def main(
input_path,
image_output_path,
weights_path,
image_extensions,
face_threshold,
plate_threshold,
write_json,
keep_exif,
obfuscation_parameters
):
download_weights(download_directory=weights_path)
kernel_size, sigma, box_kernel_size = obfuscation_parameters.split(',')
obfuscator = Obfuscator(kernel_size=int(kernel_size), sigma=float(sigma), box_kernel_size=int(box_kernel_size))
detectors = {
'face': Detector(kind='face', weights_path=get_weights_path(weights_path, kind='face')),
'plate': Detector(kind='plate', weights_path=get_weights_path(weights_path, kind='plate'))
}
detection_thresholds = {
'face': face_threshold,
'plate': plate_threshold
}
anonymizer = Anonymizer(obfuscator=obfuscator, detectors=detectors)
anonymizer.anonymize_images(
input_path=input_path,
output_path=image_output_path,
detection_thresholds=detection_thresholds,
file_types=image_extensions.split(','),
write_json=write_json,
keep_exif=keep_exif
)
def detect(ids,
threshold=0.3,
weights_path="./weights",
panos_path="/home/www-data/panos",
output_path="./panosout"):
print(threshold)
detectors = {
'face':
Detector(kind='face',
weights_path=get_weights_path(weights_path, kind='face')),
'plate':
Detector(kind='face',
weights_path=get_weights_path(weights_path, kind='plate'))
}
detection_thresholds = {'face': threshold, 'plate': threshold}
if not os.path.exists(f"{output_path}/{threshold}"):
os.makedirs(f"{output_path}/{threshold}")
for id in ids:
print(f"Loading {panos_path}/{id}.jpg")
with Image.open(f"{panos_path}/{id}.jpg") as image:
npimage = numpy.array(image.convert('RGB'))
detected_boxes = []
for kind, detector in detectors.items():
new_boxes = detector.detect(npimage,
detection_thresholds[kind])
detected_boxes.extend(new_boxes)
draw = ImageDraw.Draw(image)
for box in detected_boxes:
print(
f"{box.x_min} {box.y_min} {box.x_max} {box.y_max} {box.score} {box.kind}"
)
# draw.rectangle([box.x_min, box.y_min, box.x_max, box.y_max],outline=(255, 0, 0), width=5)
cropbox = (int(box.x_min), int(box.y_min), int(box.x_max),
int(box.y_max))
crop = image.crop(cropbox)
for i in range(10):
crop = crop.filter(ImageFilter.BLUR)
image.paste(crop, cropbox)
image.save(f"{output_path}/{threshold}/{id}.jpg", "JPEG")
def test_it_detects_obvious_faces(tmp_path):
weights_directory = tmp_path / 'weights'
face_weights_path = get_weights_path(weights_directory, kind='face')
download_weights(weights_directory)
detector = Detector(kind='face', weights_path=face_weights_path)
np_image = load_np_image('./test/detection/face_test_image.jpg')
left_face = Box(x_min=267, y_min=64, x_max=311, y_max=184, score=0.0, kind='face')
right_face = Box(x_min=369, y_min=68, x_max=420, y_max=152, score=0.0, kind='face')
boxes = detector.detect(np_image, detection_threshold=0.2)
assert len(boxes) >= 2
for box in boxes:
assert box.score >= 0.2
assert boxes[0].score >= 0.5 and boxes[1].score >= 0.5
assert ((box_covers_box(boxes[0], left_face) and box_covers_box(boxes[1], right_face)) or
(box_covers_box(boxes[1], left_face) and box_covers_box(boxes[0], right_face)))