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import subprocess
import os
if os.getenv('SYSTEM') == 'spaces':
subprocess.call('pip install -U openmim'.split())
subprocess.call('pip install python-dotenv'.split())
subprocess.call('pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu113'.split())
subprocess.call('mim install mmcv>=2.0.0'.split())
subprocess.call('mim install mmengine'.split())
subprocess.call('mim install mmdet'.split())
subprocess.call('pip install opencv-python-headless==4.5.5.64'.split())
subprocess.call('pip install git+https://github.com/cocodataset/panopticapi.git'.split())
import gradio as gr
from huggingface_hub import snapshot_download
import cv2
import dotenv
dotenv.load_dotenv()
import numpy as np
import gradio as gr
import glob
from inference import inference_frame,inference_frame_serial
from inference import inference_frame_par_ready
from inference import process_frame
import os
import pathlib
import multiprocessing as mp
from time import time
REPO_ID='SharkSpace/videos_examples'
snapshot_download(repo_id=REPO_ID, token=os.environ.get('SHARK_MODEL'),repo_type='dataset',local_dir='videos_example')
def process_video(input_video):
cap = cv2.VideoCapture(input_video)
output_path = "output.mp4"
fps = int(cap.get(cv2.CAP_PROP_FPS))
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
video = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (width, height))
iterating, frame = cap.read()
while iterating:
# flip frame vertically
display_frame = inference_frame_serial(frame)
video.write(frame)
yield display_frame, None
iterating, frame = cap.read()
video.release()
yield display_frame, output_path
with gr.Blocks() as demo:
with gr.Row():
input_video = gr.Video(label="input")
processed_frames = gr.Image(label="last frame")
output_video = gr.Video(label="output")
with gr.Row():
paths = sorted(pathlib.Path('videos_example/').rglob('*.mp4'))
samples=[[path.as_posix()] for path in paths if 'raw_videos' in str(path)]
examples = gr.Examples(samples, inputs=input_video)
process_video_btn = gr.Button("process video")
process_video_btn.click(process_video, input_video, [processed_frames, output_video])
demo.queue()
demo.launch() |