set up hls stream interface

app.py

@@ -22,6 +22,8 @@ import torchvision.transforms.functional as F
 from huggingface_hub import hf_hub_download
 from huggingface_hub import HfApi
 
+from hls_download import download_clips
+
 plt.style.use('dark_background')
 
 onnx_file = hf_hub_download(repo_id="dylanplummer/ropenet", filename="nextjump.onnx", repo_type="model", token=os.environ['DATASET_SECRET'])
@@ -50,12 +52,14 @@ def sigmoid(x):
 
 
 @spaces.GPU()
-def inference(x, count_only_api, api_key, 
+def inference(stream_url, start_time, end_time, count_only_api, api_key, 
               img_size=288, seq_len=64, stride_length=32, stride_pad=3, batch_size=4, 
               miss_threshold=0.8, marks_threshold=0.5, median_pred_filter=True, center_crop=True, both_feet=True, 
               api_call=False,
               progress=gr.Progress()):
     progress(0, desc="Starting...")
+
+    x = download_clips(stream_url, os.getcwd(), int(start_time), int(end_time))
     # check if GPU is available
     if torch.cuda.is_available():
         providers = [("CUDAExecutionProvider", {"device_id": torch.cuda.current_device(),
@@ -330,9 +334,13 @@ DESCRIPTION += '\nDemo created by [Dylan Plummer](https://dylan-plummer.github.i
 
 with gr.Blocks(theme='WeixuanYuan/Soft_dark') as demo:
     gr.Markdown(DESCRIPTION)
-    in_video = gr.PlayableVideo(label="Input Video", elem_id='input-video', format='mp4', 
-                                width=400, height=400, interactive=True, container=True,
-                                max_length=150)
+    # in_video = gr.PlayableVideo(label="Input Video", elem_id='input-video', format='mp4', 
+    #                             width=400, height=400, interactive=True, container=True,
+    #                             max_length=150)
+    with gr.Row():
+        in_stream_url = gr.Textbox(label="Stream URL", elem_id='stream-url', visible=True)
+        in_stream_start = gr.Textbox(label="Start Time", elem_id='stream-start', visible=True)
+        in_stream_end = gr.Textbox(label="End Time", elem_id='stream-end', visible=True)
             
     with gr.Row():
         run_button = gr.Button(value="Run", elem_id='run-button', scale=1)
@@ -374,8 +382,19 @@ with gr.Blocks(theme='WeixuanYuan/Soft_dark') as demo:
 
     demo_inference = partial(inference, count_only_api=False, api_key=None)
 
+    # playlist_url = "https://hiemdall-dev2.azurewebsites.net/api/playlist/"
+
+    # stream_urls = [
+    #                #f"{playlist_url}rec_v3v44xW3/vod",
+    #                f"{playlist_url}rec_DA6NyTCw/vod",
+    #                f"{playlist_url}rec_aY68fV8L/vod",
+    #                f"{playlist_url}rec_d9W4ugUl/vod",
+    #                f"{playlist_url}rec_rTxSIdcO/vod",
+    #                f"{playlist_url}rec_rd2FAyUo/vod",
+    #                ]
+
     gr.Examples(examples=[
-                        [os.path.join(os.path.dirname(__file__), "files", "dylan.mp4")],
+                        ['https://hiemdall-dev2.azurewebsites.net/api/playlist/rec_DA6NyTCw/vod', '0', '30'],
                         #[os.path.join(os.path.dirname(__file__), "files", "train14.mp4")],
                         #[os.path.join(os.path.dirname(__file__), "files", "train_17.mp4")],
                         #[os.path.join(os.path.dirname(__file__), "files", "train13.mp4")],
@@ -388,13 +407,13 @@ with gr.Blocks(theme='WeixuanYuan/Soft_dark') as demo:
                         #[os.path.join(os.path.dirname(__file__), "files", "train_66.mp4")],
                         #[os.path.join(os.path.dirname(__file__), "files", "train_21.mp4")]
                     ],
-                inputs=[in_video],
+                inputs=[in_stream_url, in_stream_start, in_stream_end],
                 outputs=[out_text, out_plot, out_hist, out_event_type_dist],
                 fn=demo_inference, cache_examples=os.getenv('SYSTEM') == 'spaces')
     
-    run_button.click(demo_inference, [in_video], outputs=[out_text, out_plot, out_hist, out_event_type_dist])
+    run_button.click(demo_inference, [in_stream_url, in_stream_start, in_stream_end], outputs=[out_text, out_plot, out_hist, out_event_type_dist])
     api_inference = partial(inference, api_call=True)
-    api_dummy_button.click(api_inference, [in_video, count_only, api_token], outputs=[period_length], api_name='inference')
+    api_dummy_button.click(api_inference, [in_stream_url, in_stream_start, in_stream_end, count_only, api_token], outputs=[period_length], api_name='inference')
 
 
 if __name__ == "__main__":

hls_download.py

@@ -0,0 +1,35 @@
+import subprocess
+import os
+# import m3u8
+# import numpy as np
+# import matplotlib.pyplot as plt
+# from tqdm import tqdm
+# from moviepy.editor import VideoFileClip
+# from scipy.signal import correlate, find_peaks
+# from scipy.io import wavfile
+
+
+def download_clips(stream_url, out_dir, start_time, end_time, resize=True):
+    output_file = os.path.join(out_dir, f"train_{len(os.listdir(out_dir))}.mp4")
+    # output video at 30fps
+    if resize:
+        subprocess.run(['ffmpeg', '-i', stream_url, 
+                        '-ss', str(start_time - 2), 
+                        '-to', str(end_time + 11),  # pad end time by a couple of seconds
+                        '-r', str(30), 
+                        '-vcodec', 'libx264',
+                        '-crf', str(24),
+                        '-preset', 'fast',
+                        '-vf', 'scale=-2:540',
+                        '-c:a', 'copy', output_file])
+    else:
+        subprocess.run(['ffmpeg', '-i', stream_url, 
+                        '-ss', str(start_time - 2), 
+                        '-to', str(end_time + 11),  # pad end time by a couple of seconds
+                        '-r', str(30), 
+                        '-vcodec', 'libx264',
+                        '-crf', str(24),
+                        '-preset', 'fast',
+                        '-vf', 'scale=-2:720',
+                        '-c:a', 'copy', output_file])
+    return output_file