orena-seg / data_preprocessing /refine_blue_intervals.py
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Add blue-screen preprocessing scripts
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#!/usr/bin/env python3
"""Frame-level refinement of coarse blue-screen intervals."""
from __future__ import annotations
import argparse, json, time
from pathlib import Path
import cv2, numpy as np
def mask_for(h,w):
yy,xx=np.ogrid[:h,:w]
return (((xx-w/2)/(w*.47))**2+((yy-h/2)/(h*.47))**2<=1)
def metrics(frame, mask):
small=cv2.resize(frame,(160,160),interpolation=cv2.INTER_AREA)
m=cv2.resize(mask.astype(np.uint8),(160,160),interpolation=cv2.INTER_NEAREST).astype(bool)
hsv=cv2.cvtColor(small,cv2.COLOR_BGR2HSV); h,s,v=hsv[...,0],hsv[...,1],hsv[...,2]
valid=m&(v>18); br=float(((valid)&(h>=88)&(h<=140)&(s>=55)&(v>=35)).sum()/valid.sum()) if valid.any() else 0.0
gray=cv2.cvtColor(small,cv2.COLOR_BGR2GRAY)
texture=float(cv2.Laplacian(gray,cv2.CV_64F).var())
return br, texture, small[m].tobytes()
def main():
ap=argparse.ArgumentParser(); ap.add_argument('video',type=Path); ap.add_argument('--output',type=Path,required=True); ap.add_argument('--interval',action='append',nargs=2,type=float,metavar=('START','END'),required=True); ap.add_argument('--pad',type=float,default=5); ap.add_argument('--blue-ratio',type=float,default=.45); ap.add_argument('--confirm-frames',type=int,default=3)
a=ap.parse_args(); t0=time.perf_counter(); cap=cv2.VideoCapture(str(a.video));
if not cap.isOpened(): raise SystemExit('cannot open video')
fps=cap.get(cv2.CAP_PROP_FPS) or 25.; total=int(cap.get(cv2.CAP_PROP_FRAME_COUNT) or 0); results=[]
for coarse_start,coarse_end in a.interval:
start=max(0.,coarse_start-a.pad); end=coarse_end+a.pad
cap.set(cv2.CAP_PROP_POS_FRAMES,int(start*fps)); idx=int(start*fps); samples=[]; mask=None
while idx <= int(end*fps):
ok,fr=cap.read()
if not ok: break
if mask is None: mask=mask_for(fr.shape[0],fr.shape[1])
br,texture,pix=metrics(fr,mask)
samples.append({'frame':idx,'time_seconds':idx/fps,'blue_ratio':br,'texture':texture,'hash':hash(pix),'blue_like':br>=a.blue_ratio})
idx+=1
# Require confirmation frames on each side to suppress isolated noise.
flags=[x['blue_like'] for x in samples]; n=a.confirm_frames
starts=[]; ends=[]
for i in range(len(flags)-n+1):
if all(flags[i:i+n]): starts.append(i)
if i+n<=len(flags) and not any(flags[i:i+n]): ends.append(i)
if starts:
s=starts[0]; e=len(flags)-1
for i in range(s+n,len(flags)-n+1):
if not any(flags[i:i+n]): e=i-1; break
seg=samples[s:e+1]
results.append({'coarse_interval':[coarse_start,coarse_end],'start_frame':seg[0]['frame'],'end_frame':seg[-1]['frame'],'start_seconds':round(seg[0]['time_seconds'],3),'end_seconds':round((seg[-1]['frame']+1)/fps,3),'duration_seconds':round((seg[-1]['frame']-seg[0]['frame']+1)/fps,3),'mean_blue_ratio':round(float(np.mean([x['blue_ratio'] for x in seg])),4),'scan_start_seconds':start,'scan_end_seconds':end,'frames_scanned':len(samples)})
cap.release(); out={'video':str(a.video),'fps':fps,'frame_count':total,'method':'frame-level ROI blue-ratio refinement','elapsed_seconds':round(time.perf_counter()-t0,3),'intervals':results}
a.output.parent.mkdir(parents=True,exist_ok=True); a.output.write_text(json.dumps(out,ensure_ascii=False,indent=2),encoding='utf-8'); print(json.dumps(out,ensure_ascii=False,indent=2))
if __name__=='__main__': main()