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)
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)
