def binarize_file_adaptive(filename, outFolder):
FileHelper.create_or_clear_dir(outFolder)
fullFilename = filename
path, filename = os.path.split(filename)
filename_base, filename_ext = os.path.splitext(filename)
for neighbours, shift in itertools.product(range(3, 17, 2), range(10)):
image = load_image_adaptive(fullFilename, neighbours, shift, binary_type=cv2.THRESH_TRUNC)
ffsave = "%s_%d_%d%s" % (filename_base, neighbours, shift, filename_ext)
cv2.imwrite(os.path.join(outFolder, ffsave), image)
def binarize_file_threshold(filename, outFolder):
FileHelper.create_or_clear_dir(outFolder)
fullFilename = filename
path, filename = os.path.split(filename)
filename_base, filename_ext = os.path.splitext(filename)
for threshold in range(30, 200, 20):
image = load_image_thresholding(fullFilename, threshold, binary_type=cv2.THRESH_BINARY)
ffsave = "%s_%d%s" % (filename_base, threshold, filename_ext)
cv2.imwrite(os.path.join(outFolder, ffsave), image)
def process_folder_group(folder, output, saveStructure=False, rules=default_sample):
FileHelper.create_or_clear_dir(output)
for d in os.listdir(folder):
if os.path.isdir(os.path.join(folder, d)):
files = process_folder(os.path.join(folder, d), rules)
if saveStructure:
copyto = os.path.join(output, d)
os.makedirs(copyto)
else:
copyto = output
for f in files:
shutil.copy2(os.path.join(folder, d, f), copyto)
def process_sample(classifier, inputFolder, outputFolder=None, jobs=-1, saveCorrects=False):
logger = logging.getLogger("TestClassifier")
positiveInput = os.path.join(inputFolder, 'positive')
negativeInput = os.path.join(inputFolder, 'negative')
falseNegativeOutput, falsePositiveOutput = None, None
trueNegativeOutput, truePositiveOutput = None, None
if outputFolder:
falsePositiveOutput = os.path.join(outputFolder, 'falsePositive')
falseNegativeOutput = os.path.join(outputFolder, 'falseNegative')
FileHelper.create_or_clear_dir(falsePositiveOutput)
FileHelper.create_or_clear_dir(falseNegativeOutput)
if saveCorrects:
truePositiveOutput = os.path.join(outputFolder, 'truePositive')
trueNegativeOutput = os.path.join(outputFolder, 'trueNegative')
FileHelper.create_or_clear_dir(truePositiveOutput)
FileHelper.create_or_clear_dir(trueNegativeOutput)
logger.debug('Process positive examples')
truePositive, falseNegative = process_folder(classifier, positiveInput, False, truePositiveOutput, falseNegativeOutput, jobs)
logger.info('True positives: %d; False negatives: %d' % (truePositive, falseNegative))
logger.debug('Process negative examples')
trueNegative, falsePositive = process_folder(classifier, negativeInput, True, falsePositiveOutput, trueNegativeOutput, jobs)
logger.info('False positives: %d; True negatives: %d' % (falsePositive, trueNegative))
return truePositive, falseNegative, trueNegative, falsePositive
def binarize_folder_threshold(inFolder, outFolder):
fileList = FileHelper.read_images_in_dir(inFolder)
FileHelper.create_or_clear_dir(outFolder)
for filename in fileList:
image = load_image_thresholding(os.path.join(inFolder, filename), 190)
cv2.imwrite(os.path.join(outFolder, filename), image)
imageShowed = cv2.imread(filename)
for r, (x1, y1, x2, y2) in result:
cv2.rectangle(imageShowed, (y1, x1), (y2, x2), colorMap[r], 1)
path, filename = os.path.split(filename)
bfn, ext = os.path.splitext(filename)
cv2.imwrite(os.path.join(output, bfn+'_wclrd'+ext), imageShowed)
# cv2.imshow('image', imageShowed)
# key = 255
# while key != 27:
# key = cv2.waitKey(20) & 0xFF
if __name__ == '__main__':
import sys
classifier = load(sys.argv[1])
output = sys.argv[3]
FileHelper.create_or_clear_dir(output)
colorMap = ColorMap((0, 255, 255), (0, 0, 255), 0.0, 1.0)
wfc = WindowedFeatureClassifier(classifier)
if os.path.isdir(sys.argv[2]):
dir = sys.argv[2]
for filename in os.listdir(dir):
if os.path.isfile(os.path.join(dir, filename)):
process_file(wfc, os.path.join(dir, filename), output)
else:
process_file(wfc, sys.argv[2], output)
base_template = drawContours(base_template, template_contours)
cv2.imwrite('/home/valeriy/projects/hashtag/logos/bw/twitter_big_template_contours.png', base_template)
if len(template_contours) > 1:
print 'Warning: too complex template'
template_contours = template_contours[0]
# print len(template_contours)
print '%0.3f %3d %0.2f %0.2f ' % (0.0, len(template_contours), contourSolidity(template_contours), contourExtent(template_contours))
# template_contours = approxContour(template_contours)
inFolder = '/home/valeriy/projects/hashtag/logos/twitter_frames/'
outFolder = '/home/valeriy/projects/hashtag/logos/bin_twitter_frames/'
FileHelper.create_or_clear_dir(outFolder)
for filename in sorted(FileHelper.read_images_in_dir(inFolder))[:3]:
print filename
basefile = os.path.join(inFolder, filename)
basefile_without_ext = os.path.join(outFolder, os.path.splitext(filename)[0])
# image = binarization.load_image_adaptive(basefile, 11, 10, inverse=False)
image = binarization.load_image_thresholding(basefile, 180)
# image = binarization.load_image_thresholding(basefile, 200, binary_type=cv2.THRESH_BINARY_INV)
contour_image = image.copy()
contours, hierarchy = cv2.findContours(contour_image, cv2.RETR_TREE, findContourMethod)
image_drawed = drawContours(image, contours)
# cv2.imwrite(basefile_without_ext + '_bin_thresh_100.jpg', image_drawed)
# cv2.imwrite(basefile_without_ext + '_contour.jpg', contour_image)
# contours = approxAllContours(contours)
