Mercurial > hg > nsaunier > traffic-intelligence
comparison python/cvutils.py @ 959:4f32d82ca390
corrected error due to change in Hog (scikit image)
| author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
|---|---|
| date | Thu, 24 Aug 2017 17:22:24 -0400 |
| parents | 747a5c68bd3c |
| children | 373e8ef6ee25 |
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| 958:747a5c68bd3c | 959:4f32d82ca390 |
|---|---|
| 603 | 603 |
| 604 if skimageAvailable: | 604 if skimageAvailable: |
| 605 from skimage.feature import hog | 605 from skimage.feature import hog |
| 606 from skimage import color, transform | 606 from skimage import color, transform |
| 607 | 607 |
| 608 def HOG(image, rescaleSize = (64, 64), orientations=9, pixelsPerCell=(8,8), cellsPerBlock=(2,2), blockNorm='L1', visualize=False, normalize=False): | 608 def HOG(image, rescaleSize = (64, 64), orientations = 9, pixelsPerCell = (8,8), cellsPerBlock = (2,2), blockNorm = 'L1', visualize = False, transformSqrt = False): |
| 609 bwImg = color.rgb2gray(image) | 609 bwImg = color.rgb2gray(image) |
| 610 inputImg = transform.resize(bwImg, rescaleSize) | 610 inputImg = transform.resize(bwImg, rescaleSize) |
| 611 features = hog(inputImg, orientations, pixelsPerCell, cellsPerBlock, blockNorm, visualize, normalize, True) | 611 features = hog(inputImg, orientations, pixelsPerCell, cellsPerBlock, blockNorm, visualize, transformSqrt, True) |
| 612 if visualize: | 612 if visualize: |
| 613 from matplotlib.pyplot import imshow, figure, subplot | 613 from matplotlib.pyplot import imshow, figure, subplot |
| 614 hogViz = features[1] | 614 hogViz = features[1] |
| 615 features = features[0] | 615 features = features[0] |
| 616 figure() | 616 figure() |
| 618 imshow(inputImg) | 618 imshow(inputImg) |
| 619 subplot(1,2,2) | 619 subplot(1,2,2) |
| 620 imshow(hogViz) | 620 imshow(hogViz) |
| 621 return float32(features) | 621 return float32(features) |
| 622 | 622 |
| 623 def createHOGTrainingSet(imageDirectory, classLabel, rescaleSize = (64,64), orientations=9, pixelsPerCell=(8,8), blockNorm='L1', cellsPerBlock=(2, 2), visualize=False, normalize=False): | 623 def createHOGTrainingSet(imageDirectory, classLabel, rescaleSize = (64,64), orientations = 9, pixelsPerCell = (8,8), blockNorm = 'L1', cellsPerBlock = (2, 2), visualize = False, transformSqrt = False): |
| 624 inputData = [] | 624 inputData = [] |
| 625 for filename in listdir(imageDirectory): | 625 for filename in listdir(imageDirectory): |
| 626 img = imread(imageDirectory+filename) | 626 img = imread(imageDirectory+filename) |
| 627 features = HOG(img, rescaleSize, orientations, pixelsPerCell, cellsPerBlock, blockNorm, visualize, normalize) | 627 features = HOG(img, rescaleSize, orientations, pixelsPerCell, cellsPerBlock, blockNorm, visualize, transformSqrt) |
| 628 inputData.append(features) | 628 inputData.append(features) |
| 629 | 629 |
| 630 nImages = len(inputData) | 630 nImages = len(inputData) |
| 631 return array(inputData, dtype = float32), array([classLabel]*nImages) | 631 return array(inputData, dtype = float32), array([classLabel]*nImages) |
| 632 | 632 |
