| """Per-frame structural agreement between the graybox and each styled look of paired matrix clips. |
| |
| usage: matrix_qa.py DATA_DIR [DATA_DIR ...] [--clips 40] |
| |
| For the same clips in every DATA_DIR (the first --clips per map, sorted), every |
| frame of every look is reduced to a blurred 160x90 edge-strength map (Sobel on |
| grey); the Pearson correlation between the graybox map and each styled map |
| measures whether the same geometry is on screen. A frame whose geometry was |
| dropped in one look scores far lower than its neighbours. Also reports the |
| share of the graybox's bottom third that is sky colour. |
| """ |
| import argparse |
| import json |
| from pathlib import Path |
|
|
| import cv2 |
| import numpy as np |
|
|
| LOOKS = ('cartoon', 'war', 'punk') |
| SKY = {'paris': np.array([192, 168, 121]), 'tsinghua': np.array([196, 171, 124])} |
|
|
|
|
| def edges(frame): |
| g = cv2.cvtColor(cv2.resize(frame, (160, 90), interpolation=cv2.INTER_AREA), cv2.COLOR_BGR2GRAY).astype(np.float32) |
| m = cv2.magnitude(cv2.Sobel(g, cv2.CV_32F, 1, 0), cv2.Sobel(g, cv2.CV_32F, 0, 1)) |
| m = cv2.GaussianBlur(m, (0, 0), 2) |
| return (m - m.mean()) / (m.std() + 1e-6) |
|
|
|
|
| def frames(path): |
| cap = cv2.VideoCapture(str(path)) |
| out = [] |
| while True: |
| ok, f = cap.read() |
| if not ok: |
| break |
| out.append(f) |
| return out |
|
|
|
|
| def main(): |
| parser = argparse.ArgumentParser() |
| parser.add_argument('data', type=Path, nargs='+') |
| parser.add_argument('--clips', type=int, default=40) |
| args = parser.parse_args() |
| report = {} |
| for data in args.data: |
| corr = {look: [] for look in LOOKS} |
| sky = [] |
| for map_id in ('paris', 'tsinghua'): |
| for clip in sorted((data / map_id).glob('clip_*'))[:args.clips]: |
| gray = frames(clip / 'original.mp4') |
| g_edges = [edges(f) for f in gray] |
| for f in gray[::24]: |
| b = f[2 * f.shape[0] // 3:].astype(int) |
| sky.append(float((np.abs(b - SKY[map_id]).sum(-1) < 12).mean())) |
| for look in LOOKS: |
| for ge, f in zip(g_edges, frames(clip / f'{look}.mp4')): |
| corr[look].append(float((ge * edges(f)).mean())) |
| report[str(data)] = dict( |
| frames=len(corr['war']), graybox_sky_bottom_third=round(float(np.mean(sky)), 3), |
| **{look: dict(mean=round(float(np.mean(v)), 3), p05=round(float(np.percentile(v, 5)), 3), |
| below_0_3=round(float(np.mean(np.array(v) < .3)), 4)) for look, v in corr.items()}) |
| print(json.dumps(report, indent=1)) |
|
|
|
|
| if __name__ == '__main__': |
| main() |
|
|
