Update app.py

app.py

@@ -76,17 +76,74 @@ def predict2(image_np):
     result_pil_img = tf.keras.utils.array_to_img(image_np_with_detections[0])
     
     return result_pil_img
-
-
+###
+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)
+    )
+    while True:
+        ret, frame = video_reader.read()
+        if not ret:
+            break  # Break the loop if the video is finished
+        
+        processed_frame = predict(frame)
+        processed_frame_np = np.array(processed_frame)
+        video_writer.write(processed_frame_np)
+                
+    # Release camera and close windows
+    video_reader.release()
+    video_writer.release() 
+    cv2.destroyAllWindows() 
+    cv2.waitKey(1)        
+    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
+    predict_on_video(video_path, output_path, detection_model, category_index)
+    return output_path
+
+# Specify paths to example images
+sample_images = [["sample1.jpg"], ["sample2.jpg"],
+                 ["sample3.jpg"]
+                ]
+###
 REPO_ID = "gregarific/assignmodel"
 detection_model = load_model()
 # pil_image = Image.open(image_path)
 # image_arr = pil_image_as_numpy_array(pil_image)
-
-# predicted_img = predict(image_arr)
-# predicted_img.save('predicted.jpg')
-
-gr.Interface(fn=predict,
+###
+tab1 = gr.Interface(fn=predict,
              inputs=gr.Image(type="pil"),
-             outputs=gr.Image(type="pil")
-             ).launch(share=True)
+             outputs=gr.Image(type="pil"), 
+             examples=sample_images, 
+             title="Image - Object Detection (WheelChair vs Motorized WheelChair)",
+             description='Model used: SSD MobileNet V1 320x320.'
+             )
+
+#gr.Interface(fn=predict,
+#             inputs=gr.Image(type="pil"),
+#             outputs=gr.Image(type="pil")
+#            ).launch(share=True)
+tab2 = gr.Interface(
+    fn=process_video,
+    inputs=gr.File(label="Upload a video"),
+    outputs=gr.File(label="output"),
+    title='Video - Object Detection (WheelChair Type)'
+)
+
+
+iface = gr.TabbedInterface([tab1, tab2], tab_names  = ['Image','Video'], title='WheelChair Type Detection')
+
+iface.launch(share=True)