File size: 6,471 Bytes
9c98083 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 | """Video scene-cut detection, clip extraction, and subject-centered crop/resize.
Uses plain (non-relative) imports of sibling modules so this file works whether
invoked as ``python dataset_builder/build.py`` or ``python -m dataset_builder.build``
(build.py inserts its own directory onto sys.path before importing).
"""
import math
import statistics
import cv2
import imageio
import numpy as np
from PIL import Image
import crop_resize
from detect import detect_subject_bbox, subdivide_bbox
def _frame_hist(frame_bgr):
hsv = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2HSV)
hist = cv2.calcHist([hsv], [0, 1], None, [50, 60], [0, 180, 0, 256])
cv2.normalize(hist, hist)
return hist
def find_scene_cuts(video_path, diff_thresh=0.45, sample_stride=1):
"""Returns a sorted list of frame indices where a scene cut is detected,
via Bhattacharyya distance between consecutive HSV histograms."""
cap = cv2.VideoCapture(video_path)
cuts = []
prev_hist = None
idx = 0
while True:
ok, frame = cap.read()
if not ok:
break
if idx % sample_stride == 0:
hist = _frame_hist(frame)
if prev_hist is not None:
score = cv2.compareHist(prev_hist, hist, cv2.HISTCMP_BHATTACHARYYA)
if score > diff_thresh:
cuts.append(idx)
prev_hist = hist
idx += 1
cap.release()
return cuts
def build_clip_ranges(total_frames, fps, cuts, clip_min_sec=2.0, clip_max_sec=6.0):
"""Turns scene-cut frame indices into (start, end) frame ranges honoring
clip_min/max seconds: too-short segments are merged forward, segments
longer than clip_max are chopped into roughly-equal sub-segments."""
min_frames = max(1, int(clip_min_sec * fps))
max_frames = max(min_frames, int(clip_max_sec * fps))
boundaries = [0] + sorted(set(c for c in cuts if 0 < c < total_frames)) + [total_frames]
raw_ranges = []
start = boundaries[0]
for b in boundaries[1:]:
if b - start >= min_frames:
raw_ranges.append((start, b))
start = b
if start < total_frames:
if raw_ranges and (total_frames - start) < min_frames:
s, _ = raw_ranges[-1]
raw_ranges[-1] = (s, total_frames)
else:
raw_ranges.append((start, total_frames))
ranges = []
for s, e in raw_ranges:
length = e - s
if length <= max_frames:
ranges.append((s, e))
continue
n_chunks = math.ceil(length / max_frames)
chunk_len = length // n_chunks
for i in range(n_chunks):
cs = s + i * chunk_len
ce = e if i == n_chunks - 1 else cs + chunk_len
ranges.append((cs, ce))
return [(s, e) for s, e in ranges if e - s >= max(1, min_frames // 2)]
def _sample_indices(start, end, n=3):
if end - start <= n:
return list(range(start, end))
step = (end - start) / (n + 1)
return [int(start + step * (i + 1)) for i in range(n)]
def _median_bbox(bboxes):
return (
statistics.median(b[0] for b in bboxes),
statistics.median(b[1] for b in bboxes),
statistics.median(b[2] for b in bboxes),
statistics.median(b[3] for b in bboxes),
)
def _motion_tag(frames_gray):
if len(frames_gray) < 2:
return "static, minimal motion"
mags = []
for a, b in zip(frames_gray, frames_gray[1:]):
flow = cv2.calcOpticalFlowFarneback(a, b, None, 0.5, 3, 15, 3, 5, 1.2, 0)
mag, _ = cv2.cartToPolar(flow[..., 0], flow[..., 1])
mags.append(float(np.mean(mag)))
avg = sum(mags) / len(mags)
if avg < 0.5:
return "static, minimal motion"
if avg < 2.0:
return "subtle motion"
if avg < 5.0:
return "moderate motion"
return "fast, dynamic motion"
def extract_clip(video_path, start, end, target_fps, max_tier="1080p", pad_overrides=None,
body_region="auto", body_region_frac=0.45):
"""Reads frames [start, end) from video_path, computes one stable subject
crop for the whole clip (median bbox over sampled frames), resamples to
target_fps, trims to a 4n+1 frame count, and returns
(frames_bgr, (target_w, target_h), representative_pil_image, motion_tag),
or None if no frames could be read."""
cap = cv2.VideoCapture(video_path)
src_fps = cap.get(cv2.CAP_PROP_FPS) or target_fps
img_w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
img_h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
bboxes, kinds = [], []
for idx in _sample_indices(start, end, n=3):
cap.set(cv2.CAP_PROP_POS_FRAMES, idx)
ok, frame = cap.read()
if not ok:
continue
bbox, kind = detect_subject_bbox(frame)
bboxes.append(bbox)
kinds.append(kind)
if not bboxes:
cap.release()
return None
bbox = _median_bbox(bboxes)
kind = max(set(kinds), key=kinds.count)
if kind == "person" and body_region != "auto":
bbox = subdivide_bbox(bbox, body_region, body_region_frac)
kind = f"person_{body_region}"
crop_box, (tw, th), _bucket_name, _tier = crop_resize.compute_crop(
img_w, img_h, bbox, kind, pad_overrides, max_tier
)
step = src_fps / target_fps
raw_indices = []
f = float(start)
while f < end:
raw_indices.append(int(round(f)))
f += step
if len(raw_indices) >= 5:
n = ((len(raw_indices) - 1) // 4) * 4 + 1
raw_indices = raw_indices[:n]
frames_out, frames_gray = [], []
for idx in raw_indices:
cap.set(cv2.CAP_PROP_POS_FRAMES, idx)
ok, frame = cap.read()
if not ok:
continue
cropped = crop_resize.crop_resize_np(frame, crop_box, tw, th)
frames_out.append(cropped)
if len(frames_gray) < 6:
frames_gray.append(cv2.cvtColor(cropped, cv2.COLOR_BGR2GRAY))
cap.release()
if not frames_out:
return None
motion_tag = _motion_tag(frames_gray)
mid = frames_out[len(frames_out) // 2]
representative = Image.fromarray(cv2.cvtColor(mid, cv2.COLOR_BGR2RGB))
return frames_out, (tw, th), representative, motion_tag
def write_clip_mp4(frames_bgr, out_path, fps):
with imageio.get_writer(str(out_path), fps=fps, codec="libx264", quality=8) as writer:
for frame in frames_bgr:
writer.append_data(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
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