update

app.py

@@ -1,249 +0,0 @@
-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.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()
-
-# 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()
-