update limits

LHM/utils/ffmpeg_utils.py

@@ -48,19 +48,20 @@ def images_to_video(images, output_path, fps, gradio_codec: bool, verbose=False,
                 f"Frame value out of range: {frame.min()} ~ {frame.max()}"
         else:
             frame = images[i]
-        # width, height = frame.shape[1], frame.shape[0]
+        width, height = frame.shape[1], frame.shape[0]
         # reshape to limit the export time
-        # if width > 1200 or height > 1200 or images.shape[0] > 400:
-        #     frames.append(cv2.resize(frame, (width // 2, height // 2)))
+        if width > 1200 or height > 1200 or images.shape[0] > 200:
+            frames.append(cv2.resize(frame, (width // 2, height // 2)))
         frames.append(frame)
-        # limit the frames directly @NOTE huggingface only！
-    frames = frames[:300]
+    # limit the frames directly @NOTE huggingface only！
+    frames = frames[:200]
     
     frames = np.stack(frames)
 
-    try:
-        imageio.mimwrite(output_path, frames, fps=30, codec="libx264")
-        print("save {} using imageio.".format(output_path))
-    except:
-        iio.imwrite(output_path,frames,fps=fps,codec="libx264",pixelformat="yuv420p",bitrate=bitrate,macro_block_size=16)
-        print("save {} using imageio.v3 .".format(output_path))
+    # try:
+    #     imageio.mimwrite(output_path, frames, fps=30, codec="libx264")
+    #     print("save {} using imageio.".format(output_path))
+    # except:
+    print("start saving {} using imageio.v3 .".format(output_path))
+    iio.imwrite(output_path,frames,fps=fps,codec="libx264",pixelformat="yuv420p",bitrate=bitrate, macro_block_size=32)
+    print("saved {} using imageio.v3 .".format(output_path))

app.py

@@ -589,11 +589,6 @@ def demo_lhm(pose_estimator, face_detector, lhm, cfg):
 
         os.makedirs(os.path.dirname(dump_video_path), exist_ok=True)
 
-        return rgb, dump_image_path, dump_video_path
-
-    def core_fn_export(image, video_params, working_dir):
-        rgb, dump_image_path, dump_video_path = core_fn(image=image, video_params=video_params, working_dir=working_dir)
-        print("start to export the video.")
         images_to_video(
             rgb,
             output_path=dump_video_path,
@@ -601,8 +596,22 @@ def demo_lhm(pose_estimator, face_detector, lhm, cfg):
             gradio_codec=False,
             verbose=True,
         )
-        
+
         return dump_image_path, dump_video_path
+        # return rgb, dump_image_path, dump_video_path
+
+    # def core_fn_export(image, video_params, working_dir):
+    #     rgb, dump_image_path, dump_video_path = core_fn(image=image, video_params=video_params, working_dir=working_dir)
+    #     print("start to export the video.")
+    #     images_to_video(
+    #         rgb,
+    #         output_path=dump_video_path,
+    #         fps=30,
+    #         gradio_codec=False,
+    #         verbose=True,
+    #     )
+        
+    #     return dump_image_path, dump_video_path
 
     _TITLE = '''LHM: Large Animatable Human Model'''
 
@@ -728,7 +737,7 @@ def demo_lhm(pose_estimator, face_detector, lhm, cfg):
             outputs=[working_dir],
             queue=False,
         ).success(
-            fn=core_fn_export,
+            fn=core_fn,
             inputs=[input_image, video_input, working_dir], # video_params refer to smpl dir
             outputs=[processed_image, output_video],
         )
@@ -771,17 +780,7 @@ def launch_gradio_app():
 
     demo_lhm(pose_estimator, facedetector, lhm, cfg)
 
-
-
 if __name__ == '__main__':
     launch_pretrained()
     # launch_env_not_compile_with_cuda()
-    launch_gradio_app()
-
-# import gradio as gr
-
-# def greet(name):
-#     return "Hello " + name + "!!"
-
-# demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-# demo.launch()
+    launch_gradio_app()