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Raghucharan16 2025-02-23 11:51:02 +05:30
parent e19c7fcc1c
commit 2dbae0edca
1 changed files with 30 additions and 8 deletions
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38
deepface/models/facial_recognition/Buffalo_L.py
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@ -10,7 +10,7 @@ try:
from insightface.model_zoo import get_model from insightface.model_zoo import get_model
except ModuleNotFoundError as err: except ModuleNotFoundError as err:
raise ModuleNotFoundError( raise ModuleNotFoundError(
"InsightFace is an optional dependency for the Buffalo_L model." "InsightFace is an optional dependency for the Buffalo_L model. "
"You can install it with: pip install insightface>=0.7.3" "You can install it with: pip install insightface>=0.7.3"
) from err ) from err
@ -18,21 +18,43 @@ class Buffalo_L(FacialRecognition):
def __init__(self): def __init__(self):
self.model = None self.model = None
self.input_shape = (112, 112) # Buffalo_L expects 112x112 self.input_shape = (112, 112) # Buffalo_L expects 112x112
self.output_shape = 512 # Embedding size self.output_shape = 512 # Embedding size
self.load_model() self.load_model()
def load_model(self): def load_model(self):
""" """
Load the InsightFace Buffalo_L recognition model. Load the InsightFace Buffalo_L recognition model.
""" """
# Use DeepFace's utility to download weights if necessary # Define the model filename and subdirectory
model_rel_path = os.path.join("insightface", "buffalo_l", "w600k_r50.onnx") sub_dir = "buffalo_l"
model_file = "w600k_r50.onnx"
model_rel_path = os.path.join(sub_dir, model_file)
# Define the weights directory and ensure the buffalo_l subdirectory exists
weights_dir = os.path.join(os.path.expanduser("~"), ".deepface", "weights")
buffalo_l_dir = os.path.join(weights_dir, sub_dir)
if not os.path.exists(buffalo_l_dir):
os.makedirs(buffalo_l_dir, exist_ok=True)
print("Created directory:", buffalo_l_dir)
# Download the model weights if not already present
weights_path = weight_utils.download_weights_if_necessary( weights_path = weight_utils.download_weights_if_necessary(
file_name="webface_r50.onnx", file_name=model_rel_path,
source_url="https://drive.google.com/uc?export=download&confirm=pbef&id=1N0GL-8ehw_bz2eZQWz2b0A5XBdXdxZhg" source_url="https://drive.google.com/uc?export=download&confirm=pbef&id=1N0GL-8ehw_bz2eZQWz2b0A5XBdXdxZhg"
) )
# Load model from weights folder print("Downloaded model path:", weights_path)
self.model = get_model("buffalo_l/w600k_r50.onnx", root=os.path.dirname(weights_path))
# Verify that the model file exists at the expected location
expected_model_path = os.path.join(buffalo_l_dir, model_file)
if os.path.exists(expected_model_path):
print("Model file found at expected location:", expected_model_path)
else:
print("Model file NOT found at expected location:", expected_model_path)
# Use the full absolute path for loading the model
full_model_path = os.path.join(buffalo_l_dir, model_file)
print("Full model path:", full_model_path)
self.model = get_model(full_model_path)
self.model.prepare(ctx_id=-1, input_size=self.input_shape) self.model.prepare(ctx_id=-1, input_size=self.input_shape)
def preprocess(self, img: np.ndarray) -> np.ndarray: def preprocess(self, img: np.ndarray) -> np.ndarray:
@ -47,7 +69,7 @@ class Buffalo_L(FacialRecognition):
img = img[0] # Remove batch dimension img = img[0] # Remove batch dimension
if img.max() <= 1.0: # If normalized to [0, 1] if img.max() <= 1.0: # If normalized to [0, 1]
img = (img * 255).astype(np.uint8) img = (img * 255).astype(np.uint8)
# Always convert RGB to BGR (DeepFace outputs RGB, InsightFace expects BGR) # Convert RGB to BGR (DeepFace outputs RGB, InsightFace expects BGR)
img = img[:, :, ::-1] img = img[:, :, ::-1]
return img return img