update

featureExtraction.py

@@ -0,0 +1,48 @@
+# featureExtraction.py
+
+import cv2
+import numpy as np
+
+from globalVars import HEIGHT, WIDTH
+
+
+def extract_edge_features(frame):
+    """
+    Extract edge features using Canny edge detection.
+
+    Args:
+    - frame (ndarray): Image frame.
+
+    Returns:
+    - ndarray: Edge feature map.
+    """
+    edges = cv2.Canny(frame, threshold1=100, threshold2=200)
+    return edges.astype(np.float32) / 255.0
+
+def extract_histogram_features(frame, bins=64):
+    """
+    Extract histogram features from a frame.
+
+    Args:
+    - frame (ndarray): Image frame.
+    - bins (int): Number of bins for the histogram.
+
+    Returns:
+    - ndarray: Normalized histogram feature vector.
+    """
+    histogram, _ = np.histogram(frame.flatten(), bins=bins, range=[0, 255])
+    return histogram.astype(np.float32) / frame.size
+
+def preprocess_frame(frame):
+    # Check frame dimensions and resize if necessary
+    if frame.shape[:2] != (HEIGHT, WIDTH):
+        frame = cv2.resize(frame, (WIDTH, HEIGHT), interpolation=cv2.INTER_NEAREST)
+    
+    # Extract features
+    edge_feature = extract_edge_features(frame)
+    histogram_feature = extract_histogram_features(frame)
+    histogram_feature_image = np.full((HEIGHT, WIDTH), histogram_feature.mean()) # Convert histogram feature to image-like shape
+    combined_feature = np.stack([edge_feature, histogram_feature_image], axis=-1)
+
+    compressed_frame = frame / 255.0  # Assuming the frame is uint8, scale to [0, 1]
+    return combined_feature, compressed_frame