Update app.py

app.py

@@ -63,10 +63,6 @@ def predict(image_np):
     
     return result_pil_img
 
-# def predict2(pilimg):
-#     image = None
-#     image = load_image_into_numpy_array(pilimg)
-#     return predict(image)
     
 detection_model = load_model()
 
@@ -74,18 +70,67 @@ detection_model = load_model()
 sample_images = [["test_1.jpg"],["test_9.jpg"],["test_6.jpg"],["test_7.jpg"],
                  ["test_10.jpg"], ["test_11.jpg"],["test_8.jpg"]]
 
-# Create a list of example inputs and outputs using a for loop
-# example_inputs = [Image.open(image) for image in sample_images]
-# example_outputs = [predict(input_image) for input_image in example_inputs]
-
-# Save the example output image
-# example_outputs[0].save("/home/user/app/predicted_1.jpg")
-
-iface = gr.Interface(fn=predict,
+tab1 = gr.Interface(fn=predict,
                      inputs=gr.Image(label='Upload an expressway image', type="pil"),
                      outputs=gr.Image(type="pil"),
                      title='Blue and Yellow Taxi detection in live expressway traffic conditions (data.gov.sg)',
                      examples = sample_images
                     )
 
+def predict_on_video(video_in_filepath, video_out_filepath, detection_model, category_index):
+    video_reader = cv2.VideoCapture(video_in_filepath)
+    
+    frame_h = int(video_reader.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    frame_w = int(video_reader.get(cv2.CAP_PROP_FRAME_WIDTH))
+    fps = video_reader.get(cv2.CAP_PROP_FPS)
+
+    video_writer = cv2.VideoWriter(
+        video_out_filepath,
+        cv2.VideoWriter_fourcc(*'mp4v'),
+        fps,
+        (frame_w, frame_h)
+    )
+
+    label_id_offset = 0
+
+    while True:
+        ret, frame = video_reader.read()
+
+        if not ret:
+            break  # Break the loop if the video is finished
+
+        processed_frame = predict(frame, detection_model, category_index, label_id_offset)
+
+        # Convert processed frame to numpy array
+        processed_frame_np = np.array(processed_frame)
+
+        # Write the frame to the output video
+        video_writer.write(processed_frame_np)
+
+    # Release video reader and writer
+    video_reader.release()
+    video_writer.release()
+    cv2.destroyAllWindows()
+    cv2.waitKey(1)
+
+# Function to process a video
+def process_video(video_path):
+    output_path = "output_video.mp4"  # Output path for the processed video
+    # Assuming you have detection_model and category_index defined
+    predict_on_video(video_path, output_path, detection_model, category_index)
+    return output_path
+
+# Create the video processing interface
+tab2 = gr.Interface(
+    fn=process_video,
+    inputs=gr.File(label="Upload a video", type="video", accept=".mp4"),
+    outputs=gr.File(type="video/mp4"),
+    title='Video Processing',
+    examples=["example_video.mp4"]
+)
+
+# Create a Tabbed Interface
+iface = gr.Tabbed(tab1, tab2)
+
+# Launch the interface
 iface.launch(share=True)