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Enable the test scripts to use different face detectors. #4

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Enable the test scripts to use different face detectors. #4

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20 changes: 16 additions & 4 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -34,7 +34,7 @@ python face_warping_test.py -v 0 -p 1 -r -k

Command-line arguments:
```
-v VIDEO: Index of the webcam to use (start from 0) or
-v VIDEO: Index of the webcam to use (default=0) or
path of the input video file
-x WIDTH: Width of the warped frames (default=256)
-y HEIGHT: Height of the warped frames (default=256)
Expand All @@ -47,8 +47,14 @@ Command-line arguments:
-s: To use square-shaped detection box
-n: To use nearest-neighbour interpolation during restoration
-k: Keep aspect ratio in tanh-polar or tanh-circular warping
-d: Device to be used by PyTorch (default=cuda:0)
-d: Device to be used by the warping functions (default=cuda:0)
-b: Enable benchmark mode for CUDNN
-dt: Confidence threshold for face detection (default=0.8)
-dm: Face detection method, can be either RatinaFace (default)
or S3FD
-dw: Weights to be loaded for face detection, can be either
resnet50 or mobilenet0.25 when using RetinaFace
-dd: Device to be used for face detection (default=cuda:0)
```

There is also a script to specifically test the transform from ROI-tanh-polar space to the Cartesian ROI-tanh space (or in the reverse direction).
Expand All @@ -59,7 +65,7 @@ python tanh_polar_to_cartesian_test.py -v 0 -r -k

Command-line arguments:
```
-v VIDEO: Index of the webcam to use (start from 0) or
-v VIDEO: Index of the webcam to use (default=0) or
path of the input video file
-x WIDTH: Width of the warped frames (default=256)
-y HEIGHT: Height of the warped frames (default=256)
Expand All @@ -70,6 +76,12 @@ Command-line arguments:
-s: To use square-shaped detection box
-k: Keep aspect ratio in tanh-polar or tanh-circular warping
-i: To perform computation in the reverse direction
-d: Device to be used by PyTorch (default=cuda:0)
-d: Device to be used by the warping functions (default=cuda:0)
-b: Enable benchmark mode for CUDNN
-dt: Confidence threshold for face detection (default=0.8)
-dm: Face detection method, can be either RatinaFace (default)
or S3FD
-dw: Weights to be loaded for face detection, can be either
resnet50 or mobilenet0.25 when using RetinaFace
-dd: Device to be used for face detection (default=cuda:0)
```
35 changes: 28 additions & 7 deletions face_warping_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,7 @@
import numpy as np
from typing import Tuple, Optional
from argparse import ArgumentParser
from ibug.face_detection import RetinaFacePredictor
from ibug.face_detection import RetinaFacePredictor, S3FDPredictor

from ibug.roi_tanh_warping import *
from ibug.roi_tanh_warping import reference_impl as ref
Expand Down Expand Up @@ -102,7 +102,7 @@ def test_reference_impl(frame: np.ndarray, face_box: np.ndarray, target_width: i

def main() -> None:
parser = ArgumentParser()
parser.add_argument('--video', '-v', help='Video source')
parser.add_argument('--video', '-v', help='Video source (default=0)', default=0)
parser.add_argument('--width', '-x', help='Width of the warped image (default=256)', type=int, default=256)
parser.add_argument('--height', '-y', help='Height of the warped image (default=256)', type=int, default=256)
parser.add_argument('--polar', '-p', help='Use polar coordinates', type=int, default=0)
Expand All @@ -117,17 +117,38 @@ def main() -> None:
action='store_true', default=False)
parser.add_argument('--keep-aspect-ratio', '-k', help='Keep aspect ratio in tanh-polar or tanh-circular warping',
action='store_true', default=False)
parser.add_argument('--device', '-d', help='Device to be used by PyTorch (default=cuda:0)', default='cuda:0')
parser.add_argument('--device', '-d', default='cuda:0',
help='Device to be used by the warping functions (default=cuda:0)')
parser.add_argument('--benchmark', '-b', help='Enable benchmark mode for CUDNN',
action='store_true', default=False)
parser.add_argument('--detection-threshold', '-dt', type=float, default=0.8,
help='Confidence threshold for face detection (default=0.8)')
parser.add_argument('--detection-method', '-dm', default='retinaface',
help='Face detection method, can be either RatinaFace or S3FD (default=RatinaFace)')
parser.add_argument('--detection-weights', '-dw', default=None,
help='Weights to be loaded for face detection, ' +
'can be either resnet50 or mobilenet0.25 when using RetinaFace')
parser.add_argument('--detection-device', '-dd', default='cuda:0',
help='Device to be used for face detection (default=cuda:0)')
args = parser.parse_args()

# Make the models run a bit faster
torch.backends.cudnn.benchmark = args.benchmark

# Create object detector
detector = RetinaFacePredictor(device=args.device, model=RetinaFacePredictor.get_model('mobilenet0.25'))
print('RetinaFace detector created using mobilenet0.25 backbone.')
# Create the face detector
args.detection_method = args.detection_method.lower()
if args.detection_method == 'retinaface':
face_detector = RetinaFacePredictor(threshold=args.detection_threshold, device=args.detection_device,
model=(RetinaFacePredictor.get_model(args.detection_weights)
if args.detection_weights else None))
print('Face detector created using RetinaFace.')
elif args.detection_method == 's3fd':
face_detector = S3FDPredictor(threshold=args.detection_threshold, device=args.detection_device,
model=(S3FDPredictor.get_model(args.detection_weights)
if args.detection_weights else None))
print('Face detector created using S3FD.')
else:
raise ValueError('detector-method must be set to either RetinaFace or S3FD')

# Open webcam
if os.path.exists(args.video):
Expand All @@ -148,7 +169,7 @@ def main() -> None:
break
else:
# Face detection
face_boxes = detector(frame, rgb=False)
face_boxes = face_detector(frame, rgb=False)
if len(face_boxes) > 0:
biggest_face_idx = int(np.argmax([(bbox[3] - bbox[1]) * (bbox[2] - bbox[0])
for bbox in face_boxes]))
Expand Down
35 changes: 28 additions & 7 deletions tanh_polar_to_cartesian_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,7 @@
import numpy as np
from typing import Tuple, Optional
from argparse import ArgumentParser
from ibug.face_detection import RetinaFacePredictor
from ibug.face_detection import RetinaFacePredictor, S3FDPredictor

from ibug.roi_tanh_warping import *
from ibug.roi_tanh_warping import reference_impl as ref
Expand Down Expand Up @@ -96,7 +96,7 @@ def test_pytorch_impl(device: str, frame: np.ndarray, face_box: np.ndarray, targ

def main() -> None:
parser = ArgumentParser()
parser.add_argument('--video', '-v', help='Video source')
parser.add_argument('--video', '-v', help='Video source (default=0)', default=0)
parser.add_argument('--width', '-x', help='Width of the warped image (default=256)', type=int, default=256)
parser.add_argument('--height', '-y', help='Height of the warped image (default=256)', type=int, default=256)
parser.add_argument('--offset', '-o', help='Angular offset, only used when polar>0', type=float, default=0.0)
Expand All @@ -112,17 +112,38 @@ def main() -> None:
action='store_true', default=False)
parser.add_argument('--reverse', '-i', help='Perform computation in the reverse direction',
action='store_true', default=False)
parser.add_argument('--device', '-d', help='Device to be used (default=cuda:0)', default='cuda:0')
parser.add_argument('--device', '-d', default='cuda:0',
help='Device to be used by the warping functions (default=cuda:0)')
parser.add_argument('--benchmark', '-b', help='Enable benchmark mode for CUDNN',
action='store_true', default=False)
parser.add_argument('--detection-threshold', '-dt', type=float, default=0.8,
help='Confidence threshold for face detection (default=0.8)')
parser.add_argument('--detection-method', '-dm', default='retinaface',
help='Face detection method, can be either RatinaFace or S3FD (default=RatinaFace)')
parser.add_argument('--detection-weights', '-dw', default=None,
help='Weights to be loaded for face detection, ' +
'can be either resnet50 or mobilenet0.25 when using RetinaFace')
parser.add_argument('--detection-device', '-dd', default='cuda:0',
help='Device to be used for face detection (default=cuda:0)')
args = parser.parse_args()

# Make the models run a bit faster
torch.backends.cudnn.benchmark = args.benchmark

# Create face detector
detector = RetinaFacePredictor(device=args.device, model=RetinaFacePredictor.get_model('mobilenet0.25'))
print('RetinaFace detector created using mobilenet0.25 backbone.')
# Create the face detector
args.detection_method = args.detection_method.lower()
if args.detection_method == 'retinaface':
face_detector = RetinaFacePredictor(threshold=args.detection_threshold, device=args.detection_device,
model=(RetinaFacePredictor.get_model(args.detection_weights)
if args.detection_weights else None))
print('Face detector created using RetinaFace.')
elif args.detection_method == 's3fd':
face_detector = S3FDPredictor(threshold=args.detection_threshold, device=args.detection_device,
model=(S3FDPredictor.get_model(args.detection_weights)
if args.detection_weights else None))
print('Face detector created using S3FD.')
else:
raise ValueError('detector-method must be set to either RetinaFace or S3FD')

# Open webcam
if os.path.exists(args.video):
Expand All @@ -143,7 +164,7 @@ def main() -> None:
break
else:
# Face detection
face_boxes = detector(frame, rgb=False)
face_boxes = face_detector(frame, rgb=False)
if len(face_boxes) > 0:
biggest_face_idx = int(np.argmax([(bbox[3] - bbox[1]) * (bbox[2] - bbox[0])
for bbox in face_boxes]))
Expand Down