SkalskiP commited on
Commit
b643479
β€’
1 Parent(s): 5b163f1

Add mask generation to video processing pipeline

Browse files
Files changed (1) hide show
  1. app.py +38 -10
app.py CHANGED
@@ -1,23 +1,39 @@
 
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  import time
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  import uuid
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  from typing import Tuple
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  import gradio as gr
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  import supervision as sv
 
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  from tqdm import tqdm
 
 
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  START_FRAME = 0
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  END_FRAME = 10
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  TOTAL = END_FRAME - START_FRAME
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- def process(
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- source_video: str,
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- prompt: str,
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- confidence: float,
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- progress=gr.Progress(track_tqdm=True)
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- ) -> Tuple[str, str]:
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- name = str(uuid.uuid4())
 
 
 
 
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  video_info = sv.VideoInfo.from_video_path(source_video)
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  frame_iterator = iter(sv.get_video_frames_generator(
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  source_path=source_video, start=START_FRAME, end=END_FRAME))
@@ -25,10 +41,22 @@ def process(
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  with sv.VideoSink(f"{name}.mp4", video_info=video_info) as sink:
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  for _ in tqdm(range(TOTAL), desc="Masking frames"):
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  frame = next(frame_iterator)
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- sink.write_frame(frame)
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- time.sleep(0.1)
 
 
 
 
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- return f"{name}.mp4", f"{name}.mp4"
 
 
 
 
 
 
 
 
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  with gr.Blocks() as demo:
 
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+ import torch
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  import time
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  import uuid
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  from typing import Tuple
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  import gradio as gr
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  import supervision as sv
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+ import numpy as np
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  from tqdm import tqdm
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+ from transformers import pipeline
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+ from PIL import Image
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  START_FRAME = 0
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  END_FRAME = 10
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  TOTAL = END_FRAME - START_FRAME
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+ DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
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+ SAM_GENERATOR = pipeline(
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+ task="mask-generation",
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+ model="facebook/sam-vit-base",
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+ device=DEVICE)
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+ MASK_ANNOTATOR = sv.MaskAnnotator(
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+ color=sv.Color.red(),
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+ color_lookup=sv.ColorLookup.INDEX)
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+
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+ def run_sam(frame: np.ndarray) -> sv.Detections:
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+ # convert from Numpy BGR to PIL RGB
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+ image = Image.fromarray(frame[:, :, ::-1])
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+
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+ outputs = SAM_GENERATOR(image)
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+ mask = np.array(outputs['masks'])
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+ return sv.Detections(xyxy=sv.mask_to_xyxy(masks=mask), mask=mask)
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+
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+
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+ def mask_video(source_video: str, prompt: str, confidence: float, name: str) -> str:
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  video_info = sv.VideoInfo.from_video_path(source_video)
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  frame_iterator = iter(sv.get_video_frames_generator(
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  source_path=source_video, start=START_FRAME, end=END_FRAME))
 
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  with sv.VideoSink(f"{name}.mp4", video_info=video_info) as sink:
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  for _ in tqdm(range(TOTAL), desc="Masking frames"):
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  frame = next(frame_iterator)
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+ detections = run_sam(frame)
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+ annotated_frame = MASK_ANNOTATOR.annotate(
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+ scene=frame.copy(), detections=detections)
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+ sink.write_frame(annotated_frame)
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+ return f"{name}.mp4"
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+
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+ def process(
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+ source_video: str,
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+ prompt: str,
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+ confidence: float,
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+ progress=gr.Progress(track_tqdm=True)
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+ ) -> Tuple[str, str]:
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+ name = str(uuid.uuid4())
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+ masked_video = mask_video(source_video, prompt, confidence, name)
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+ return masked_video, masked_video
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  with gr.Blocks() as demo: