update

app.py

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+import gradio as gr
+#import torch
+import yolov7
+import subprocess
+import tempfile
+import time
+from pathlib import Path
+
+import cv2
+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')
+    
+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/model_baseline",
+            "alshimaa/model_yolo7",
+            #"kadirnar/yolov7-v0.1",
+        ],
+        default="alshimaa/model_baseline",
+        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/model_yolo7', 640, 0.25, 0.45], ['image2.jpg', 'alshimaa/model_yolo7', 640, 0.25, 0.45], ['image3.jpg', 'alshimaa/model_yolo7', 640, 0.25, 0.45]],
+    cache_examples=True,
+    theme='huggingface',
+)
+
+
+video_interface = gr.Interface(
+    fn=video_fn,
+    inputs=[
+        gr.Video(type="file"),
+        gr.inputs.Dropdown(
+        choices=[
+            "alshimaa/model_baseline",
+            "alshimaa/model_yolo7",
+            #"kadirnar/yolov7-v0.1",
+        ],
+        default="alshimaa/model_baseline",
+        label="Model",
+    ),
+    ],
+    outputs=gr.outputs.Video(type="filepath", format="mp4", label="Output Video"),
+    # examples=[
+    #     ["video.mp4", 0.25, 0.45, 0, 2],
+       
+    # ],
+    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()
+
+# import subprocess
+# import tempfile
+# import time
+# from pathlib import Path
+
+# import cv2
+# import gradio as gr
+
+# from inferer import Inferer
+
+# pipeline = Inferer("alshimaa/model_yolo7", device='cuda')
+
+
+# def fn_image(image, conf_thres, iou_thres):
+#     return pipeline(image, conf_thres, iou_thres)
+
+
+# def fn_video(video_file, conf_thres, iou_thres, start_sec, duration):
+#     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(pipeline(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=fn_image,
+#     inputs=[
+#         "image",
+#         gr.Slider(0, 1, value=0.5, label="Confidence Threshold"),
+#         gr.Slider(0, 1, value=0.5, label="IOU Threshold"),
+#     ],
+#     outputs=gr.Image(type="file"),
+#     examples=[["image1.jpg", 0.5, 0.5], ["image2.jpg", 0.25, 0.45], ["image3.jpg", 0.25, 0.45]],
+#     title="Smart Environmental Eye (SEE)",
+#     allow_flagging=False,
+#     allow_screenshot=False,
+# )
+
+# video_interface = gr.Interface(
+#     fn=fn_video,
+#     inputs=[
+#         gr.Video(type="file"),
+#         gr.Slider(0, 1, value=0.25, label="Confidence Threshold"),
+#         gr.Slider(0, 1, value=0.45, label="IOU Threshold"),
+#         gr.Slider(0, 10, value=0, label="Start Second", step=1),
+#         gr.Slider(0, 10 if pipeline.device.type != 'cpu' else 3, value=4, label="Duration", step=1),
+#     ],
+#     outputs=gr.Video(type="file", format="mp4"),
+#     # examples=[
+#     #     ["video.mp4", 0.25, 0.45, 0, 2],
+       
+#     # ],
+#     title="Smart Environmental Eye (SEE)",
+#     allow_flagging=False,
+#     allow_screenshot=False,
+# )
+
+
+
+# if __name__ == "__main__":
+#     gr.TabbedInterface(
+#         [image_interface, video_interface],
+#         ["Run on Images", "Run on Videos"],
+#     ).launch()
+