Update app.py

app.py

@@ -37,7 +37,7 @@ model.eval()
 #     transforms.ToTensor()
 # ])
 transform = transforms.Compose([  # Ensure input is a PIL image
-    transforms.Resize((640, 640)),
+    transforms.Resize((960, 540)),
     transforms.ToTensor()
 ])
 # transform = transforms.Compose([
@@ -148,65 +148,7 @@ def detect_objects_in_image(image):
 
 #     return Image.open(buf)
 
-def generate_vehicle_count_graph(object_counts):
-    color_palette = ['#4C9ACD', '#88B8A3', '#7F9C9C', '#D1A3B5', '#A1C6EA', '#FFB6C1', '#F0E68C', '#D3B0D8', '#F8A5D1',
-                     '#B8B8D1']
 
-    fig, ax = plt.subplots(figsize=(8, 5))
-    labels = list(object_counts.keys())
-    values = list(object_counts.values())
-
-    ax.bar(labels, values, color=color_palette[:len(labels)])
-    ax.set_xlabel("Vehicle Categories", fontsize=12, fontweight='bold')
-    ax.set_ylabel("Number of Vehicles", fontsize=12, fontweight='bold')
-    ax.set_title("Detected Vehicles in Image", fontsize=14, fontweight='bold')
-
-    plt.xticks(rotation=45, ha='right', fontsize=10)
-    plt.yticks(fontsize=10)
-    plt.tight_layout()
-
-    buf = BytesIO()
-    plt.savefig(buf, format='png')
-    buf.seek(0)
-
-    plt.close(fig)  # ✅ CLOSE THE FIGURE TO FREE MEMORY
-    print("checkl1")
-    return Image.open(buf)
-
-
-def detect_objects_in_video(video_input):
-    cap = cv2.VideoCapture(video_input)
-    if not cap.isOpened():
-        return "Error: Cannot open video file.", None  # Returning a second value (None) to match expected outputs
-
-    frame_width, frame_height, fps = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(
-        cap.get(cv2.CAP_PROP_FRAME_HEIGHT)), int(cap.get(cv2.CAP_PROP_FPS))
-    temp_video_output = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False).name
-    out = cv2.VideoWriter(temp_video_output, cv2.VideoWriter_fourcc(*'mp4v'), fps, (frame_width, frame_height))
-
-    # Initialize the counts for vehicle categories
-    total_counts = {name: 0 for name in ['car', 'truck', 'bus', 'motorcycle', 'bicycle']}
-
-    while cap.isOpened():
-        ret, frame = cap.read()
-        if not ret:
-            break
-
-            
-
-        image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
-
-        # Get frame with detected objects and graph
-        frame_with_boxes, graph_image = detect_objects_in_image(image)
-
-        # Convert image back to OpenCV format for writing video
-        out.write(cv2.cvtColor(np.array(frame_with_boxes), cv2.COLOR_RGB2BGR))
-
-    cap.release()
-    out.release()
-    print("checl2")
-
-    return temp_video_output, graph_image  # Return both expected outputs