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[update] lint

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Nat Lee 2025-01-14 18:12:32 +08:00
parent da4a0c5452
commit c72b47484d
1 changed files with 11 additions and 12 deletions
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17
deepface/modules/demography.py
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@ -9,7 +9,6 @@ from tqdm import tqdm
from deepface.modules import modeling, detection, preprocessing from deepface.modules import modeling, detection, preprocessing
from deepface.models.demography import Gender, Race, Emotion from deepface.models.demography import Gender, Race, Emotion
# pylint: disable=trailing-whitespace
def analyze( def analyze(
img_path: Union[str, np.ndarray], img_path: Union[str, np.ndarray],
actions: Union[tuple, list] = ("emotion", "age", "gender", "race"), actions: Union[tuple, list] = ("emotion", "age", "gender", "race"),
@ -142,8 +141,14 @@ def analyze(
return preprocessing.resize_image(img=img_content, target_size=(224, 224)) return preprocessing.resize_image(img=img_content, target_size=(224, 224))
# Filter out empty faces # Filter out empty faces
face_data = [(preprocess_face(img_obj), img_obj["facial_area"], img_obj["confidence"]) face_data = [
for img_obj in img_objs if img_obj["face"].size > 0] (
preprocess_face(img_obj),
img_obj["facial_area"],
img_obj["confidence"]
)
for img_obj in img_objs if img_obj["face"].size > 0
]
if not face_data: if not face_data:
return [] return []
@ -151,30 +156,25 @@ def analyze(
# Unpack the face data # Unpack the face data
valid_faces, face_regions, face_confidences = zip(*face_data) valid_faces, face_regions, face_confidences = zip(*face_data)
faces_array = np.array(valid_faces) faces_array = np.array(valid_faces)
# Initialize the results list with face regions and confidence scores # Initialize the results list with face regions and confidence scores
results = [{"region": region, "face_confidence": conf} results = [{"region": region, "face_confidence": conf}
for region, conf in zip(face_regions, face_confidences)] for region, conf in zip(face_regions, face_confidences)]
# Iterate over the actions and perform analysis # Iterate over the actions and perform analysis
pbar = tqdm( pbar = tqdm(
actions, actions,
desc="Finding actions", desc="Finding actions",
disable=silent if len(actions) > 1 else True, disable=silent if len(actions) > 1 else True,
) )
for action in pbar: for action in pbar:
pbar.set_description(f"Action: {action}") pbar.set_description(f"Action: {action}")
model = modeling.build_model(task="facial_attribute", model_name=action.capitalize()) model = modeling.build_model(task="facial_attribute", model_name=action.capitalize())
predictions = model.predict(faces_array) predictions = model.predict(faces_array)
# If the model returns a single prediction, reshape it to match the number of faces. # If the model returns a single prediction, reshape it to match the number of faces.
# Determine the correct shape of predictions by using number of faces and predictions shape. # Determine the correct shape of predictions by using number of faces and predictions shape.
# Example: For 1 face with Emotion model, predictions will be reshaped to (1, 7). # Example: For 1 face with Emotion model, predictions will be reshaped to (1, 7).
if faces_array.shape[0] == 1 and len(predictions.shape) == 1: if faces_array.shape[0] == 1 and len(predictions.shape) == 1:
# For models like `Emotion`, which return a single prediction for a single face # For models like `Emotion`, which return a single prediction for a single face
predictions = predictions.reshape(1, -1) predictions = predictions.reshape(1, -1)
# Update the results with the predictions # Update the results with the predictions
# ---------------------------------------- # ----------------------------------------
# For emotion, calculate the percentage of each emotion and find the dominant emotion # For emotion, calculate the percentage of each emotion and find the dominant emotion
@ -228,5 +228,4 @@ def analyze(
] ]
for result, race_result in zip(results, race_results): for result, race_result in zip(results, race_results):
result.update(race_result) result.update(race_result)
return results return results