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deepface/models/Demography.py
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@ -29,19 +29,21 @@ class Demography(ABC):
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)
"""
if not self.model_name: # Check if called from derived class
raise NotImplementedError("virtual method must not be called directly")
raise NotImplementedError("no model selected")
assert img_batch.ndim == 4, "expected 4-dimensional tensor input"
if img_batch.shape[0] == 1: # Single image
if img_batch.shape[-1] != 3: # Check if grayscale
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)
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:
@ -54,10 +56,8 @@ class Demography(ABC):
Returns:
Four-dimensional numpy array (n, 224, 224, 3)
"""
if isinstance(img, list): # Convert from list to image batch.
image_batch = np.array(img)
else:
image_batch = img
image_batch = np.array(img)
# Remove batch dimension in advance if exists
image_batch = image_batch.squeeze()