for r, (x1, y1, x2, y2) in result:
cv2.rectangle(image, (y1, x1), (y2, x2), colorMap[r], 1)
path, bf = os.path.split(filename)
cv2.imwrite(os.path.join(output, bf), image)
if __name__ == '__main__':
parser = ArgParserWithDefaultHelp(description='Visualising tool for pattern matcher and classifier')
parser.add_argument('folder', help='Folder, that contains folders with frames')
parser.add_argument('patterns', help='Folder with patterns')
parser.add_argument('classifier', help='Classifier')
parser.add_argument('output', help='Output folder for processed images')
parser.add_argument('-j', '--jobs', default=-1, type=int, help='Processes amount for parallel processing')
args = parser.parse_args()
classifier = load(args.classifier)
FileHelper.create_or_clear_dir(args.output)
wfc = WindowedFeatureClassifier(classifier)
pm = PatternMatcher()
pm.load_patterns_folder(args.patterns)
tasks = []
for dir, filename in FileHelper.read_images_in_dir_recursively(args.folder):
tasks.append(delayed(process_image)(wfc, pm, os.path.join(dir, filename), args.output))
p = Parallel(n_jobs=args.jobs, verbose=100)
p(tasks)
import os
from image.pattern_matcher import PatternMatcher
from misc.file_helper import FileHelper
if __name__ == "__main__":
import sys
# for dir, filename in FileHelper.read_images_in_dir_recursively('frames_tagged_291013_goodbad'):
# pm = PatternMatcher()
# pattern_dir = 'frames_tagged_291013_goodbad_ht/' + '/'.join((dir.split('/')[1:]))
# bfn, ext = os.path.splitext(filename)
#
# pm.load_patterns([os.path.join(pattern_dir, bfn+'_ht'+ext)], convert2GS=True)
# pm.match(os.path.join(dir, filename), generateHist=True)
pm = PatternMatcher()
# pm.load_patterns([sys.argv[1]], convert2GS=True)
pm.load_patterns_folder(sys.argv[1])
if os.path.isdir(sys.argv[2]):
for dir, filename in FileHelper.read_images_in_dir_recursively(sys.argv[2]):
print os.path.join(dir, filename)
pm.match(os.path.join(dir, filename), visualise=True)
else:
pm.match(sys.argv[2], visualise=True)
