def run(image_path: str, weights_path: str):
face_marker = FaceMarker(weights_path=weights_path)
face_aligner = FaceAligner()
if path.isdir(image_path):
image_paths = list_files(image_path, IMAGE_EXT, recursive=True)
if len(image_paths) == 0:
raise ValueError(f'No images found in {image_path}.')
elif path.isfile(image_path):
image_paths = [image_path]
else:
raise ValueError(f'Input "{image_path}" is not file nor a directory.')
logger.info('Starting analysis...')
logger.info('Press "space" key to display next result. Press "q" to quit.')
images = []
for image_path in image_paths:
image_name = path.basename(image_path)
image = cv.imread(image_path)
if image is None:
logger.warn(f'Unable to open image file {image_path}')
continue
h, w, = image.shape[0:2]
logger.info(f'{image_name} loaded. Image size is {w}x{h} pixels.')
images.append(image)
n_images = len(images)
tic = time()
marks, scores = face_marker.mark(images)
toc = time()
logger.info(f'Number of images loaded: {n_images}')
logger.info(f'Total processing time: {(toc - tic):.4f} s.')
for i in range(n_images):
image = images[i]
mark = marks[i]
score = scores[i]
aligned_image, nose_dev = face_aligner.align(image, mark)
for point in mark:
cv.circle(image, (int(point[0]), int(point[1])), 2, (0, 255, 0),
-1)
cv.imshow(f'Face {i} (score {score:.3f})', image)
title = f'Aligned Face {i} (dev: {nose_dev[0]:.3f}, {nose_dev[1]:.3f})'
cv.imshow(title, aligned_image)
ret = cv.waitKey()
if ret == ord(' '):
cv.destroyAllWindows()
elif ret == ord('q'):
cv.destroyAllWindows()
break
def list_images(root_path):
if path.isdir(root_path):
image_paths = list_files(root_path, IMAGE_EXT, recursive=True)
if len(image_paths) == 0:
raise ValueError(f'No images found in {root_path}.')
elif path.isfile(root_path):
image_paths = [root_path]
else:
raise ValueError(f'Input "{root_path}" is not file nor a directory.')
return image_paths
def setUp(self) -> None:
detection_min_height = 24
detection_min_score = 0.8
marking_min_score = 0.6
max_frame_size = 1024
store_frames = True
align_max_deviation = None
detection_face_padding = 0.2
face_detector = FaceDetector(
weights_path=DETECTOR_WEIGHTS_PATH,
min_height=detection_min_height,
min_score=detection_min_score,
force_cpu=True
)
face_marker: FaceMarker = FaceMarker(
weights_path=MARKER_WEIGHTS_PATH,
force_cpu=True
)
face_aligner = FaceAligner()
face_encoder = FaceEncoder(
weights_path=ENCODER_WEIGHTS_PATH,
force_cpu=True
)
self.frame_analyzer = FrameAnalyzer(
detector=face_detector,
marker=face_marker,
aligner=face_aligner,
encoder=face_encoder,
detection_only=False,
max_frame_size=max_frame_size,
store_frames=store_frames,
max_deviation=align_max_deviation,
marking_min_score=marking_min_score,
face_padding=detection_face_padding
)
self.image_paths = list_files(DATA_DIR, IMAGE_EXT, recursive=True)
def handle(self, *args, **options):
images_path = options['src']
image_exts = tuple([ext.strip() for ext in options['exts'].split(',')])
max_subjects = options['count']
if max_subjects <= 0:
max_subjects = float('inf')
print(f'Listing images from {images_path}...')
image_paths = list_files(images_path, image_exts, recursive=True)
files_count = len(image_paths)
print(f'Found {files_count} images.')
print(f'Starting processing images.')
root = settings.FACES_IMAGES_PATH
max_iter = min(files_count, max_subjects)
for index, image_path in enumerate(image_paths):
if index >= max_subjects:
break
image_name = path.basename(image_path)
frame_path = create_image(image_path, root, 'frame_')
frame = Frame.objects.create(image=frame_path)
face_analyzer.analyze_frame(frame.pk)
# frame = Frame.objects.get(pk=frame.pk)
n_faces = frame.faces.count()
if n_faces:
face = frame.faces.all()[0]
name = path.splitext(image_name)[0]
subject_name = ' '.join(name.split('_')[0:-1])
subject = Subject.objects.create(name=subject_name)
subject.faces.add(face)
if n_faces > 1:
warn(f'More than one face detected in "{image_name}".')
else:
warn(f'No face detected in "{image_name}".')
print(f'Progress {(100 * index / max_iter):.1f}%.')
print(f'Done!')
def run(
image_path: str,
detector_weights_path: str,
marker_weights_path: str,
encoder_weights_path: str,
show_scores: bool = False
):
from dnfal.vision import FacesVision
from dnfal.settings import Settings
from dnfal.loggers import logger
settings = Settings()
settings.detector_weights_path = detector_weights_path
settings.marker_weights_path = marker_weights_path
settings.encoder_weights_path = encoder_weights_path
settings.log_to_console = True
settings.detection_min_height = 24
settings.detection_min_score = 0.8
settings.marking_min_score = 0.6
faces_vision = FacesVision(settings)
if path.isdir(image_path):
image_paths = list_files(image_path, IMAGE_EXT, recursive=True)
if len(image_paths) == 0:
raise ValueError(f'No images found in {image_path}.')
elif path.isfile(image_path):
image_paths = [image_path]
else:
raise ValueError(f'Input "{image_path}" is not file nor a directory.')
logger.info('Starting analysis...')
logger.info('Press "space" key to display next result. Press "q" to quit.')
max_image_size = 1920
drawer = Drawer()
drawer.font_scale = 0.7
drawer.font_linewidth = 2
drawer.font_color = (0, 255, 0)
for image_path in image_paths:
image_name = path.basename(image_path)
image = cv.imread(image_path)
if image is None:
logger.warn(f'Unable to open image file {image_path}')
continue
h, w, = image.shape[0:2]
logger.info(f'{image_name} loaded. Image size is {w}x{h} pixels.')
if max(w, h) > max_image_size:
image, scale = resize(image, max_image_size)
h, w, = image.shape[0:2]
logger.info(f'Image resized to {w}x{h} pixels.')
tic = time()
faces, _ = faces_vision.frame_analyzer.find_faces(image)
toc = time()
logger.info(f'Found {len(faces)} faces in {(toc - tic):.3f} seconds.')
for ind, face in enumerate(faces):
face_image = face.image.copy()
box = (
int(w * face.box[0]),
int(h * face.box[1]),
int(w * face.box[2]),
int(h * face.box[3]),
)
if show_scores:
s = (int(100 * face.detect_score), int(100 * face.mark_score))
label = f'Face {ind}, ({s[0]} %, {s[1]} %)'
else:
label = f'Face {ind}'
drawer.draw_labeled_box(image, label, box)
for m in face.landmarks:
cv.circle(face_image, (m[0], m[1]), 2, (0, 255, 0), -1)
nv = face.nose_deviation
print(
f'Detected face [{ind}]: {{ score: {face.detect_score}, '
f'nose deviation: [{nv[0]:.3f}, {nv[1]:.3f}] }}'
)
cv.imshow(f'Face "{ind}"', face_image)
cv.imshow(f'Faces in {image_name}', image)
ret = cv.waitKey()
if ret == ord(' '):
cv.destroyAllWindows()
elif ret == ord('q'):
cv.destroyAllWindows()
break
def run(image_path: str, detector_weights_path: str, marker_weights_path: str):
from dnfal.amazon.faces import analyze_faces, AwsFace
from dnfal.detection import FaceDetector
from dnfal.alignment import FaceMarker, FaceAligner
from dnfal.engine import FrameAnalyzer
from dnfal.loggers import logger, config_logger
config_logger(level='info')
face_detector = FaceDetector(weights_path=detector_weights_path,
min_score=0.9,
nms_thresh=0.7)
face_marker = FaceMarker(weights_path=marker_weights_path)
face_aligner = FaceAligner(out_size=256)
frame_analyzer = FrameAnalyzer(detector=face_detector,
marker=face_marker,
aligner=face_aligner,
encoder=None,
face_padding=0.4,
store_aligned=True)
if path.isdir(image_path):
image_paths = list_files(image_path, IMAGE_EXT, recursive=True)
if len(image_paths) == 0:
raise ValueError(f'No images found in {image_path}.')
elif path.isfile(image_path):
image_paths = [image_path]
else:
raise ValueError(f'Input "{image_path}" is not file nor a directory.')
logger.info('Starting analysis...')
logger.info('Press "space" key to display next result. Press "q" to quit.')
max_image_size = 1920
drawer = Drawer()
drawer.font_scale = 0.5
drawer.font_linewidth = 1
drawer.text_margins = (2, 3, 12, 3)
GENDER_LABELS = {
AwsFace.GENDER_MAN: 'Man',
AwsFace.GENDER_WOMAN: 'Woman',
}
for image_path in image_paths:
image_name = path.basename(image_path)
image = cv.imread(image_path)
if image is None:
logger.warn(f'Unable to open image file {image_path}')
continue
h, w, = image.shape[0:2]
logger.info(f'{image_name} loaded. Image size is {w}x{h} pixels.')
if max(w, h) > max_image_size:
image, scale = resize(image, max_image_size)
h, w, = image.shape[0:2]
logger.info(f'Image resized to {w}x{h} pixels.')
image = cv.copyMakeBorder(image,
left=0,
top=100,
right=0,
bottom=0,
borderType=cv.BORDER_CONSTANT,
value=(0, 0, 0))
h, w, = image.shape[0:2]
tic = time()
faces, _ = frame_analyzer.find_faces(image)
n_faces = len(faces)
toc = time()
logger.info(f'Found {n_faces} faces in {(toc - tic):.3f} seconds.')
if n_faces:
tic = time()
aws_faces = analyze_faces([face.image for face in faces])
delay = time() - tic
logger.info(f'Faces analyzed in {delay:.3f} seconds.')
for ind, face in enumerate(faces):
gender_label = 'Unknown gender'
age_label = 'Unknown age'
aws_face = aws_faces[ind]
if aws_face is not None:
gender_pred = GENDER_LABELS[aws_face.gender]
gender_prob = 100 * aws_face.gender_score
age_low = aws_face.age_low
age_high = aws_face.age_high
gender_label = f'{gender_pred} ({int(gender_prob)}%)'
age_label = f'{age_low}-{age_high} years'
label = f'{gender_label} \n {age_label}'
box = (
int(w * face.box[0]),
int(h * face.box[1]),
int(w * face.box[2]),
int(h * face.box[3]),
)
drawer.draw_labeled_box(image, label, box)
cv.imshow(f'Faces in {image_name}', image)
ret = cv.waitKey()
if ret == ord(' '):
cv.destroyAllWindows()
elif ret == ord('q'):
cv.destroyAllWindows()
break