Spaces:
Runtime error
Runtime error
File size: 3,839 Bytes
cd0d6f2 6454b14 eddda5a 7576d10 b73d81d cd0d6f2 5bbee66 6e8c2ef 4d701c0 eddda5a e213266 7576d10 6454b14 8353801 cd0d6f2 8353801 02cdb95 021ea63 5636b5c 588ce8d 8353801 cd0d6f2 5636b5c f3a075d 02cdb95 f3a075d 6454b14 021ea63 588ce8d 6454b14 021ea63 6454b14 5636b5c 6454b14 5636b5c 6454b14 5636b5c 6454b14 5636b5c 6454b14 588ce8d 6454b14 588ce8d 6454b14 588ce8d 6454b14 5636b5c 6454b14 588ce8d 5636b5c 588ce8d 6454b14 5636b5c 6454b14 588ce8d 7576d10 6454b14 780307f 588ce8d 7576d10 4809f98 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 |
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
from inference import classes
from inference import class_sizes_lower
from metrics import process_results_for_plot
from metrics import prediction_dashboard
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')
theme = gr.themes.Soft(
primary_hue="sky",
neutral_hue="slate",
)
def process_video(input_video, out_fps = 'auto', skip_frames = 7):
cap = cv2.VideoCapture(input_video)
output_path = "output.mp4"
if out_fps != 'auto' and type(out_fps) == int:
fps = int(out_fps)
else:
fps = int(cap.get(cv2.CAP_PROP_FPS))
if out_fps == 'auto':
fps = int(fps / skip_frames)
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()
cnt = 0
while iterating:
if (cnt % skip_frames) == 0:
# flip frame vertically
display_frame, result = inference_frame_serial(frame)
video.write(cv2.cvtColor(display_frame, cv2.COLOR_BGR2RGB))
#print(result)
top_pred = process_results_for_plot(predictions = result.numpy(),
classes = classes,
class_sizes = class_sizes_lower)
pred_dashbord = prediction_dashboard(top_pred = top_pred)
#print('sending frame')
print(cnt)
yield cv2.cvtColor(display_frame, cv2.COLOR_BGR2RGB), cv2.cvtColor(frame, cv2.COLOR_BGR2RGB), None, pred_dashbord
cnt += 1
iterating, frame = cap.read()
video.release()
yield None, None, output_path, None
with gr.Blocks(theme=theme) as demo:
with gr.Row():
input_video = gr.Video(label="Input")
output_video = gr.Video(label="Output Video")
with gr.Row():
original_frames = gr.Image(label="Original Frame")
dashboard = gr.Image(label="Dashboard")
processed_frames = gr.Image(label="Shark Engine")
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, original_frames, output_video, dashboard])
demo.queue()
if os.getenv('SYSTEM') == 'spaces':
demo.launch(width='40%',auth=(os.environ.get('SHARK_USERNAME'), os.environ.get('SHARK_PASSWORD')))
else:
demo.launch()
|