Create app.py

app.py

@@ -0,0 +1,51 @@
+import cv2
+import gradio as gr
+import supervision as sv
+from ultralytics import YOLO
+from PIL import Image
+import torch
+import time
+import numpy as np
+import uuid
+model = YOLO("yolov8s.pt")
+
+def stream_object_detection(video):
+    cap = cv2.VideoCapture(video)
+    # This means we will output mp4 videos
+    video_codec = cv2.VideoWriter_fourcc(*"mp4v") # type: ignore
+    fps = int(cap.get(cv2.CAP_PROP_FPS))
+    desired_fps = fps // SUBSAMPLE
+    width  = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) // 2
+    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) // 2
+    iterating, frame = cap.read()
+    n_frames = 0
+    output_video_name = f"output_{uuid.uuid4()}.mp4"
+    output_video = cv2.VideoWriter(output_video_name, video_codec, desired_fps, (width, height)) # type: ignore
+
+    while iterating:
+        frame = cv2.resize( frame, (0,0), fx=0.5, fy=0.5)
+        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        result = model(Image.fromarray(frame))[0]
+        detections = sv.Detections.from_ultralytics(result)
+        outp = draw_box(frame,detections)
+        frame = np.array(outp)
+        # Convert RGB to BGR
+        frame = frame[:, :, ::-1].copy()
+        output_video.write(frame)
+        batch = []
+        output_video.release()
+        yield output_video_name
+        output_video_name = f"output_{uuid.uuid4()}.mp4"
+        output_video = cv2.VideoWriter(output_video_name, video_codec, desired_fps, (width, height)) # type: ignore
+        iterating, frame = cap.read()
+        n_frames += 1
+
+with gr.Blocks() as app:
+    #inp = gr.Image(type="filepath")
+    with gr.Row():
+        with gr.Column():
+            inp = gr.Video()
+            btn = gr.Button()
+        outp_v = gr.Video(label="Processed Video", streaming=True, autoplay=True)
+    btn.click(stream_object_detection,inp,[outp_v])
+app.queue(concurrency_limit=20).launch()