commit

app.py

@@ -16,7 +16,7 @@ import gradio as gr
 # o_file('https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg', 'zidane.jpg')
 # #torch.hub.download_url_to_file('https://raw.githubusercontent.com/obss/sahi/main/tests/data/small-vehicles1.jpeg', 'small-vehicles1.jpeg')
 
-   
+    
 def image_fn(
     image: gr.inputs.Image = None,
     model_path: gr.inputs.Dropdown = None,
@@ -42,18 +42,64 @@ def image_fn(
     results = model([image], size=image_size)
     return results.render()[0]
   
+  
+        
+def video_fn(model_path, video_file, conf_thres, iou_thres, start_sec, duration):
+    model = yolov7.load(model_path, device="cpu", hf_model=True, trace=False)
+    start_timestamp = time.strftime("%H:%M:%S", time.gmtime(start_sec))
+    end_timestamp = time.strftime("%H:%M:%S", time.gmtime(start_sec + duration))
 
+    suffix = Path(video_file).suffix
 
-demo_app = gr.Interface(
+    clip_temp_file = tempfile.NamedTemporaryFile(suffix=suffix)
+    subprocess.call(
+        f"ffmpeg -y -ss {start_timestamp} -i {video_file} -to {end_timestamp} -c copy {clip_temp_file.name}".split()
+    )
+
+    # Reader of clip file
+    cap = cv2.VideoCapture(clip_temp_file.name)
+
+    # This is an intermediary temp file where we'll write the video to
+    # Unfortunately, gradio doesn't play too nice with videos rn so we have to do some hackiness
+    # with ffmpeg at the end of the function here.
+    with tempfile.NamedTemporaryFile(suffix=".mp4") as temp_file:
+        out = cv2.VideoWriter(temp_file.name, cv2.VideoWriter_fourcc(*"MP4V"), 30, (1280, 720))
+
+        num_frames = 0
+        max_frames = duration * 30
+        while cap.isOpened():
+            try:
+                ret, frame = cap.read()
+                if not ret:
+                    break
+            except Exception as e:
+                print(e)
+                continue
+            print("FRAME DTYPE", type(frame))
+            out.write(model([frame], conf_thres, iou_thres))
+            num_frames += 1
+            print("Processed {} frames".format(num_frames))
+            if num_frames == max_frames:
+                break
+
+        out.release()
+
+        # Aforementioned hackiness
+        out_file = tempfile.NamedTemporaryFile(suffix="out.mp4", delete=False)
+        subprocess.run(f"ffmpeg -y -loglevel quiet -stats -i {temp_file.name} -c:v libx264 {out_file.name}".split())
+
+    return out_file.name
+
+image_interface = gr.Interface(
     fn=image_fn,
     inputs=[
     gr.inputs.Image(type="pil", label="Input Image"),
     gr.inputs.Dropdown(
         choices=[
-            "alshimaa/yolo5_epoch100",
+            "alshimaa/SEE_model_yolo7",
             #"kadirnar/yolov7-v0.1",
         ],
-        default="alshimaa/yolo5_epoch100",
+        default="alshimaa/SEE_model_yolo7",
         label="Model",
     )
     #gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size")
@@ -61,140 +107,41 @@ demo_app = gr.Interface(
     #gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold")
 ],
     outputs=gr.outputs.Image(type="filepath", label="Output Image"),
-    title="Object Detector: Identify People Without Mask",
-    examples=[['img1.png', 'alshimaa/yolo5_epoch100', 640, 0.25, 0.45], ['img2.png', 'alshimaa/yolo5_epoch100', 640, 0.25, 0.45], ['img3.png', 'alshimaa/yolo5_epoch100', 640, 0.25, 0.45]],
+    title="Smart Environmental Eye (SEE)",
+    examples=[['image1.jpg', 'alshimaa/SEE_model_yolo7', 640, 0.25, 0.45], ['image2.jpg', 'alshimaa/SEE_model_yolo7', 640, 0.25, 0.45], ['image3.jpg', 'alshimaa/SEE_model_yolo7', 640, 0.25, 0.45]],
     cache_examples=True,
-    live=True,
     theme='huggingface',
 )
-demo_app.launch(debug=True, enable_queue=True)
 
-    
-# def image_fn(
-#     image: gr.inputs.Image = None,
-#     model_path: gr.inputs.Dropdown = None,
-#     image_size: gr.inputs.Slider = 640,
-#     conf_threshold: gr.inputs.Slider = 0.25,
-#     iou_threshold: gr.inputs.Slider = 0.45,
-# ):
-#     """
-#     YOLOv7 inference function
-#     Args:
-#         image: Input image
-#         model_path: Path to the model
-#         image_size: Image size
-#         conf_threshold: Confidence threshold
-#         iou_threshold: IOU threshold
-#     Returns:
-#         Rendered image
-#     """
-
-#     model = yolov7.load(model_path, device="cpu", hf_model=True, trace=False)
-#     model.conf = conf_threshold
-#     model.iou = iou_threshold
-#     results = model([image], size=image_size)
-#     return results.render()[0]
-  
-  
-        
-# def video_fn(model_path, video_file, conf_thres, iou_thres, start_sec, duration):
-#     model = yolov7.load(model_path, device="cpu", hf_model=True, trace=False)
-#     start_timestamp = time.strftime("%H:%M:%S", time.gmtime(start_sec))
-#     end_timestamp = time.strftime("%H:%M:%S", time.gmtime(start_sec + duration))
-
-#     suffix = Path(video_file).suffix
-
-#     clip_temp_file = tempfile.NamedTemporaryFile(suffix=suffix)
-#     subprocess.call(
-#         f"ffmpeg -y -ss {start_timestamp} -i {video_file} -to {end_timestamp} -c copy {clip_temp_file.name}".split()
-#     )
-
-#     # Reader of clip file
-#     cap = cv2.VideoCapture(clip_temp_file.name)
-
-#     # This is an intermediary temp file where we'll write the video to
-#     # Unfortunately, gradio doesn't play too nice with videos rn so we have to do some hackiness
-#     # with ffmpeg at the end of the function here.
-#     with tempfile.NamedTemporaryFile(suffix=".mp4") as temp_file:
-#         out = cv2.VideoWriter(temp_file.name, cv2.VideoWriter_fourcc(*"MP4V"), 30, (1280, 720))
-
-#         num_frames = 0
-#         max_frames = duration * 30
-#         while cap.isOpened():
-#             try:
-#                 ret, frame = cap.read()
-#                 if not ret:
-#                     break
-#             except Exception as e:
-#                 print(e)
-#                 continue
-#             print("FRAME DTYPE", type(frame))
-#             out.write(model([frame], conf_thres, iou_thres))
-#             num_frames += 1
-#             print("Processed {} frames".format(num_frames))
-#             if num_frames == max_frames:
-#                 break
-
-#         out.release()
-
-#         # Aforementioned hackiness
-#         out_file = tempfile.NamedTemporaryFile(suffix="out.mp4", delete=False)
-#         subprocess.run(f"ffmpeg -y -loglevel quiet -stats -i {temp_file.name} -c:v libx264 {out_file.name}".split())
-
-#     return out_file.name
-
-# image_interface = gr.Interface(
-#     fn=image_fn,
-#     inputs=[
-#     gr.inputs.Image(type="pil", label="Input Image"),
-#     gr.inputs.Dropdown(
-#         choices=[
-#             "alshimaa/SEE_model_yolo7",
-#             #"kadirnar/yolov7-v0.1",
-#         ],
-#         default="alshimaa/SEE_model_yolo7",
-#         label="Model",
-#     )
-#     #gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size")
-#     #gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),
-#     #gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold")
-# ],
-#     outputs=gr.outputs.Image(type="filepath", label="Output Image"),
-#     title="Smart Environmental Eye (SEE)",
-#     examples=[['image1.jpg', 'alshimaa/SEE_model_yolo7', 640, 0.25, 0.45], ['image2.jpg', 'alshimaa/SEE_model_yolo7', 640, 0.25, 0.45], ['image3.jpg', 'alshimaa/SEE_model_yolo7', 640, 0.25, 0.45]],
-#     cache_examples=True,
-#     theme='huggingface',
-# )
-
-
-# video_interface = gr.Interface(
-#     fn=video_fn,
-#     inputs=[
-#         gr.inputs.Video(source = "upload", type = "mp4", label = "Input Video"),
-#         gr.inputs.Dropdown(
-#         choices=[
-#             "alshimaa/SEE_model_yolo7",
-#             #"kadirnar/yolov7-v0.1",
-#         ],
-#         default="alshimaa/SEE_model_yolo7",
-#         label="Model",
-#     ),
-#     ],
-#     outputs=gr.outputs.Video(type = "mp4", label = "Output Video"),
-#     # examples=[
-#     #     ["video.mp4", 0.25, 0.45, 0, 2],
+
+video_interface = gr.Interface(
+    fn=video_fn,
+    inputs=[
+        gr.inputs.Video(source = "upload", type = "mp4", label = "Input Video"),
+        gr.inputs.Dropdown(
+        choices=[
+            "alshimaa/SEE_model_yolo7",
+            #"kadirnar/yolov7-v0.1",
+        ],
+        default="alshimaa/SEE_model_yolo7",
+        label="Model",
+    ),
+    ],
+    outputs=gr.outputs.Video(type = "mp4", label = "Output Video"),
+    # examples=[
+    #     ["video.mp4", 0.25, 0.45, 0, 2],
        
-#     # ],
-#     title="Smart Environmental Eye (SEE)",
-#     cache_examples=True,
-#     theme='huggingface',
+    # ],
+    title="Smart Environmental Eye (SEE)",
+    cache_examples=True,
+    theme='huggingface',
    
-# )
+)
 
-# if __name__ == "__main__":
-#     gr.TabbedInterface(
-#         [image_interface, video_interface],
-#         ["Run on Images", "Run on Videos"],
-#     ).launch()
+if __name__ == "__main__":
+    gr.TabbedInterface(
+        [image_interface, video_interface],
+        ["Run on Images", "Run on Videos"],
+    ).launch()