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[fix] lint

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NatLee 2025-01-13 22:27:11 +08:00
parent eb7b8411e8
commit 688fbe6b90
2 changed files with 25 additions and 21 deletions
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38
deepface/models/Demography.py
View File

@ -28,24 +28,32 @@ class Demography(ABC):
And switch to batch prediction if receives batched images.
Args:
img_batch: Batch of images as np.ndarray (n, x, y, c), with n >= 1, x = image width, y = image height, c = channel
Or Single image as np.ndarray (1, x, y, c), with x = image width, y = image height and c = channel
The channel dimension may be omitted if the image is grayscale. (For emotion model)
img_batch:
Batch of images as np.ndarray (n, x, y, c)
with n >= 1, x = image width, y = image height, c = channel
Or Single image as np.ndarray (1, x, y, c)
with x = image width, y = image height and c = channel
The channel dimension may be omitted if the image is grayscale. (For emotion model)
"""
if not self.model_name: # Check if called from derived class
raise NotImplementedError("no model selected")
assert img_batch.ndim == 4, "expected 4-dimensional tensor input"
# Single image
if img_batch.shape[0] == 1:
# Check if grayscale by checking last dimension, if not 3, it is grayscale.
if img_batch.shape[-1] != 3:
# Remove batch dimension
img_batch = img_batch.squeeze(0)
# Predict with legacy method.
return self.model(img_batch, training=False).numpy()[0, :]
# Batch of images
# Predict with batch prediction
return self.model.predict_on_batch(img_batch)
if img_batch.shape[0] == 1: # Single image
if img_batch.shape[-1] != 3: # Check if grayscale by checking last dimension, if not 3, it is grayscale.
img_batch = img_batch.squeeze(0) # Remove batch dimension
predict_result = self.model(img_batch, training=False).numpy()[0, :] # Predict with legacy method.
return predict_result
else: # Batch of images
return self.model.predict_on_batch(img_batch) # Predict with batch prediction
def _preprocess_batch_or_single_input(self, img: Union[np.ndarray, List[np.ndarray]]) -> np.ndarray:
def _preprocess_batch_or_single_input(
self,
img: Union[np.ndarray, List[np.ndarray]]
) -> np.ndarray:
"""
Preprocess single or batch of images, return as 4-D numpy array.
@ -56,15 +64,11 @@ class Demography(ABC):
Returns:
Four-dimensional numpy array (n, 224, 224, 3)
"""
image_batch = np.array(img)
# Remove batch dimension in advance if exists
image_batch = image_batch.squeeze()
# Check input dimension
if len(image_batch.shape) == 3:
# Single image - add batch dimension
image_batch = np.expand_dims(image_batch, axis=0)
return image_batch

8
deepface/modules/demography.py
View File

@ -168,9 +168,9 @@ def analyze(
model = modeling.build_model(task="facial_attribute", model_name=action.capitalize())
predictions = model.predict(faces_array)
# If the model returns a single prediction, reshape it to match the number of faces
# Use number of faces and number of predictions shape to determine the correct shape of predictions
# For example, if there are 1 face to predict with Emotion model, reshape predictions to (1, 7)
# 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.
# 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:
# For models like `Emotion`, which return a single prediction for a single face
predictions = predictions.reshape(1, -1)
@ -229,4 +229,4 @@ def analyze(
for result, race_result in zip(results, race_results):
result.update(race_result)
return results
return results