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README.md

@@ -9,4 +9,4 @@ app_file: app.py
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,61 @@
+import io
+import numpy
+import gradio
+import spaces
+import moviepy
+import supervision
+from PIL import Image
+from ultralytics import YOLOE
+
+
+@spaces.GPU
+def inference(video_path):
+    model = YOLOE("./model.pt")
+    names = ["person", "vehicle"]
+    model.set_classes(names, model.get_text_pe(names))
+    clip = moviepy.VideoFileClip(video_path)
+    results = []
+    for i, frame in enumerate(clip.iter_frames(fps=1)):
+        image = Image.fromarray(numpy.uint8(frame))
+        result = model.predict(frame, imgsz=640, conf=0.25, iou=0.7)
+        detections = supervision.Detections.from_ultralytics(result[0])
+        resolution_wh = image.size
+        thickness = supervision.calculate_optimal_line_thickness(resolution_wh=resolution_wh)
+        text_scale = supervision.calculate_optimal_text_scale(resolution_wh=resolution_wh)
+        labels = [
+            f"{class_name} {confidence:.2f}"
+            for class_name, confidence
+            in zip(detections['class_name'], detections.confidence)
+        ]
+        annotated_image = image.copy()
+        annotated_image = supervision.MaskAnnotator(color_lookup=supervision.ColorLookup.INDEX, opacity=0.4).annotate(
+            scene=annotated_image, detections=detections)
+        annotated_image = supervision.BoxAnnotator(color_lookup=supervision.ColorLookup.INDEX,
+                                                   thickness=thickness).annotate(
+            scene=annotated_image, detections=detections)
+        annotated_image = supervision.LabelAnnotator(color_lookup=supervision.ColorLookup.INDEX, text_scale=text_scale,
+                                                     smart_position=True).annotate(
+            scene=annotated_image, detections=detections, labels=labels)
+        results.append(annotated_image)
+    frames = [numpy.array(img) for img in results]
+    output_clip = moviepy.ImageSequenceClip(frames, fps=1)
+    buf = io.BytesIO()
+    output_clip.write_videofile(buf, codec="libx264", audio=False)
+    clip.close()
+    buf.seek(0)
+    return buf
+
+
+def gradio_interface(video_file):
+    output_video = inference(video_file.name)
+    return output_video
+
+
+if __name__ == "__main__":
+    gradio.Interface(
+        fn=gradio_interface,
+        inputs=gradio.Video(type="file"),
+        outputs=gradio.Video(),
+        title="Video Object Detection",
+        description="Upload a video to run object detection using YOLOE.",
+    ).launch()

model.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bf39e17c356f4b73977aa978bbdf6d19d0830e6bff46727d2d1f260414e41e0f
+size 74264838

requirements.txt

@@ -0,0 +1,5 @@
+ultralytics
+supervision
+moviepy
+spaces
+numpy