#!/usr/bin/python
#-*- coding: utf-8 -*-

# Full SyncNet Pipeline: Face Detection -> Tracking -> Cropping -> SyncNet Evaluation
#
# Example usage:
#   python batch_syncnet_full.py --video_dir /path/to/videos --output_file results.json
#   python batch_syncnet_full.py --video_dir /share/zhaohu_workspace/benchmarks/outputs_benchmark_4/latent_sync --output_file results_latentsync.json
"""
python batch_syncnet_full.py --video_dir /share/zhaohu_workspace/benchmarks/outputs_benchmark_4/latent_sync  --output_file results_latentsync.json 2>&1

python batch_syncnet_full.py \
    --video_dir /share/zhaohu_workspace/video_gen-hunyuanvideo1.5_tai2v_training/outputs/test_results_step_010800 \
    --output_file results_10800.json 2>&1


"""
#
# Prerequisites:
#   sh download_model.sh  # Download required models first

import sys
import time
import os
import argparse
import pickle
import subprocess
import glob
import cv2
import json
import numpy as np
from shutil import rmtree

import scenedetect
from scenedetect.video_manager import VideoManager
from scenedetect.scene_manager import SceneManager
from scenedetect.stats_manager import StatsManager
from scenedetect.detectors import ContentDetector

from scipy.interpolate import interp1d
from scipy.io import wavfile
from scipy import signal

from detectors import S3FD
from SyncNetInstance import SyncNetInstance

# ==================== PARSE ARGS ====================

parser = argparse.ArgumentParser(description="Full SyncNet Pipeline - Batch Processing")

# Input/Output
parser.add_argument('--video_dir', type=str, required=True, help='Directory containing video files')
parser.add_argument('--output_file', type=str, default='syncnet_results.json', help='Output JSON file')
parser.add_argument('--data_dir', type=str, default='data/work', help='Working directory for intermediate files')
parser.add_argument('--video_ext', type=str, default='mp4,avi,mov,mkv', help='Video extensions (comma-separated)')

# Model paths
parser.add_argument('--syncnet_model', type=str, default='data/syncnet_v2.model', help='SyncNet model path')

# Pipeline parameters
parser.add_argument('--facedet_scale', type=float, default=0.25, help='Scale factor for face detection')
parser.add_argument('--crop_scale', type=float, default=0.40, help='Scale bounding box')
parser.add_argument('--min_track', type=int, default=50, help='Minimum facetrack duration (frames)')
parser.add_argument('--frame_rate', type=int, default=25, help='Frame rate')
parser.add_argument('--num_failed_det', type=int, default=25, help='Missed detections allowed before stopping track')
parser.add_argument('--min_face_size', type=int, default=100, help='Minimum face size in pixels')

# SyncNet parameters
parser.add_argument('--batch_size', type=int, default=20, help='Batch size for SyncNet')
parser.add_argument('--vshift', type=int, default=15, help='Video shift for sync evaluation')

# Cleanup
parser.add_argument('--keep_intermediate', action='store_true', help='Keep intermediate files')

opt = parser.parse_args()

# Setup directories
opt.avi_dir = os.path.join(opt.data_dir, 'pyavi')
opt.tmp_dir = os.path.join(opt.data_dir, 'pytmp')
opt.work_dir = os.path.join(opt.data_dir, 'pywork')
opt.crop_dir = os.path.join(opt.data_dir, 'pycrop')
opt.frames_dir = os.path.join(opt.data_dir, 'pyframes')


# ==================== UTILITY FUNCTIONS ====================

def bb_intersection_over_union(boxA, boxB):
    xA = max(boxA[0], boxB[0])
    yA = max(boxA[1], boxB[1])
    xB = min(boxA[2], boxB[2])
    yB = min(boxA[3], boxB[3])

    interArea = max(0, xB - xA) * max(0, yB - yA)
    boxAArea = (boxA[2] - boxA[0]) * (boxA[3] - boxA[1])
    boxBArea = (boxB[2] - boxB[0]) * (boxB[3] - boxB[1])

    iou = interArea / float(boxAArea + boxBArea - interArea)
    return iou


def track_shot(opt, scenefaces):
    iouThres = 0.5
    tracks = []

    while True:
        track = []
        for framefaces in scenefaces:
            for face in framefaces:
                if track == []:
                    track.append(face)
                    framefaces.remove(face)
                elif face['frame'] - track[-1]['frame'] <= opt.num_failed_det:
                    iou = bb_intersection_over_union(face['bbox'], track[-1]['bbox'])
                    if iou > iouThres:
                        track.append(face)
                        framefaces.remove(face)
                        continue
                else:
                    break

        if track == []:
            break
        elif len(track) > opt.min_track:
            framenum = np.array([f['frame'] for f in track])
            bboxes = np.array([np.array(f['bbox']) for f in track])

            frame_i = np.arange(framenum[0], framenum[-1] + 1)

            bboxes_i = []
            for ij in range(0, 4):
                interpfn = interp1d(framenum, bboxes[:, ij])
                bboxes_i.append(interpfn(frame_i))
            bboxes_i = np.stack(bboxes_i, axis=1)

            if max(np.mean(bboxes_i[:, 2] - bboxes_i[:, 0]), np.mean(bboxes_i[:, 3] - bboxes_i[:, 1])) > opt.min_face_size:
                tracks.append({'frame': frame_i, 'bbox': bboxes_i})

    return tracks


def crop_video(opt, track, cropfile):
    flist = glob.glob(os.path.join(opt.frames_dir, opt.reference, '*.jpg'))
    flist.sort()

    fourcc = cv2.VideoWriter_fourcc(*'XVID')
    vOut = cv2.VideoWriter(cropfile + 't.avi', fourcc, opt.frame_rate, (224, 224))

    dets = {'x': [], 'y': [], 's': []}

    for det in track['bbox']:
        dets['s'].append(max((det[3] - det[1]), (det[2] - det[0])) / 2)
        dets['y'].append((det[1] + det[3]) / 2)
        dets['x'].append((det[0] + det[2]) / 2)

    # Smooth detections
    dets['s'] = signal.medfilt(dets['s'], kernel_size=13)
    dets['x'] = signal.medfilt(dets['x'], kernel_size=13)
    dets['y'] = signal.medfilt(dets['y'], kernel_size=13)

    for fidx, frame in enumerate(track['frame']):
        cs = opt.crop_scale
        bs = dets['s'][fidx]
        bsi = int(bs * (1 + 2 * cs))

        image = cv2.imread(flist[frame])
        frame_padded = np.pad(image, ((bsi, bsi), (bsi, bsi), (0, 0)), 'constant', constant_values=(110, 110))

        my = dets['y'][fidx] + bsi
        mx = dets['x'][fidx] + bsi

        face = frame_padded[int(my - bs):int(my + bs * (1 + 2 * cs)), int(mx - bs * (1 + cs)):int(mx + bs * (1 + cs))]

        vOut.write(cv2.resize(face, (224, 224)))

    audiotmp = os.path.join(opt.tmp_dir, opt.reference, 'audio.wav')
    audiostart = (track['frame'][0]) / opt.frame_rate
    audioend = (track['frame'][-1] + 1) / opt.frame_rate

    vOut.release()

    # Crop audio
    command = "ffmpeg -y -i %s -ss %.3f -to %.3f %s" % (
        os.path.join(opt.avi_dir, opt.reference, 'audio.wav'), audiostart, audioend, audiotmp)
    subprocess.call(command, shell=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)

    # Combine audio and video
    command = "ffmpeg -y -i %st.avi -i %s -c:v copy -c:a copy %s.avi" % (cropfile, audiotmp, cropfile)
    subprocess.call(command, shell=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)

    os.remove(cropfile + 't.avi')

    return {'track': track, 'proc_track': dets}


def inference_video(opt, DET):
    flist = glob.glob(os.path.join(opt.frames_dir, opt.reference, '*.jpg'))
    flist.sort()

    dets = []

    for fidx, fname in enumerate(flist):
        image = cv2.imread(fname)
        image_np = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        bboxes = DET.detect_faces(image_np, conf_th=0.9, scales=[opt.facedet_scale])

        dets.append([])
        for bbox in bboxes:
            dets[-1].append({'frame': fidx, 'bbox': (bbox[:-1]).tolist(), 'conf': bbox[-1]})

    return dets


def scene_detect(opt):
    video_path = os.path.join(opt.avi_dir, opt.reference, 'video.avi')
    video_manager = VideoManager([video_path])
    stats_manager = StatsManager()
    scene_manager = SceneManager(stats_manager)
    scene_manager.add_detector(ContentDetector())
    base_timecode = video_manager.get_base_timecode()

    video_manager.set_downscale_factor()
    video_manager.start()
    scene_manager.detect_scenes(frame_source=video_manager)

    scene_list = scene_manager.get_scene_list(base_timecode)

    if scene_list == []:
        scene_list = [(video_manager.get_base_timecode(), video_manager.get_current_timecode())]

    return scene_list


def process_single_video(opt, videofile, DET, syncnet):
    """Process a single video through the full pipeline."""

    video_name = os.path.basename(videofile)
    opt.reference = os.path.splitext(video_name)[0]

    # Clean up previous runs
    for d in [opt.work_dir, opt.crop_dir, opt.avi_dir, opt.frames_dir, opt.tmp_dir]:
        path = os.path.join(d, opt.reference)
        if os.path.exists(path):
            rmtree(path)
        os.makedirs(path, exist_ok=True)

    # ========== STEP 1: Convert video and extract frames ==========

    # Convert to standard format
    command = "ffmpeg -y -i %s -qscale:v 2 -async 1 -r %d %s" % (
        videofile, opt.frame_rate, os.path.join(opt.avi_dir, opt.reference, 'video.avi'))
    subprocess.call(command, shell=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)

    # Extract frames
    command = "ffmpeg -y -i %s -qscale:v 2 -threads 1 -f image2 %s" % (
        os.path.join(opt.avi_dir, opt.reference, 'video.avi'),
        os.path.join(opt.frames_dir, opt.reference, '%06d.jpg'))
    subprocess.call(command, shell=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)

    # Extract audio
    command = "ffmpeg -y -i %s -ac 1 -vn -acodec pcm_s16le -ar 16000 %s" % (
        os.path.join(opt.avi_dir, opt.reference, 'video.avi'),
        os.path.join(opt.avi_dir, opt.reference, 'audio.wav'))
    subprocess.call(command, shell=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)

    # Check if frames were extracted
    flist = glob.glob(os.path.join(opt.frames_dir, opt.reference, '*.jpg'))
    if len(flist) == 0:
        return {'status': 'error', 'error_message': 'No frames extracted from video'}

    # ========== STEP 2: Face detection ==========

    faces = inference_video(opt, DET)

    # ========== STEP 3: Scene detection ==========

    scene = scene_detect(opt)

    # ========== STEP 4: Face tracking ==========

    alltracks = []
    for shot in scene:
        if shot[1].frame_num - shot[0].frame_num >= opt.min_track:
            alltracks.extend(track_shot(opt, faces[shot[0].frame_num:shot[1].frame_num]))

    if len(alltracks) == 0:
        return {'status': 'error', 'error_message': 'No face tracks found'}

    # ========== STEP 5: Crop face tracks ==========

    vidtracks = []
    for ii, track in enumerate(alltracks):
        cropfile = os.path.join(opt.crop_dir, opt.reference, '%05d' % ii)
        vidtracks.append(crop_video(opt, track, cropfile))

    # ========== STEP 6: Run SyncNet on cropped faces ==========

    flist = glob.glob(os.path.join(opt.crop_dir, opt.reference, '0*.avi'))
    flist.sort()

    if len(flist) == 0:
        return {'status': 'error', 'error_message': 'No cropped face videos created'}

    all_offsets = []
    all_confs = []
    all_min_dists = []

    for fname in flist:
        try:
            offset, conf, dist = syncnet.evaluate(opt, videofile=fname)
            all_offsets.append(int(offset))
            all_confs.append(float(conf))
            all_min_dists.append(float(dist.min()) if dist is not None else None)
        except Exception as e:
            print(f"    Warning: Failed to evaluate track {fname}: {e}")

    if len(all_confs) == 0:
        return {'status': 'error', 'error_message': 'SyncNet evaluation failed for all tracks'}

    # Aggregate results (use the track with highest confidence)
    best_idx = np.argmax(all_confs)

    return {
        'status': 'success',
        'num_tracks': len(alltracks),
        'offset': all_offsets[best_idx],
        'confidence': all_confs[best_idx],
        'min_dist': all_min_dists[best_idx],
        'all_offsets': all_offsets,
        'all_confidences': all_confs,
        'avg_confidence': float(np.mean(all_confs))
    }


def cleanup_video(opt):
    """Clean up intermediate files for a video."""
    for d in [opt.work_dir, opt.crop_dir, opt.avi_dir, opt.frames_dir, opt.tmp_dir]:
        path = os.path.join(d, opt.reference)
        if os.path.exists(path):
            rmtree(path)


# ==================== MAIN ====================

def main():
    print("=" * 60)
    print("Full SyncNet Pipeline - Batch Processing")
    print("=" * 60)

    # Find video files
    video_extensions = opt.video_ext.split(',')
    video_files = []
    for ext in video_extensions:
        video_files.extend(glob.glob(os.path.join(opt.video_dir, f'*.{ext.strip()}')))
        video_files.extend(glob.glob(os.path.join(opt.video_dir, f'*.{ext.strip().upper()}')))

    video_files = sorted(list(set(video_files)))
    total = len(video_files)

    print(f"Found {total} video files to process.")

    if total == 0:
        print("No video files found. Exiting.")
        return

    # Create working directories
    for d in [opt.avi_dir, opt.tmp_dir, opt.work_dir, opt.crop_dir, opt.frames_dir]:
        os.makedirs(d, exist_ok=True)

    # Load models
    print("\nLoading face detector (S3FD)...")
    DET = S3FD(device='cuda')

    print("Loading SyncNet model...")
    syncnet = SyncNetInstance()
    syncnet.loadParameters(opt.syncnet_model)
    print(f"Model {opt.syncnet_model} loaded.\n")

    # Process videos
    results = {}

    for idx, videofile in enumerate(video_files):
        video_name = os.path.basename(videofile)
        print(f"[{idx + 1}/{total}] Processing: {video_name}")

        try:
            result = process_single_video(opt, videofile, DET, syncnet)
            results[video_name] = result

            if result['status'] == 'success':
                print(f"    -> Tracks: {result['num_tracks']}, Offset: {result['offset']}, "
                      f"Confidence: {result['confidence']:.3f}, Avg Conf: {result['avg_confidence']:.3f}")
            else:
                print(f"    -> Error: {result['error_message']}")

        except Exception as e:
            results[video_name] = {'status': 'error', 'error_message': str(e)}
            print(f"    -> Error: {str(e)}")

        # Cleanup intermediate files
        if not opt.keep_intermediate:
            cleanup_video(opt)

    # Summary statistics
    successful = sum(1 for r in results.values() if r['status'] == 'success')

    summary = {
        'total_videos': total,
        'successful': successful,
        'failed': total - successful,
    }

    if successful > 0:
        confs = [r['confidence'] for r in results.values() if r['status'] == 'success']
        offsets = [r['offset'] for r in results.values() if r['status'] == 'success']
        min_dists = [r['min_dist'] for r in results.values() if r['status'] == 'success' and r['min_dist'] is not None]

        summary['avg_confidence'] = float(np.mean(confs))
        summary['min_confidence'] = float(np.min(confs))
        summary['max_confidence'] = float(np.max(confs))
        summary['std_confidence'] = float(np.std(confs))
        summary['avg_offset'] = float(np.mean(offsets))
        summary['avg_min_dist'] = float(np.mean(min_dists)) if min_dists else None

    # Save results with summary
    output = {
        'summary': summary,
        'videos': results
    }

    with open(opt.output_file, 'w') as f:
        json.dump(output, f, indent=2)

    print("\n" + "=" * 60)
    print(f"Results saved to {opt.output_file}")
    print("=" * 60)

    # Print summary
    print(f"\n[SUMMARY]")
    print(f"Processed {successful}/{total} videos successfully.")

    if successful > 0:
        print(f"\nConfidence scores:")
        print(f"  Average: {summary['avg_confidence']:.3f}")
        print(f"  Std:     {summary['std_confidence']:.3f}")
        print(f"  Min:     {summary['min_confidence']:.3f}")
        print(f"  Max:     {summary['max_confidence']:.3f}")
        print(f"\nAverage offset: {summary['avg_offset']:.2f}")
        if summary['avg_min_dist'] is not None:
            print(f"Average min_dist: {summary['avg_min_dist']:.3f}")


if __name__ == '__main__':
    main()