Added video output support for gradio (#113)

added video output support for gradio

---------

Co-authored-by: Sencer Yücel <sencer.yucel@turkai.com>
Co-authored-by: wa22 <wa22@mails.tsinghua.edu.cn>

app.py

@@ -1,30 +1,60 @@
-import PIL.Image as Image
 import gradio as gr
-
+import cv2
+import tempfile
 from ultralytics import YOLOv10
 
-def predict_image(img, model_id, image_size, conf_threshold):
+
+def yolov10_inference(image, video, model_id, image_size, conf_threshold):
     model = YOLOv10.from_pretrained(f'jameslahm/{model_id}')
-    results = model.predict(
+    if image:
-        source=img,
+        results = model.predict(source=image, imgsz=image_size, conf=conf_threshold)
-        conf=conf_threshold,
+        annotated_image = results[0].plot()
-        show_labels=True,
+        return annotated_image[:, :, ::-1], None
-        show_conf=True,
+    else:
-        imgsz=image_size,
+        video_path = tempfile.mktemp(suffix=".webm")
-    )
+        with open(video_path, "wb") as f:
+            with open(video, "rb") as g:
+                f.write(g.read())
 
-    for r in results:
+        cap = cv2.VideoCapture(video_path)
-        im_array = r.plot()
+        fps = cap.get(cv2.CAP_PROP_FPS)
-        im = Image.fromarray(im_array[..., ::-1])
+        frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+        frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+
+        output_video_path = tempfile.mktemp(suffix=".webm")
+        out = cv2.VideoWriter(output_video_path, cv2.VideoWriter_fourcc(*'vp80'), fps, (frame_width, frame_height))
+
+        while cap.isOpened():
+            ret, frame = cap.read()
+            if not ret:
+                break
+
+            results = model.predict(source=frame, imgsz=image_size, conf=conf_threshold)
+            annotated_frame = results[0].plot()
+            out.write(annotated_frame)
+
+        cap.release()
+        out.release()
+
+        return None, output_video_path
+
+
+def yolov10_inference_for_examples(image, model_path, image_size, conf_threshold):
+    annotated_image, _ = yolov10_inference(image, None, model_path, image_size, conf_threshold)
+    return annotated_image
 
-    return im
 
 def app():
     with gr.Blocks():
         with gr.Row():
             with gr.Column():
-                image = gr.Image(type="pil", label="Image")
+                image = gr.Image(type="pil", label="Image", visible=True)
-                
+                video = gr.Video(label="Video", visible=False)
+                input_type = gr.Radio(
+                    choices=["Image", "Video"],
+                    value="Image",
+                    label="Input Type",
+                )
                 model_id = gr.Dropdown(
                     label="Model",
                     choices=[
@@ -54,17 +84,34 @@ def app():
                 yolov10_infer = gr.Button(value="Detect Objects")
 
             with gr.Column():
-                output_image = gr.Image(type="pil", label="Annotated Image")
+                output_image = gr.Image(type="numpy", label="Annotated Image", visible=True)
+                output_video = gr.Video(label="Annotated Video", visible=False)
+
+        def update_visibility(input_type):
+            image = gr.update(visible=True) if input_type == "Image" else gr.update(visible=False)
+            video = gr.update(visible=False) if input_type == "Image" else gr.update(visible=True)
+            output_image = gr.update(visible=True) if input_type == "Image" else gr.update(visible=False)
+            output_video = gr.update(visible=False) if input_type == "Image" else gr.update(visible=True)
+
+            return image, video, output_image, output_video
+
+        input_type.change(
+            fn=update_visibility,
+            inputs=[input_type],
+            outputs=[image, video, output_image, output_video],
+        )
+
+        def run_inference(image, video, model_id, image_size, conf_threshold, input_type):
+            if input_type == "Image":
+                return yolov10_inference(image, None, model_id, image_size, conf_threshold)
+            else:
+                return yolov10_inference(None, video, model_id, image_size, conf_threshold)
+
 
         yolov10_infer.click(
-            fn=predict_image,
+            fn=run_inference,
-            inputs=[
+            inputs=[image, video, model_id, image_size, conf_threshold, input_type],
-                image,
+            outputs=[output_image, output_video],
-                model_id,
-                image_size,
-                conf_threshold,
-            ],
-            outputs=[output_image],
         )
 
         gr.Examples(
@@ -82,7 +129,7 @@ def app():
                     0.25,
                 ],
             ],
-            fn=predict_image,
+            fn=yolov10_inference_for_examples,
             inputs=[
                 image,
                 model_id,