Update FeatureExtraction.py

FeatureExtraction.py

@@ -1,41 +1,29 @@
-import cv2
-import numpy as np
-from tensorflow.keras.models import load_model
-from tensorflow.keras.applications.resnet import ResNet152
-from tensorflow.keras.layers import AveragePooling2D, Flatten
-from tensorflow.keras.models import Model
-from tqdm import tqdm
-import os
-
-class FeatureExtractor:
-    def __init__(self, img_shape, channels):
-        self.seq_length = 40  # Number of frames to process
-        self.height = img_shape[0]
-        self.width = img_shape[1]
-        self.channels = channels
-
-        # Load ResNet152 model without the top fully connected layer
-        self.base_model = ResNet152(include_top=False, input_shape=(224, 224, 3), weights='imagenet')
-
-        # Freeze the base model layers
-        for layer in self.base_model.layers:
-            layer.trainable = False
-
-        # Adding an Average Pooling layer followed by Flatten
-        self.op = self.base_model.output
-        self.x_model = AveragePooling2D((7, 7), name='avg_pool')(self.op)
-        self.x_model = Flatten()(self.x_model)
-
-        # Create the feature extraction model
-        self.model = Model(self.base_model.input, self.x_model)
-
-    def extract_feature(self, frames_buffer):
-        x_op = np.zeros((2048, 40))  # Shape (features_dim, seq_length)
-        for i in range(len(frames_buffer)):
-            x_t = frames_buffer[i]
-            x_t = cv2.resize(x_t, (224, 224))  # Resize each frame to the required input size
-            x_t = np.expand_dims(x_t, axis=0)  # Add batch dimension
-            x = self.model.predict(x_t)
-            x_op[:, i] = x
-
+import cv2
+import numpy as np
+from tensorflow.keras.applications.resnet import ResNet152
+from tensorflow.keras.layers import AveragePooling2D, Flatten
+from tensorflow.keras.models import Model
+
+class FeatureExtractor:
+    def __init__(self, img_shape, channels, seq_length):
+        self.seq_length = seq_length
+        self.height = img_shape[0]
+        self.width = img_shape[1]
+        self.channels = channels
+        self.base_model = ResNet152(include_top=False, input_shape=(224, 224, 3), weights='imagenet')
+        for layer in self.base_model.layers:
+            layer.trainable = False
+        self.op = self.base_model.output
+        self.x_model = AveragePooling2D((7, 7), name='avg_pool')(self.op)
+        self.x_model = Flatten()(self.x_model)
+        self.model = Model(self.base_model.input, self.x_model)
+
+    def extract_feature(self, frames_buffer):
+        x_op = np.zeros((2048, self.seq_length))
+        for i in range(len(frames_buffer)):
+            x_t = frames_buffer[i]
+            x_t = cv2.resize(x_t, (224, 224))
+            x_t = np.expand_dims(x_t, axis=0)
+            x = self.model.predict(x_t)
+            x_op[:, i] = x.flatten()
         return x_op