SEE with yolov7

app.py

@@ -94,11 +94,10 @@ image_interface = gr.Interface(
     gr.inputs.Image(type="pil", label="Input Image"),
     gr.inputs.Dropdown(
         choices=[
-            "alshimaa/model_baseline",
-            "alshimaa/model_yolo7",
+            "alshimaa/yolov7",
             #"kadirnar/yolov7-v0.1",
         ],
-        default="alshimaa/model_baseline",
+        default="alshimaa/yolov7",
         label="Model",
     )
     #gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size")
@@ -107,7 +106,7 @@ image_interface = gr.Interface(
 ],
     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]],
+    examples=[['image1.jpg', 'alshimaa/yolov7', 640, 0.25, 0.45], ['image2.jpg', 'alshimaa/yolov7', 640, 0.25, 0.45], ['image3.jpg', 'alshimaa/yolov7', 640, 0.25, 0.45]],
     cache_examples=True,
     theme='huggingface',
 )
@@ -119,11 +118,10 @@ video_interface = gr.Interface(
         gr.inputs.Video(source = "upload", type = "mp4", label = "Input Video"),
         gr.inputs.Dropdown(
         choices=[
-            "alshimaa/model_baseline",
-            "alshimaa/model_yolo7",
+            "alshimaa/yolov7",
             #"kadirnar/yolov7-v0.1",
         ],
-        default="alshimaa/model_baseline",
+        default="alshimaa/yolov7",
         label="Model",
     ),
     ],
@@ -143,108 +141,3 @@ if __name__ == "__main__":
         [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()
-

yolov7

@@ -1 +1 @@
-Subproject commit 8c672fc62ebd20600909537c2a6a4da97374346d
+Subproject commit 139f2ba03d7ed9965695775507ecb8fb7ca2b326