n0p/mlx-video
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

format

Browse Source
This commit is contained in:
Prince Canuma
2026-03-18 17:40:05 +01:00
parent 78bcfba31b
commit 17397da70c
77 changed files with 4125 additions and 1655 deletions
Download Patch File Download Diff File Expand all files Collapse all files

78
scripts/video/video_quality.py
View File

@@ -158,10 +158,14 @@ def analyze_video(frames, chunk_size=None, compute_flow=False):
boundary_metrics = []
for b in boundaries:
if b < n and b > 0:
pre = metrics["frame_diff"][b - 1] if b > 1 else metrics["frame_diff"][1]
pre = (
metrics["frame_diff"][b - 1] if b > 1 else metrics["frame_diff"][1]
)
at = metrics["frame_diff"][b]
ratio = at / (pre + 1e-10)
brightness_jump = metrics["brightness"][b] - metrics["brightness"][b - 1]
brightness_jump = (
metrics["brightness"][b] - metrics["brightness"][b - 1]
)
contrast_jump = (
(metrics["contrast"][b] - metrics["contrast"][b - 1])
/ (metrics["contrast"][b - 1] + 1e-10)
@@ -198,7 +202,9 @@ def print_report(metrics, path, fps, total_frames, frames_analyzed):
print("VIDEO QUALITY REPORT")
print("=" * 72)
print(f" File: {path}")
print(f" Total frames: {total_frames} Analyzed: {frames_analyzed} FPS: {fps:.1f}")
print(
f" Total frames: {total_frames} Analyzed: {frames_analyzed} FPS: {fps:.1f}"
)
duration = total_frames / fps if fps > 0 else 0
print(f" Duration: {duration:.1f}s")
print()
@@ -211,52 +217,76 @@ def print_report(metrics, path, fps, total_frames, frames_analyzed):
print("-" * 40)
if n_uniform:
frames_list = np.where(metrics["is_uniform"])[0][:10]
print(f" Uniform/blank frames: {n_uniform} — frames {list(frames_list)}{'...' if n_uniform > 10 else ''}")
print(
f" Uniform/blank frames: {n_uniform} — frames {list(frames_list)}{'...' if n_uniform > 10 else ''}"
)
if n_noisy:
frames_list = np.where(metrics["is_noisy"])[0][:10]
print(f" Noisy frames: {n_noisy} — frames {list(frames_list)}{'...' if n_noisy > 10 else ''}")
print(
f" Noisy frames: {n_noisy} — frames {list(frames_list)}{'...' if n_noisy > 10 else ''}"
)
print()
print("SHARPNESS")
print("-" * 40)
print(f" Laplacian var: mean={np.mean(sl):8.1f} min={np.min(sl):8.1f} max={np.max(sl):8.1f} std={np.std(sl):.1f}")
print(f" Gradient mag: mean={np.mean(sg):8.2f} min={np.min(sg):8.2f} max={np.max(sg):8.2f} std={np.std(sg):.2f}")
print(
f" Laplacian var: mean={np.mean(sl):8.1f} min={np.min(sl):8.1f} max={np.max(sl):8.1f} std={np.std(sl):.1f}"
)
print(
f" Gradient mag: mean={np.mean(sg):8.2f} min={np.min(sg):8.2f} max={np.max(sg):8.2f} std={np.std(sg):.2f}"
)
if np.std(sl) / (np.mean(sl) + 1e-10) > 0.3:
print(" ⚠ High sharpness variation — possible blur artifacts")
print()
print("BRIGHTNESS & CONTRAST")
print("-" * 40)
print(f" Brightness: mean={np.mean(br):6.1f} min={np.min(br):6.1f} max={np.max(br):6.1f} std={np.std(br):.2f}")
print(f" Contrast (std): mean={np.mean(ct):6.1f} min={np.min(ct):6.1f} max={np.max(ct):6.1f} std={np.std(ct):.2f}")
print(
f" Brightness: mean={np.mean(br):6.1f} min={np.min(br):6.1f} max={np.max(br):6.1f} std={np.std(br):.2f}"
)
print(
f" Contrast (std): mean={np.mean(ct):6.1f} min={np.min(ct):6.1f} max={np.max(ct):6.1f} std={np.std(ct):.2f}"
)
if np.std(br) > 3.0:
print(" ⚠ Brightness instability — may indicate chunk boundary artifacts")
print()
print("COLOR DISTRIBUTION (BGR)")
print("-" * 40)
print(f" Blue: mean={np.mean(metrics['color_mean_b']):6.1f} std={np.std(metrics['color_mean_b']):.2f}")
print(f" Green: mean={np.mean(metrics['color_mean_g']):6.1f} std={np.std(metrics['color_mean_g']):.2f}")
print(f" Red: mean={np.mean(metrics['color_mean_r']):6.1f} std={np.std(metrics['color_mean_r']):.2f}")
print(
f" Blue: mean={np.mean(metrics['color_mean_b']):6.1f} std={np.std(metrics['color_mean_b']):.2f}"
)
print(
f" Green: mean={np.mean(metrics['color_mean_g']):6.1f} std={np.std(metrics['color_mean_g']):.2f}"
)
print(
f" Red: mean={np.mean(metrics['color_mean_r']):6.1f} std={np.std(metrics['color_mean_r']):.2f}"
)
print()
print("TEMPORAL STABILITY")
print("-" * 40)
fd_nz = fd[1:] # skip first frame (always 0)
if len(fd_nz) > 0:
print(f" Frame diff: mean={np.mean(fd_nz):6.2f} min={np.min(fd_nz):6.2f} max={np.max(fd_nz):6.2f} std={np.std(fd_nz):.2f}")
print(
f" Frame diff: mean={np.mean(fd_nz):6.2f} min={np.min(fd_nz):6.2f} max={np.max(fd_nz):6.2f} std={np.std(fd_nz):.2f}"
)
if np.std(fd_nz) / (np.mean(fd_nz) + 1e-10) > 0.5:
print(" ⚠ High diff variance — jitter or discontinuities")
if "flow_mean" in metrics:
fm = metrics["flow_mean"][1:]
print(f" Optical flow: mean={np.mean(fm):6.2f} max_frame={np.max(metrics['flow_max'][1:]):.1f}")
print(
f" Optical flow: mean={np.mean(fm):6.2f} max_frame={np.max(metrics['flow_max'][1:]):.1f}"
)
print()
# Chunk boundaries
if "boundaries" in metrics and metrics["boundaries"]:
print("CHUNK BOUNDARIES")
print("-" * 40)
print(f" {'Frame':>6} {'Diff ratio':>10} {'Brightness':>10} {'Contrast %':>10} {'Sharpness %':>11}")
print(
f" {'Frame':>6} {'Diff ratio':>10} {'Brightness':>10} {'Contrast %':>10} {'Sharpness %':>11}"
)
for bm in metrics["boundaries"]:
print(
f" {bm['frame']:6d}"
@@ -267,7 +297,9 @@ def print_report(metrics, path, fps, total_frames, frames_analyzed):
)
avg_ratio = np.mean([b["diff_ratio"] for b in metrics["boundaries"]])
if avg_ratio > 2.0:
print(f" ⚠ Boundary diff ratio {avg_ratio:.1f}x — visible chunk transitions")
print(
f" ⚠ Boundary diff ratio {avg_ratio:.1f}x — visible chunk transitions"
)
print()
# Overall grade
@@ -303,9 +335,7 @@ def main():
type=int,
help="Frames per chunk for boundary analysis (e.g., 32)",
)
parser.add_argument(
"--start", type=int, default=0, help="Start frame (default: 0)"
)
parser.add_argument("--start", type=int, default=0, help="Start frame (default: 0)")
parser.add_argument("--end", type=int, help="End frame (default: all)")
parser.add_argument(
"--flow",
@@ -329,8 +359,14 @@ def main():
import csv
keys = [
"sharpness_lap", "sharpness_grad", "brightness", "contrast",
"color_mean_b", "color_mean_g", "color_mean_r", "frame_diff",
"sharpness_lap",
"sharpness_grad",
"brightness",
"contrast",
"color_mean_b",
"color_mean_g",
"color_mean_r",
"frame_diff",
]
if args.flow:
keys += ["flow_mean", "flow_max"]