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Merge pull request #1031 from AndreaLanfranchi/al20240221
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Small knob polishing and amend duplicated attempts to add undetectable images to pickle
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@serengil
serengil committed Feb 22, 2024
2 parents ab8adf2 + 954ac6e commit 14bbc2f
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Showing 4 changed files with 63 additions and 61 deletions.
3 changes: 1 addition & 2 deletions deepface/modules/detection.py
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Original file line number Diff line number Diff line change
Expand Up @@ -210,6 +210,5 @@ def align_face(
return img, 0

angle = float(np.degrees(np.arctan2(right_eye[1] - left_eye[1], right_eye[0] - left_eye[0])))
img = Image.fromarray(img)
img = np.array(img.rotate(angle))
img = np.array(Image.fromarray(img).rotate(angle))
return img, angle
5 changes: 2 additions & 3 deletions deepface/modules/modeling.py
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Original file line number Diff line number Diff line change
Expand Up @@ -40,11 +40,10 @@ def build_model(model_name: str) -> Any:
if not "model_obj" in globals():
model_obj = {}

if not model_name in model_obj:
if not model_name in model_obj.keys():
model = models.get(model_name)
if model:
model = model()
model_obj[model_name] = model
model_obj[model_name] = model()
else:
raise ValueError(f"Invalid model_name passed - {model_name}")

Expand Down
51 changes: 24 additions & 27 deletions deepface/modules/realtime.py
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Original file line number Diff line number Diff line change
Expand Up @@ -68,9 +68,8 @@ def analysis(

cap = cv2.VideoCapture(source) # webcam
while True:
_, img = cap.read()

if img is None:
has_frame, img = cap.read()
if not has_frame:
break

# cv2.namedWindow('img', cv2.WINDOW_FREERATIO)
Expand All @@ -92,6 +91,8 @@ def analysis(
faces = []
for face_obj in face_objs:
facial_area = face_obj["facial_area"]
if facial_area["w"] <= 130: # discard small detected faces
continue
faces.append(
(
facial_area["x"],
Expand All @@ -111,36 +112,32 @@ def analysis(
detected_faces = []
face_index = 0
for x, y, w, h in faces:
if w > 130: # discard small detected faces

face_detected = True
if face_index == 0:
face_included_frames = (
face_included_frames + 1
) # increase frame for a single face
face_detected = True
if face_index == 0:
face_included_frames += 1 # increase frame for a single face

cv2.rectangle(
img, (x, y), (x + w, y + h), (67, 67, 67), 1
) # draw rectangle to main image
cv2.rectangle(
img, (x, y), (x + w, y + h), (67, 67, 67), 1
) # draw rectangle to main image

cv2.putText(
img,
str(frame_threshold - face_included_frames),
(int(x + w / 4), int(y + h / 1.5)),
cv2.FONT_HERSHEY_SIMPLEX,
4,
(255, 255, 255),
2,
)
cv2.putText(
img,
str(frame_threshold - face_included_frames),
(int(x + w / 4), int(y + h / 1.5)),
cv2.FONT_HERSHEY_SIMPLEX,
4,
(255, 255, 255),
2,
)

detected_face = img[int(y) : int(y + h), int(x) : int(x + w)] # crop detected face
detected_face = img[int(y) : int(y + h), int(x) : int(x + w)] # crop detected face

# -------------------------------------
# -------------------------------------

detected_faces.append((x, y, w, h))
face_index = face_index + 1
detected_faces.append((x, y, w, h))
face_index = face_index + 1

# -------------------------------------
# -------------------------------------

if face_detected == True and face_included_frames == frame_threshold and freeze == False:
freeze = True
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65 changes: 36 additions & 29 deletions deepface/modules/recognition.py
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Original file line number Diff line number Diff line change
Expand Up @@ -99,7 +99,7 @@ def find(

file_name = f"representations_{model_name}.pkl"
file_name = file_name.replace("-", "_").lower()
datastore_path = f"{db_path}/{file_name}"
datastore_path = os.path.join(db_path, file_name)

df_cols = [
"identity",
Expand Down Expand Up @@ -162,7 +162,7 @@ def find(
logger.info(
f"{len(newbies)} new representations are just added"
f" whereas {len(oldies)} represented one(s) are just dropped"
f" in {db_path}/{file_name} file."
f" in {os.path.join(db_path,file_name)} file."
)

if not silent:
Expand All @@ -173,8 +173,8 @@ def find(

if len(employees) == 0:
raise ValueError(
f"There is no image in {db_path} folder!"
"Validate .jpg, .jpeg or .png files exist in this path.",
f"Could not find any valid image in {db_path} folder!"
"Valid images are .jpg, .jpeg or .png files.",
)

# ------------------------
Expand All @@ -196,7 +196,7 @@ def find(
pickle.dump(representations, f)

if not silent:
logger.info(f"Representations stored in {db_path}/{file_name} file.")
logger.info(f"Representations stored in {datastore_path} file.")

# ----------------------------
# now, we got representations for facial database
Expand Down Expand Up @@ -241,6 +241,9 @@ def find(
distances = []
for _, instance in df.iterrows():
source_representation = instance[f"{model_name}_representation"]
if source_representation is None:
distances.append(float("inf")) # no representation for this image
continue

target_dims = len(list(target_representation))
source_dims = len(list(source_representation))
Expand Down Expand Up @@ -292,7 +295,7 @@ def find(
return resp_obj


def __list_images(path: str) -> list:
def __list_images(path: str) -> List[str]:
"""
List images in a given path
Args:
Expand All @@ -304,7 +307,7 @@ def __list_images(path: str) -> list:
for r, _, f in os.walk(path):
for file in f:
if file.lower().endswith((".jpg", ".jpeg", ".png")):
exact_path = f"{r}/{file}"
exact_path = os.path.join(r, file)
images.append(exact_path)
return images

Expand Down Expand Up @@ -365,31 +368,35 @@ def __find_bulk_embeddings(
expand_percentage=expand_percentage,
)
except ValueError as err:
logger.warn(
f"Exception while extracting faces from {employee}: {str(err)}. Skipping it."
logger.error(
f"Exception while extracting faces from {employee}: {str(err)}"
)
img_objs = []

for img_obj in img_objs:
img_content = img_obj["face"]
img_region = img_obj["facial_area"]
embedding_obj = representation.represent(
img_path=img_content,
model_name=model_name,
enforce_detection=enforce_detection,
detector_backend="skip",
align=align,
normalization=normalization,
)
if len(img_objs) == 0:
logger.warn(f"No face detected in {employee}. It will be skipped in detection.")
representations.append((employee, None, 0, 0, 0, 0))
else:
for img_obj in img_objs:
img_content = img_obj["face"]
img_region = img_obj["facial_area"]
embedding_obj = representation.represent(
img_path=img_content,
model_name=model_name,
enforce_detection=enforce_detection,
detector_backend="skip",
align=align,
normalization=normalization,
)

img_representation = embedding_obj[0]["embedding"]
img_representation = embedding_obj[0]["embedding"]
representations.append((
employee,
img_representation,
img_region["x"],
img_region["y"],
img_region["w"],
img_region["h"]
))

instance = []
instance.append(employee)
instance.append(img_representation)
instance.append(img_region["x"])
instance.append(img_region["y"])
instance.append(img_region["w"])
instance.append(img_region["h"])
representations.append(instance)
return representations

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