#!/usr/bin/env python3
# MOG2 background subtraction motion detector
# Reads video, outputs motion segment timestamps (start end) one per line
# Usage: ipcam-mog2.py input.mkv [min_area_pct] [gap_sec] [history] [var_threshold]
import sys
import cv2
import numpy as np

if len(sys.argv) < 2:
    print("usage: ipcam-mog2.py input.mkv [min_area%] [gap_sec] [history] [var_thresh]", file=sys.stderr)
    sys.exit(1)

path = sys.argv[1]
min_area_pct = float(sys.argv[2]) if len(sys.argv) > 2 else 0.5
gap_sec = float(sys.argv[3]) if len(sys.argv) > 3 else 1.0
history = int(sys.argv[4]) if len(sys.argv) > 4 else 500
var_thresh = float(sys.argv[5]) if len(sys.argv) > 5 else 16.0

cap = cv2.VideoCapture(path)
if not cap.isOpened():
    print(f"cannot open {path}", file=sys.stderr)
    sys.exit(1)

fps = cap.get(cv2.CAP_PROP_FPS)
w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
total = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))

# downscale for speed - process at 320px wide
scale = 320.0 / w if w > 320 else 1.0
sw, sh = int(w * scale), int(h * scale)
total_pixels = sw * sh
min_pixels = total_pixels * (min_area_pct / 100.0)

mog = cv2.createBackgroundSubtractorMOG2(
    history=history,
    varThreshold=var_thresh,
    detectShadows=True,
)
# shadow detection marks shadow pixels as 127 vs foreground 255
# learning rate: -1 = automatic, or set explicitly (0.001-0.01 typical)
learn_rate = -1

kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))

segments = []
in_motion = False
seg_start = 0.0
seg_end = 0.0
frame_idx = 0
motion_frames = 0
still_frames = 0

while True:
    ret, frame = cap.read()
    if not ret:
        break
    t = frame_idx / fps

    small = cv2.resize(frame, (sw, sh)) if scale < 1.0 else frame
    mask = mog.apply(small, learningRate=learn_rate)

    # threshold: only foreground (255), ignore shadows (127)
    _, mask = cv2.threshold(mask, 200, 255, cv2.THRESH_BINARY)

    # morphological open to kill noise, close to fill gaps
    mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
    mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)

    fg_pixels = cv2.countNonZero(mask)
    motion = fg_pixels > min_pixels

    if motion:
        still_frames = 0
        motion_frames += 1
        if not in_motion:
            in_motion = True
            seg_start = t
        seg_end = t
    else:
        still_frames += 1
        motion_frames = 0
        if in_motion and (t - seg_end) > gap_sec:
            segments.append((seg_start, seg_end))
            in_motion = False

    frame_idx += 1

cap.release()

if in_motion:
    segments.append((seg_start, seg_end))

# merge segments that are closer than gap_sec
merged = []
for s, e in segments:
    if merged and s - merged[-1][1] <= gap_sec:
        merged[-1] = (merged[-1][0], e)
    else:
        merged.append((s, e))

if not merged:
    print("no motion detected", file=sys.stderr)
    sys.exit(1)

print(f"frames={frame_idx} fps={fps:.1f} area_thresh={min_area_pct}% "
      f"({int(min_pixels)}px) gap={gap_sec}s history={history} "
      f"var_thresh={var_thresh}", file=sys.stderr)
print(f"{len(merged)} segments", file=sys.stderr)

for s, e in merged:
    sh_, sm, ss = int(s // 3600), int((s % 3600) // 60), s % 60
    eh, em, es = int(e // 3600), int((e % 3600) // 60), e % 60
    print(f"{sh_:02d}:{sm:02d}:{ss:06.3f} {eh:02d}:{em:02d}:{es:06.3f}")