])) def projectTrajectory(homography, trajectory): '''Projects a series of points in the format [[x1, x2, ...], [y1, y2, ...]]''' from numpy.core.multiarray import array return projectArray(homography, array(trajectory)) def invertHomography(homography): 'Returns an inverted homography' from numpy.linalg.linalg import inv invH = inv(homography) invH /= invH[2,2] return invH if opencvExists: 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)''' from numpy.core.multiarray import array from numpy.lib.function_base import median from numpy.core.fromnumeric import sum 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 = sum(dp**2) if d < maxTranslation2: delta.append(dp) if len(delta) >= minNMatches: return median(delta, axis=0) else: print(dp) return None