commit

app.py

@@ -1,6 +1,6 @@
 import gradio as gr
 #import torch
-import yolov7
+import yolov5
 import subprocess
 import tempfile
 import time
@@ -11,10 +11,10 @@ import gradio as gr
 
 
 
-# Images
-#torch.hub.download_url_to_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')
-    
+# # Images
+# #torch.hub.download_url_to_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,
@@ -23,7 +23,7 @@ def image_fn(
     iou_threshold: gr.inputs.Slider = 0.45,
 ):
     """
-    YOLOv7 inference function
+    YOLOv5 inference function
     Args:
         image: Input image
         model_path: Path to the model
@@ -34,70 +34,24 @@ def image_fn(
         Rendered image
     """
 
-    model = yolov7.load(model_path, device="cpu", hf_model=True, trace=False)
+    model = yolov5.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(
+demo_app = gr.Interface(
     fn=image_fn,
     inputs=[
     gr.inputs.Image(type="pil", label="Input Image"),
     gr.inputs.Dropdown(
         choices=[
-            "alshimaa/SEE_model_yolo7",
+            "alshimaa/yolo5_epoch100",
             #"kadirnar/yolov7-v0.1",
         ],
-        default="alshimaa/SEE_model_yolo7",
+        default="alshimaa/yolo5_epoch100",
         label="Model",
     )
     #gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size")
@@ -105,39 +59,140 @@ image_interface = 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="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]],
+    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]],
     cache_examples=True,
+    live=True,
     theme='huggingface',
 )
+demo_app.launch(debug=True, enable_queue=True)
 
-
-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],
+    
+# 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,
+# ):
+#     """
+#     YOLOv5 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 = yolov5.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 = yolov5.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],
        
-    # ],
-    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()
+