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feeding clean images to ml models

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This commit is contained in:
Sefik Ilkin Serengil 2025-04-19 12:12:57 +01:00
parent 58c9fe9b3d
commit 4f7119639a
2 changed files with 38 additions and 9 deletions
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4
deepface/DeepFace.py
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@ -463,6 +463,7 @@ def stream(
frame_threshold: int = 5,
anti_spoofing: bool = False,
output_path: Optional[str] = None,
debug: bool = False,
) -> None:
"""
Run real time face recognition and facial attribute analysis
@ -495,6 +496,8 @@ def stream(
output_path (str): Path to save the output video. (default is None
If None, no video is saved).
debug (bool): set this to True to save frame outcomes
Returns:
None
"""
@ -513,6 +516,7 @@ def stream(
frame_threshold=frame_threshold,
anti_spoofing=anti_spoofing,
output_path=output_path,
debug=debug,
)

43
deepface/modules/streaming.py
View File

@ -35,6 +35,7 @@ def analysis(
frame_threshold=5,
anti_spoofing: bool = False,
output_path: Optional[str] = None,
debug: bool = False,
):
"""
Run real time face recognition and facial attribute analysis
@ -105,15 +106,15 @@ def analysis(
freeze = False
num_frames_with_faces = 0
tic = time.time()
frame = 0
while True:
has_frame, img = cap.read()
raw_img = img.copy()
if not has_frame:
break
# we are adding some figures into img such as identified facial image, age, gender
# that is why, we need raw image itself to make analysis
raw_img = img.copy()
faces_coordinates = []
if not freeze:
@ -121,10 +122,12 @@ def analysis(
img=img, detector_backend=detector_backend, anti_spoofing=anti_spoofing
)
# we will pass img to analyze modules (identity, demography) and add some illustrations
# that is why, we will not be able to extract detected face from img clearly
detected_faces = extract_facial_areas(img=img, faces_coordinates=faces_coordinates)
# use raw_img otherwise countdown number will appear in the middle of the face
detected_faces = extract_facial_areas(img=raw_img, faces_coordinates=faces_coordinates)
img = highlight_facial_areas(img=img, faces_coordinates=faces_coordinates)
# highlight how many seconds required to freeze in the middle of detected face
img = countdown_to_freeze(
img=img,
faces_coordinates=faces_coordinates,
@ -133,21 +136,39 @@ def analysis(
)
num_frames_with_faces = num_frames_with_faces + 1 if len(faces_coordinates) else 0
freeze = num_frames_with_faces > 0 and num_frames_with_faces % frame_threshold == 0
if freeze:
# add analyze results into img - derive from raw_img
frame += 1
# restore raw image to get rid of countdown informtion
img = raw_img.copy()
# add analyze results into img
img = highlight_facial_areas(
img=raw_img, faces_coordinates=faces_coordinates, anti_spoofing=anti_spoofing
img=img, faces_coordinates=faces_coordinates, anti_spoofing=anti_spoofing
)
if debug is True:
cv2.imwrite(f"freezed_{frame}_0.jpg", detected_faces[0])
cv2.imwrite(f"freezed_{frame}_1.jpg", img)
# note: faces already detected from img (original one) in grab_facial_areas()
# perform_demography_analysis and perform_facial_recognition are using
# pre-detected faces, not using anything from img
# age, gender and emotion analysis
img = perform_demography_analysis(
enable_face_analysis=enable_face_analysis,
img=raw_img,
img=img,
faces_coordinates=faces_coordinates,
detected_faces=detected_faces,
)
if debug is True:
cv2.imwrite(f"freezed_{frame}_2.jpg", img)
# facial recogntion analysis
img = perform_facial_recognition(
img=img,
@ -159,6 +180,9 @@ def analysis(
model_name=model_name,
)
if debug is True:
cv2.imwrite(f"freezed_{frame}_3.jpg", img)
# freeze the img after analysis
freezed_img = img.copy()
@ -173,6 +197,7 @@ def analysis(
tic = time.time()
logger.info("Freeze released")
# count how many seconds required to relased freezed image in the left up area
freezed_img = countdown_to_release(img=freezed_img, tic=tic, time_threshold=time_threshold)
display_img = img if freezed_img is None else freezed_img