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Predictor.

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h-alice 2025-01-06 11:37:19 +08:00
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deepface/models/Demography.py
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@ -21,14 +21,37 @@ class Demography(ABC):
def predict(self, img: Union[np.ndarray, List[np.ndarray]]) -> Union[np.ndarray, np.float64]:
pass
def _predict_internal(self, img_batch: np.ndarray) -> np.ndarray:
"""
Predict for single image or batched images.
This method uses legacy method while receiving single image as input.
And switch to batch prediction if receives batched images.
Args:
img_batch: Batch of images as np.ndarray (n, 224, 224, 3), with n >= 1.
"""
if not self.model_name: # Check if called from derived class
raise NotImplementedError("virtual method must not be called directly")
assert img_batch.ndim == 4, "expected 4-dimensional tensor input"
if img_batch.shape[0] == 1: # Single image
img_batch = img_batch.squeeze(0) # Remove batch dimension
predict_result = self.model(img_batch, training=False).numpy()[0, :]
predict_result = np.expand_dims(predict_result, axis=0) # Add batch dimension
return predict_result
else: # Batch of images
return self.model.predict_on_batch(img_batch)
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.
Args:
img: Single image as np.ndarray (224, 224, 3) or
List of images as List[np.ndarray] or
Batch of images as np.ndarray (n, 224, 224, 3)
List of images as List[np.ndarray] or
Batch of images as np.ndarray (n, 224, 224, 3)
NOTE: If the imput is grayscale, then there's no channel dimension.
Returns:
Four-dimensional numpy array (n, 224, 224, 3)
"""