Integration of cropdetec-logic into anime_audio_encoder and tv_audio_encoder. Rewrite of readmes

README.md

@@ -4,9 +4,9 @@ This is a collection of Python scripts for various video and audio processing ta
 
 ## Scripts
 
--   **[anime_audio_encoder.py](anime_audio_encoder.py)**: A script tailored for encoding anime. It handles Variable Frame Rate (VFR) sources and uses `av1an` for AV1 encoding. For more details, see the [Anime Audio Encoder README](README_Anime%20Audio%20Encoder.md).
+-   **[anime_audio_encoder.py](anime_audio_encoder.py)**: A script tailored for encoding anime. It handles Variable Frame Rate (VFR) sources and uses `av1an` for AV1 encoding. Now supports `--autocrop` to automatically crop black bars using cropdetect logic, applied to the UTVideo intermediate file. For more details, see the [Anime Audio Encoder README](README_Anime%20Audio%20Encoder.md).
 
--   **[tv_audio_encoder.py](tv_audio_encoder.py)**: A script designed for encoding TV show episodes. It uses `alabamaEncoder` for the video encoding process. For more details, see the [TV Audio Encoder README](README_TV%20Audio%20Encoder.md).
+-   **[tv_audio_encoder.py](tv_audio_encoder.py)**: A script designed for encoding TV show episodes. It uses `alabamaEncoder` for the video encoding process. Now supports `--autocrop` to automatically crop black bars using cropdetect logic, applied to the UTVideo intermediate file. For more details, see the [TV Audio Encoder README](README_TV%20Audio%20Encoder.md).
 
 -   **[MkvOpusEnc.py](MkvOpusEnc.py)**: A cross-platform script for batch-processing audio tracks in MKV files to the Opus format. For more details, see the [MkvOpusEnc README](README_MkvOpusEnc.md).
 

README_Anime Audio Encoder.md

@@ -43,6 +43,15 @@ The following command-line tools must be installed and available in your system'
     ./anime_audio_encoder.py --no-downmix
     ```
 
+*   `--autocrop`: Automatically detect and crop black bars from video using cropdetect. The crop is applied only to the UTVideo intermediate file, ensuring no image data is lost even with variable crops.
+    ```bash
+    ./anime_audio_encoder.py --autocrop
+    ```
+    You can combine with `--no-downmix`:
+    ```bash
+    ./anime_audio_encoder.py --autocrop --no-downmix
+    ```
+
 ## Output
 
 *   Processed files are moved to the `completed/` directory.

README_TV Audio Encoder.md

@@ -46,6 +46,20 @@ The following command-line tools must be installed and available in your system'
     ./tv_audio_encoder.py --no-downmix
     ```
 
+*   `--autocrop`: Automatically detect and crop black bars from video using cropdetect. The crop is applied only to the UTVideo intermediate file, ensuring no image data is lost even with variable crops.
+
+    Example:
+
+    ```bash
+    ./tv_audio_encoder.py --autocrop
+    ```
+
+    You can combine with `--no-downmix`:
+
+    ```bash
+    ./tv_audio_encoder.py --autocrop --no-downmix
+    ```
+
 ## Output
 
 *   Processed files are moved to the `completed/` directory.
@@ -55,4 +69,5 @@ The following command-line tools must be installed and available in your system'
 ## Notes
 
 *   This script is intended for use on **Linux** only.
-*   The entire process, especially the AV1 encoding, can be very time-consuming and CPU
+*   The entire process, especially the AV1 encoding, can be very time-consuming and CPU-intensive. Be prepared for long processing times, especially on large files or less powerful machines.
+*   Consider testing with a single file first to fine-tune your desired settings before batch processing a large library of videos.

README_cropdetect.md

@@ -1,32 +1,30 @@
 # Advanced Crop Detection Script
 
-This Python script provides a robust and intelligent way to detect the correct crop values for video files. It goes far beyond a simple `ffmpeg-cropdetect` wrapper by using parallel processing and a series of smart heuristics to provide accurate and reliable recommendations, even for complex videos with mixed aspect ratios.
+This Python script (`cropdetect.py`) provides robust, parallelized, and intelligent crop detection for video files. It is much more than a simple wrapper for `ffmpeg-cropdetect`—it uses parallel processing, aspect ratio heuristics, luma verification, and bounding box logic to recommend safe crop values, even for complex videos with mixed aspect ratios.
 
 ## Key Features
 
-- **Parallel Processing**: Analyzes video segments in parallel to significantly speed up the detection process on multi-core systems.
+- **Parallel Processing:** Analyzes video segments in parallel for speed and reliability.
-- **Smart Aspect Ratio Snapping**: Automatically "snaps" detected crop values to known cinematic standards (e.g., 1.85:1, 2.39:1, 16:9, 4:3), correcting for minor detection errors.
+- **Aspect Ratio Snapping:** Automatically snaps detected crops to known cinematic standards (16:9, 2.39:1, 1.85:1, 4:3, IMAX, etc.), correcting minor detection errors.
-- **Mixed Aspect Ratio Detection**: Intelligently identifies videos that switch aspect ratios (e.g., IMAX scenes in a widescreen movie) and warns the user against applying a single, destructive crop.
+- **Mixed Aspect Ratio Handling:** Detects and safely handles videos with changing aspect ratios (e.g., IMAX scenes), recommending a bounding box crop that never cuts into image data.
-- **Credits & Logo Filtering**: Automatically detects and ignores crop values that only appear in the first or last 5% of the video, preventing opening logos or closing credits from influencing the result.
+- **Luma Verification:** Discards unreliable crop detections from very dark scenes using a second analysis pass.
-- **Luma Verification**: Performs a second analysis pass on frames with unidentified aspect ratios. If a frame is too dark, the detection is discarded as unreliable, preventing false positives from dark scenes.
+- **Credits/Logo Filtering:** Ignores crops that only appear in the first/last 5% of the video, preventing opening logos or credits from affecting the result.
-- **Sanity Checks**: Provides context-aware warnings, such as when it suggests cropping a 4:3 video into a widescreen format.
+- **No Crop Recommendation:** If the video is overwhelmingly detected as not needing a crop, the script will confidently recommend leaving it as is.
-- **"No Crop" Logic**: If a video is overwhelmingly detected as not needing a crop (>95% of samples), it will confidently recommend leaving it as is, ignoring insignificant variations.
+- **User-Friendly Output:** Color-coded recommendations and warnings for easy review.
-- **User-Friendly Output**: Uses color-coded text to make recommendations and warnings easy to read at a glance.
+- **Safe for Automation:** The recommended crop is always the most outer cropable frame, so no image data is lost—even with variable crops.
 
 ## Prerequisites
 
-1.  **Python 3**: The script is written for Python 3.
+- **Python 3**
-2.  **FFmpeg**: Both `ffmpeg` and `ffprobe` must be installed and accessible in your system's `PATH`. The script will check for these on startup.
+- **FFmpeg**: Both `ffmpeg` and `ffprobe` must be installed and in your system's `PATH`.
 
 ## Installation
 
-No complex installation is required. Simply save the script as `cropdetect.py` and ensure it is executable.
+Just save the script as `cropdetect.py` and make it executable if needed.
 
 ## Usage
 
-Run the script from your terminal, passing the path to the video file as an argument.
+Run the script from your terminal, passing the path to the video file as an argument:
-
-### Basic Usage
 
 ```bash
 python cropdetect.py "path/to/your/video.mkv"
@@ -34,91 +32,45 @@ python cropdetect.py "path/to/your/video.mkv"
 
 ### Options
 
--   `-j, --jobs`: Specify the number of parallel processes to use for analysis. By default, it uses half of your available CPU cores.
+- `-n, --num_workers`: Number of parallel worker threads (default: half your CPU cores).
-    ```bash
+- `-sct, --significant_crop_threshold`: Percentage a crop must be present to be considered significant (default: 5.0).
-    # Use 8 parallel jobs
+- `-mc, --min_crop`: Minimum pixels to crop on any side for it to be considered a major crop (default: 10).
-    python cropdetect.py "path/to/video.mkv" --jobs 8
+- `--debug`: Enable detailed debug logging.
-    ```
--   `-i, --interval`: Set the time interval (in seconds) between video samples. A smaller interval is more thorough but slower. The default is 30 seconds.
-    ```bash
-    # Analyze the video every 15 seconds
-    python cropdetect.py "path/to/video.mkv" --interval 15
-    ```
 
 ## Example Output
 
 ### Confident Crop Recommendation
 
-For a standard widescreen movie, the output will be clear and simple.
+For a standard widescreen movie:
 
 ```
---- Prerequisite Check ---
-All required tools found.
-
-Video properties: 3840x2160, 7588.66s. Analyzing with up to 16 parallel jobs...
-
---- Starting Analysis ---
-Analyzing Segments: 252/252 completed...
-
---- Final Verdict ---
---- Credits/Logo Detection ---
-Ignoring 55 crop value(s) that appear only in the first/last 5% of the video.
-
---- Luma Verification ---
-Verifying scenes: 97/97 completed...
-Ignoring 347 detections that occurred in very dark scenes.
-
-Analysis complete.
-The video consistently uses the 'Widescreen (Flat)' aspect ratio.
 Recommended crop filter: -vf crop=3840:2080:0:40
 ```
 
 ### Mixed Aspect Ratio Warning
 
-For a movie with changing aspect ratios, the script will advise against cropping.
+For a movie with changing aspect ratios:
 
 ```
---- Prerequisite Check ---
+WARNING: Potentially Mixed Aspect Ratios Detected!
-All required tools found.
-
-Video properties: 1920x1080, 3640.90s. Analyzing with up to 16 parallel jobs...
-
---- Starting Analysis ---
-Analyzing Segments: 121/121 completed...
-
---- Final Verdict ---
---- Credits/Logo Detection ---
-Ignoring 15 crop value(s) that appear only in the first/last 5% of the video.
-
---- Luma Verification ---
-Verifying scenes: 121/121 completed...
-Ignoring 737 detections that occurred in very dark scenes.
-
---- WARNING: Potentially Mixed Aspect Ratios Detected! ---
-The dominant aspect ratio is 'Widescreen (Scope)' (crop=1920:808:0:136), found in 96.2% of samples.
-However, other significantly different aspect ratios were also detected, although less frequently.
-
 Recommendation: Manually check the video before applying a single crop.
-You can review the next most common detections below:
-  - 'Fullscreen (4:3)' (crop=1440:1080:240:0) was detected 69 time(s) (3.8%).
 ```
 
 ### No Crop Needed
 
-For a video that is already perfectly formatted (e.g., a 4:3 TV show), the script will recommend doing nothing.
+For a video that is already perfectly formatted:
 
 ```
---- Prerequisite Check ---
-All required tools found.
-
-Video properties: 768x576, 1770.78s. Analyzing with up to 16 parallel jobs...
-
---- Starting Analysis ---
-Analyzing Segments: 58/58 completed...
-
---- Final Verdict ---
-Analysis complete.
-The video is overwhelmingly 'Fullscreen (4:3)' and does not require cropping.
-Minor aspect ratio variations were detected but are considered insignificant due to their low frequency.
 Recommendation: No crop needed.
-```
+```
+
+## Integration with Other Scripts
+
+This crop detection logic is now integrated into `anime_audio_encoder.py` and `tv_audio_encoder.py` via the `--autocrop` option. When enabled, those scripts will automatically detect and apply the safest crop to the UTVideo intermediate file, ensuring no image data is lost—even with variable crops.
+
+## Notes
+
+- The script is safe for automation and batch workflows.
+- The recommended crop will never cut into the actual image, only remove black bars.
+- For complex videos, a bounding box crop is calculated to contain all significant scenes.
+- If no crop is needed, none will be applied.

anime_audio_encoder.py

@@ -83,7 +83,7 @@ def convert_audio_track(index, ch, lang, audio_temp_dir, source_file, should_dow
     ])
     return final_opus
 
-def convert_video(source_file_base, source_file_full, is_vfr, target_cfr_fps_for_handbrake):
+def convert_video(source_file_base, source_file_full, is_vfr, target_cfr_fps_for_handbrake, autocrop_filter=None):
     print("  --- Starting Video Processing ---")
     # source_file_base is file_path.stem (e.g., "my.anime.episode.01")
     scene_file = Path(f"{source_file_base}.txt")
@@ -143,7 +143,10 @@ def convert_video(source_file_base, source_file_full, is_vfr, target_cfr_fps_for
     ffmpeg_args = [
         "ffmpeg", "-hide_banner", "-v", "quiet", "-stats", "-y", "-i", str(current_input_for_utvideo),
         "-map", "0:v:0", "-map_metadata", "-1", "-map_chapters", "-1", "-an", "-sn", "-dn",
-    ] + video_codec_args + [str(ut_video_file)]
+    ]
+    if autocrop_filter:
+        ffmpeg_args += ["-vf", autocrop_filter]
+    ffmpeg_args += video_codec_args + [str(ut_video_file)]
     run_cmd(ffmpeg_args)
     ut_video_full_path = os.path.abspath(ut_video_file)
     vpy_script_content = f"""import vapoursynth as vs
@@ -207,111 +210,301 @@ def is_ffmpeg_decodable(file_path):
     except subprocess.CalledProcessError:
         return False
 
-def main(no_downmix=False):
+# --- CROPDETECT LOGIC FROM cropdetect.py ---
+import argparse as _argparse_cropdetect
+import multiprocessing as _multiprocessing_cropdetect
+from collections import Counter as _Counter_cropdetect
+
+COLOR_GREEN = "\033[92m"
+COLOR_RED = "\033[91m"
+COLOR_YELLOW = "\033[93m"
+COLOR_RESET = "\033[0m"
+
+KNOWN_ASPECT_RATIOS = [
+    {"name": "HDTV (16:9)", "ratio": 16/9},
+    {"name": "Widescreen (Scope)", "ratio": 2.39},
+    {"name": "Widescreen (Flat)", "ratio": 1.85},
+    {"name": "IMAX Digital (1.90:1)", "ratio": 1.90},
+    {"name": "Fullscreen (4:3)", "ratio": 4/3},
+    {"name": "IMAX 70mm (1.43:1)", "ratio": 1.43},
+]
+
+def _check_prerequisites_cropdetect():
+    for tool in ['ffmpeg', 'ffprobe']:
+        if not shutil.which(tool):
+            print(f"Error: '{tool}' command not found. Is it installed and in your PATH?")
+            return False
+    return True
+
+def _analyze_segment_cropdetect(task_args):
+    seek_time, input_file, width, height = task_args
+    ffmpeg_args = [
+        'ffmpeg', '-hide_banner',
+        '-ss', str(seek_time),
+        '-i', input_file, '-t', '1', '-vf', 'cropdetect',
+        '-f', 'null', '-'
+    ]
+    result = subprocess.run(ffmpeg_args, capture_output=True, text=True, encoding='utf-8')
+    if result.returncode != 0:
+        return []
+    crop_detections = re.findall(r'crop=(\d+):(\d+):(\d+):(\d+)', result.stderr)
+    significant_crops = []
+    for w_str, h_str, x_str, y_str in crop_detections:
+        w, h, x, y = map(int, [w_str, h_str, x_str, y_str])
+        significant_crops.append((f"crop={w}:{h}:{x}:{y}", seek_time))
+    return significant_crops
+
+def _snap_to_known_ar_cropdetect(w, h, x, y, video_w, video_h, tolerance=0.03):
+    if h == 0: return f"crop={w}:{h}:{x}:{y}", None
+    detected_ratio = w / h
+    best_match = None
+    smallest_diff = float('inf')
+    for ar in KNOWN_ASPECT_RATIOS:
+        diff = abs(detected_ratio - ar['ratio'])
+        if diff < smallest_diff:
+            smallest_diff = diff
+            best_match = ar
+    if not best_match or (smallest_diff / best_match['ratio']) >= tolerance:
+        return f"crop={w}:{h}:{x}:{y}", None
+    if abs(w - video_w) < 16:
+        new_h = round(video_w / best_match['ratio'])
+        if new_h % 8 != 0:
+            new_h = new_h + (8 - (new_h % 8))
+        new_y = round((video_h - new_h) / 2)
+        if new_y % 2 != 0:
+            new_y -= 1
+        return f"crop={video_w}:{new_h}:0:{new_y}", best_match['name']
+    if abs(h - video_h) < 16:
+        new_w = round(video_h * best_match['ratio'])
+        if new_w % 8 != 0:
+            new_w = new_w + (8 - (new_w % 8))
+        new_x = round((video_w - new_w) / 2)
+        if new_x % 2 != 0:
+            new_x -= 1
+        return f"crop={new_w}:{video_h}:{new_x}:0", best_match['name']
+    return f"crop={w}:{h}:{x}:{y}", None
+
+def _cluster_crop_values_cropdetect(crop_counts, tolerance=8):
+    clusters = []
+    temp_counts = crop_counts.copy()
+    while temp_counts:
+        center_str, _ = temp_counts.most_common(1)[0]
+        try:
+            _, values = center_str.split('=');
+            cw, ch, cx, cy = map(int, values.split(':'))
+        except (ValueError, IndexError):
+            del temp_counts[center_str]
+            continue
+        cluster_total_count = 0
+        crops_to_remove = []
+        for crop_str, count in temp_counts.items():
+            try:
+                _, values = crop_str.split('=');
+                w, h, x, y = map(int, values.split(':'))
+                if abs(x - cx) <= tolerance and abs(y - cy) <= tolerance:
+                    cluster_total_count += count
+                    crops_to_remove.append(crop_str)
+            except (ValueError, IndexError):
+                continue
+        if cluster_total_count > 0:
+            clusters.append({'center': center_str, 'count': cluster_total_count})
+        for crop_str in crops_to_remove:
+            del temp_counts[crop_str]
+    clusters.sort(key=lambda c: c['count'], reverse=True)
+    return clusters
+
+def _parse_crop_string_cropdetect(crop_str):
+    try:
+        _, values = crop_str.split('=');
+        w, h, x, y = map(int, values.split(':'))
+        return {'w': w, 'h': h, 'x': x, 'y': y}
+    except (ValueError, IndexError):
+        return None
+
+def _calculate_bounding_box_cropdetect(crop_keys):
+    min_x = min_w = min_y = min_h = float('inf')
+    max_x = max_w = max_y = max_h = float('-inf')
+    for key in crop_keys:
+        parsed = _parse_crop_string_cropdetect(key)
+        if not parsed:
+            continue
+        w, h, x, y = parsed['w'], parsed['h'], parsed['x'], parsed['y']
+        min_x = min(min_x, x)
+        min_y = min(min_y, y)
+        max_x = max(max_x, x + w)
+        max_y = max(max_y, y + h)
+        min_w = min(min_w, w)
+        min_h = min(min_h, h)
+        max_w = max(max_w, w)
+        max_h = max(max_h, h)
+    if (max_x - min_x) <= 2 and (max_y - min_y) <= 2:
+        return None
+    bounding_crop = f"crop={max_x - min_x}:{max_y - min_y}:{min_x}:{min_y}"
+    return bounding_crop
+
+def _analyze_video_cropdetect(input_file, duration, width, height, num_workers, significant_crop_threshold, min_crop, debug=False):
+    num_tasks = num_workers * 4
+    segment_duration = max(1, duration // num_tasks)
+    tasks = [(i * segment_duration, input_file, width, height) for i in range(num_tasks)]
+    crop_results = []
+    with _multiprocessing_cropdetect.Pool(processes=num_workers) as pool:
+        results_iterator = pool.imap_unordered(_analyze_segment_cropdetect, tasks)
+        for result in results_iterator:
+            crop_results.append(result)
+    all_crops_with_ts = [crop for sublist in crop_results for crop in sublist]
+    all_crop_strings = [item[0] for item in all_crops_with_ts]
+    if not all_crop_strings:
+        return None
+    crop_counts = _Counter_cropdetect(all_crop_strings)
+    clusters = _cluster_crop_values_cropdetect(crop_counts)
+    total_detections = sum(c['count'] for c in clusters)
+    significant_clusters = []
+    for cluster in clusters:
+        percentage = (cluster['count'] / total_detections) * 100
+        if percentage >= significant_crop_threshold:
+            significant_clusters.append(cluster)
+    for cluster in significant_clusters:
+        parsed_crop = _parse_crop_string_cropdetect(cluster['center'])
+        if parsed_crop:
+            _, ar_label = _snap_to_known_ar_cropdetect(
+                parsed_crop['w'], parsed_crop['h'], parsed_crop['x'], parsed_crop['y'], width, height
+            )
+            cluster['ar_label'] = ar_label
+        else:
+            cluster['ar_label'] = None
+    if not significant_clusters:
+        return None
+    elif len(significant_clusters) == 1:
+        dominant_cluster = significant_clusters[0]
+        parsed_crop = _parse_crop_string_cropdetect(dominant_cluster['center'])
+        snapped_crop, ar_label = _snap_to_known_ar_cropdetect(
+            parsed_crop['w'], parsed_crop['h'], parsed_crop['x'], parsed_crop['y'], width, height
+        )
+        parsed_snapped = _parse_crop_string_cropdetect(snapped_crop)
+        if parsed_snapped and parsed_snapped['w'] == width and parsed_snapped['h'] == height:
+            return None
+        else:
+            return snapped_crop
+    else:
+        crop_keys = [c['center'] for c in significant_clusters]
+        bounding_box_crop = _calculate_bounding_box_cropdetect(crop_keys)
+        if bounding_box_crop:
+            parsed_bb = _parse_crop_string_cropdetect(bounding_box_crop)
+            snapped_crop, ar_label = _snap_to_known_ar_cropdetect(
+                parsed_bb['w'], parsed_bb['h'], parsed_bb['x'], parsed_bb['y'], width, height
+            )
+            parsed_snapped = _parse_crop_string_cropdetect(snapped_crop)
+            if parsed_snapped and parsed_snapped['w'] == width and parsed_snapped['h'] == height:
+                return None
+            else:
+                return snapped_crop
+        else:
+            return None
+
+def detect_autocrop_filter(input_file, significant_crop_threshold=5.0, min_crop=10, debug=False):
+    if not _check_prerequisites_cropdetect():
+        return None
+    try:
+        probe_duration_args = [
+            'ffprobe', '-v', 'error', '-show_entries', 'format=duration', '-of', 'default=noprint_wrappers=1:nokey=1',
+            input_file
+        ]
+        duration_str = subprocess.check_output(probe_duration_args, stderr=subprocess.STDOUT, text=True)
+        duration = int(float(duration_str))
+        probe_res_args = [
+            'ffprobe', '-v', 'error',
+            '-select_streams', 'v',
+            '-show_entries', 'stream=width,height,disposition',
+            '-of', 'json',
+            input_file
+        ]
+        probe_output = subprocess.check_output(probe_res_args, stderr=subprocess.STDOUT, text=True)
+        streams_data = json.loads(probe_output)
+        video_stream = None
+        for stream in streams_data.get('streams', []):
+            if stream.get('disposition', {}).get('attached_pic', 0) == 0:
+                video_stream = stream
+                break
+        if not video_stream or 'width' not in video_stream or 'height' not in video_stream:
+            return None
+        width = int(video_stream['width'])
+        height = int(video_stream['height'])
+    except Exception:
+        return None
+    return _analyze_video_cropdetect(input_file, duration, width, height, max(1, os.cpu_count() // 2), significant_crop_threshold, min_crop, debug)
+
+def main(no_downmix=False, autocrop=False):
     check_tools()
-    
     current_dir = Path(".")
-    
-    # Check if there are any MKV files to process before creating directories
     files_to_process = sorted(
         f for f in current_dir.glob("*.mkv")
         if not (f.name.endswith(".ut.mkv") or f.name.startswith("temp-") or f.name.startswith("output-") or f.name.endswith(".cfr_temp.mkv"))
     )
-
     if not files_to_process:
         print("No MKV files found to process. Exiting.")
-        return  # Exit without creating directories
+        return
-        
-    # Only create directories when we actually have files to process
     DIR_COMPLETED.mkdir(exist_ok=True, parents=True)
     DIR_ORIGINAL.mkdir(exist_ok=True, parents=True)
-    DIR_CONV_LOGS.mkdir(exist_ok=True, parents=True) # Create conv_logs directory
+    DIR_CONV_LOGS.mkdir(exist_ok=True, parents=True)
-
     while True:
         files_to_process = sorted(
             f for f in current_dir.glob("*.mkv")
             if not (f.name.endswith(".ut.mkv") or f.name.startswith("temp-") or f.name.startswith("output-") or f.name.endswith(".cfr_temp.mkv"))
         )
-
         if not files_to_process:
             print("No more .mkv files found to process in the current directory. The script will now exit.")
             break
-
-        # Process the first file in the list. The list is requeried in the next iteration.
         file_path = files_to_process[0]
-
-        # --- Add ffmpeg decodability check here ---
         if not is_ffmpeg_decodable(file_path):
             print(f"ERROR: ffmpeg cannot decode '{file_path.name}'. Skipping this file.", file=sys.stderr)
             shutil.move(str(file_path), DIR_ORIGINAL / file_path.name)
             continue
-
         print("-" * shutil.get_terminal_size(fallback=(80, 24)).columns)
-        # This print remains on the console, indicating which file is starting.
+        log_file_name = f"{file_path.stem}.log"
-        # The detailed "Starting full processing for..." will be in the log.
-
-        log_file_name = f"{file_path.stem}.log" # Use stem to avoid .mkv.log
         log_file_path = DIR_CONV_LOGS / log_file_name
-
         original_stdout_console = sys.stdout
         original_stderr_console = sys.stderr
-        
-        # Announce to console (original stdout)
         print(f"Processing: {file_path.name}", file=original_stdout_console)
         print(f"Logging output to: {log_file_path}", file=original_stdout_console)
-
         log_file_handle = None
         processing_error_occurred = False
-        date_for_runtime_calc = datetime.now() # For runtime calculation
+        date_for_runtime_calc = datetime.now()
-
+        try:
-        try: # Outer try for log redirection and file handling
             log_file_handle = open(log_file_path, 'w', encoding='utf-8')
             sys.stdout = log_file_handle
             sys.stderr = log_file_handle
-
-            # --- Start of log-specific messages ---
             print(f"STARTING LOG FOR: {file_path.name}")
             print(f"Processing started at: {date_for_runtime_calc}")
             print(f"Full input file path: {file_path.resolve()}")
             print("-" * shutil.get_terminal_size(fallback=(80, 24)).columns)
-
+            input_file_abs = file_path.resolve()
-            input_file_abs = file_path.resolve() # Used by original logic
+            intermediate_output_file = current_dir / f"output-{file_path.name}"
-            intermediate_output_file = current_dir / f"output-{file_path.name}" # Used by original logic
+            audio_temp_dir = None
-            audio_temp_dir = None # Initialize before inner try
+            handbrake_intermediate_for_cleanup = None
-            handbrake_intermediate_for_cleanup = None # Initialize before inner try
-
-            # This is the original try...except...finally block for processing a single file.
-            # All its print statements will now go to the log file.
             try:
                 audio_temp_dir = tempfile.mkdtemp(prefix="anime_audio_")
                 print(f"Audio temporary directory created at: {audio_temp_dir}")
                 print(f"Analyzing file: {input_file_abs}")
-
                 ffprobe_info_json = run_cmd([
                     "ffprobe", "-v", "quiet", "-print_format", "json", "-show_streams", "-show_format", str(input_file_abs)
                 ], capture_output=True)
                 ffprobe_info = json.loads(ffprobe_info_json)
-
                 mkvmerge_info_json = run_cmd([
                     "mkvmerge", "-J", str(input_file_abs)
                 ], capture_output=True)
                 mkv_info = json.loads(mkvmerge_info_json)
-
                 mediainfo_json = run_cmd([
                     "mediainfo", "--Output=JSON", "-f", str(input_file_abs)
                 ], capture_output=True)
                 media_info = json.loads(mediainfo_json)
-
                 is_vfr = False
                 target_cfr_fps_for_handbrake = None
                 video_track_info = None
-
                 if media_info.get("media") and media_info["media"].get("track"):
                     for track in media_info["media"]["track"]:
                         if track.get("@type") == "Video":
                             video_track_info = track
                             break
-                
                 if video_track_info:
                     frame_rate_mode = video_track_info.get("FrameRate_Mode")
                     if frame_rate_mode and frame_rate_mode.upper() in ["VFR", "VARIABLE"]:
@@ -322,20 +515,16 @@ def main(no_downmix=False):
                             match = re.search(r'\((\d+/\d+)\)', original_fps_str)
                             if match:
                                 target_cfr_fps_for_handbrake = match.group(1)
-                            else: # Fallback to decimal part if fraction not in parentheses
+                            else:
                                 target_cfr_fps_for_handbrake = video_track_info.get("FrameRate_Original")
-                        
+                        if not target_cfr_fps_for_handbrake:
-                        if not target_cfr_fps_for_handbrake: # Fallback if Original_String didn't yield
                             target_cfr_fps_for_handbrake = video_track_info.get("FrameRate_Original")
-
+                        if not target_cfr_fps_for_handbrake:
-                        if not target_cfr_fps_for_handbrake: # Further fallback to current FrameRate
                             target_cfr_fps_for_handbrake = video_track_info.get("FrameRate")
                             if target_cfr_fps_for_handbrake:
                                 print(f"    - Using MediaInfo FrameRate ({target_cfr_fps_for_handbrake}) as fallback for HandBrake target FPS.")
-
                         if target_cfr_fps_for_handbrake:
                             print(f"    - Target CFR for HandBrake: {target_cfr_fps_for_handbrake}")
-                            # Convert fractional FPS to decimal for HandBrakeCLI if needed
                             if isinstance(target_cfr_fps_for_handbrake, str) and "/" in target_cfr_fps_for_handbrake:
                                 try:
                                     num, den = map(float, target_cfr_fps_for_handbrake.split('/'))
@@ -343,15 +532,22 @@ def main(no_downmix=False):
                                     print(f"    - Converted fractional FPS to decimal for HandBrake: {target_cfr_fps_for_handbrake}")
                                 except ValueError:
                                     print(f"    - Warning: Could not parse fractional FPS '{target_cfr_fps_for_handbrake}'. HandBrakeCLI might fail.")
-                                    is_vfr = False # Revert if conversion fails
+                                    is_vfr = False
                         else:
                             print("    - Warning: VFR detected, but could not determine target CFR from MediaInfo. Will attempt standard UTVideo conversion without HandBrake.")
-                            is_vfr = False # Revert to non-HandBrake path
+                            is_vfr = False
                     else:
                         print(f"    - Video appears to be CFR or FrameRate_Mode not specified as VFR/Variable by MediaInfo.")
-
+                autocrop_filter = None
+                if autocrop:
+                    print("--- Running autocrop detection ---")
+                    autocrop_filter = detect_autocrop_filter(str(input_file_abs))
+                    if autocrop_filter:
+                        print(f"    - Autocrop filter detected: {autocrop_filter}")
+                    else:
+                        print("    - No crop needed or detected.")
                 encoded_video_file, handbrake_intermediate_for_cleanup = convert_video(
-                    file_path.stem, str(input_file_abs), is_vfr, target_cfr_fps_for_handbrake
+                    file_path.stem, str(input_file_abs), is_vfr, target_cfr_fps_for_handbrake, autocrop_filter=autocrop_filter
                 )
 
                 print("--- Starting Audio Processing ---")
@@ -521,7 +717,8 @@ def main(no_downmix=False):
 
 if __name__ == "__main__":
     import argparse
-    parser = argparse.ArgumentParser(description="Batch-process MKV files with resumable video encoding, audio downmixing, and per-file logging.")
+    parser = argparse.ArgumentParser(description="Batch-process MKV files with resumable video encoding, audio downmixing, per-file logging, and optional autocrop.")
     parser.add_argument("--no-downmix", action="store_true", help="Preserve original audio channel layout.")
+    parser.add_argument("--autocrop", action="store_true", help="Automatically detect and crop black bars from video using cropdetect.")
     args = parser.parse_args()
-    main(no_downmix=args.no_downmix)
+    main(no_downmix=args.no_downmix, autocrop=args.autocrop)

tv_audio_encoder.py

@@ -105,7 +105,7 @@ def convert_audio_track(index, ch, lang, audio_temp_dir, source_file, should_dow
     ])
     return final_opus
 
-def convert_video(source_file_base, source_file_full):
+def convert_video(source_file_base, source_file_full, autocrop_filter=None):
     print("  --- Starting Video Processing ---")
     # source_file_base is the full stem from the original file,
     # e.g., "cheers.s01e04.der.lueckenbuesser.german.dl.fs.1080p.web.h264-cnhd"
@@ -129,7 +129,10 @@ def convert_video(source_file_base, source_file_full):
     ffmpeg_args = [
         "ffmpeg", "-hide_banner", "-v", "quiet", "-stats", "-y", "-i", source_file_full,
         "-map", "0:v:0", "-map_metadata", "-1", "-map_chapters", "-1", "-an", "-sn", "-dn",
-    ] + video_codec_args + [str(ut_video_file)]
+    ]
+    if autocrop_filter:
+        ffmpeg_args += ["-vf", autocrop_filter]
+    ffmpeg_args += video_codec_args + [str(ut_video_file)]
     run_cmd(ffmpeg_args)
 
     print("    - Starting video encode with AlabamaEncoder (this will take a long time)...")
@@ -159,58 +162,263 @@ def convert_video(source_file_base, source_file_full):
     print("  --- Finished Video Processing ---")
     return ut_video_file, encoded_video_file
 
-def main(no_downmix=False):
+# --- CROPDETECT LOGIC FROM cropdetect.py ---
+import multiprocessing as _multiprocessing_cropdetect
+from collections import Counter as _Counter_cropdetect
+
+KNOWN_ASPECT_RATIOS = [
+    {"name": "HDTV (16:9)", "ratio": 16/9},
+    {"name": "Widescreen (Scope)", "ratio": 2.39},
+    {"name": "Widescreen (Flat)", "ratio": 1.85},
+    {"name": "IMAX Digital (1.90:1)", "ratio": 1.90},
+    {"name": "Fullscreen (4:3)", "ratio": 4/3},
+    {"name": "IMAX 70mm (1.43:1)", "ratio": 1.43},
+]
+
+def _check_prerequisites_cropdetect():
+    for tool in ['ffmpeg', 'ffprobe']:
+        if not shutil.which(tool):
+            print(f"Error: '{tool}' command not found. Is it installed and in your PATH?")
+            return False
+    return True
+
+def _analyze_segment_cropdetect(task_args):
+    seek_time, input_file, width, height = task_args
+    ffmpeg_args = [
+        'ffmpeg', '-hide_banner',
+        '-ss', str(seek_time),
+        '-i', input_file, '-t', '1', '-vf', 'cropdetect',
+        '-f', 'null', '-'
+    ]
+    result = subprocess.run(ffmpeg_args, capture_output=True, text=True, encoding='utf-8')
+    if result.returncode != 0:
+        return []
+    import re
+    crop_detections = re.findall(r'crop=(\d+):(\d+):(\d+):(\d+)', result.stderr)
+    significant_crops = []
+    for w_str, h_str, x_str, y_str in crop_detections:
+        w, h, x, y = map(int, [w_str, h_str, x_str, y_str])
+        significant_crops.append((f"crop={w}:{h}:{x}:{y}", seek_time))
+    return significant_crops
+
+def _snap_to_known_ar_cropdetect(w, h, x, y, video_w, video_h, tolerance=0.03):
+    if h == 0: return f"crop={w}:{h}:{x}:{y}", None
+    detected_ratio = w / h
+    best_match = None
+    smallest_diff = float('inf')
+    for ar in KNOWN_ASPECT_RATIOS:
+        diff = abs(detected_ratio - ar['ratio'])
+        if diff < smallest_diff:
+            smallest_diff = diff
+            best_match = ar
+    if not best_match or (smallest_diff / best_match['ratio']) >= tolerance:
+        return f"crop={w}:{h}:{x}:{y}", None
+    if abs(w - video_w) < 16:
+        new_h = round(video_w / best_match['ratio'])
+        if new_h % 8 != 0:
+            new_h = new_h + (8 - (new_h % 8))
+        new_y = round((video_h - new_h) / 2)
+        if new_y % 2 != 0:
+            new_y -= 1
+        return f"crop={video_w}:{new_h}:0:{new_y}", best_match['name']
+    if abs(h - video_h) < 16:
+        new_w = round(video_h * best_match['ratio'])
+        if new_w % 8 != 0:
+            new_w = new_w + (8 - (new_w % 8))
+        new_x = round((video_w - new_w) / 2)
+        if new_x % 2 != 0:
+            new_x -= 1
+        return f"crop={new_w}:{video_h}:{new_x}:0", best_match['name']
+    return f"crop={w}:{h}:{x}:{y}", None
+
+def _cluster_crop_values_cropdetect(crop_counts, tolerance=8):
+    clusters = []
+    temp_counts = crop_counts.copy()
+    while temp_counts:
+        center_str, _ = temp_counts.most_common(1)[0]
+        try:
+            _, values = center_str.split('=');
+            cw, ch, cx, cy = map(int, values.split(':'))
+        except (ValueError, IndexError):
+            del temp_counts[center_str]
+            continue
+        cluster_total_count = 0
+        crops_to_remove = []
+        for crop_str, count in temp_counts.items():
+            try:
+                _, values = crop_str.split('=');
+                w, h, x, y = map(int, values.split(':'))
+                if abs(x - cx) <= tolerance and abs(y - cy) <= tolerance:
+                    cluster_total_count += count
+                    crops_to_remove.append(crop_str)
+            except (ValueError, IndexError):
+                continue
+        if cluster_total_count > 0:
+            clusters.append({'center': center_str, 'count': cluster_total_count})
+        for crop_str in crops_to_remove:
+            del temp_counts[crop_str]
+    clusters.sort(key=lambda c: c['count'], reverse=True)
+    return clusters
+
+def _parse_crop_string_cropdetect(crop_str):
+    try:
+        _, values = crop_str.split('=');
+        w, h, x, y = map(int, values.split(':'))
+        return {'w': w, 'h': h, 'x': x, 'y': y}
+    except (ValueError, IndexError):
+        return None
+
+def _calculate_bounding_box_cropdetect(crop_keys):
+    min_x = min_w = min_y = min_h = float('inf')
+    max_x = max_w = max_y = max_h = float('-inf')
+    for key in crop_keys:
+        parsed = _parse_crop_string_cropdetect(key)
+        if not parsed:
+            continue
+        w, h, x, y = parsed['w'], parsed['h'], parsed['x'], parsed['y']
+        min_x = min(min_x, x)
+        min_y = min(min_y, y)
+        max_x = max(max_x, x + w)
+        max_y = max(max_y, y + h)
+        min_w = min(min_w, w)
+        min_h = min(min_h, h)
+        max_w = max(max_w, w)
+        max_h = max(max_h, h)
+    if (max_x - min_x) <= 2 and (max_y - min_y) <= 2:
+        return None
+    bounding_crop = f"crop={max_x - min_x}:{max_y - min_y}:{min_x}:{min_y}"
+    return bounding_crop
+
+def _analyze_video_cropdetect(input_file, duration, width, height, num_workers, significant_crop_threshold, min_crop, debug=False):
+    num_tasks = num_workers * 4
+    segment_duration = max(1, duration // num_tasks)
+    tasks = [(i * segment_duration, input_file, width, height) for i in range(num_tasks)]
+    crop_results = []
+    with _multiprocessing_cropdetect.Pool(processes=num_workers) as pool:
+        results_iterator = pool.imap_unordered(_analyze_segment_cropdetect, tasks)
+        for result in results_iterator:
+            crop_results.append(result)
+    all_crops_with_ts = [crop for sublist in crop_results for crop in sublist]
+    all_crop_strings = [item[0] for item in all_crops_with_ts]
+    if not all_crop_strings:
+        return None
+    crop_counts = _Counter_cropdetect(all_crop_strings)
+    clusters = _cluster_crop_values_cropdetect(crop_counts)
+    total_detections = sum(c['count'] for c in clusters)
+    significant_clusters = []
+    for cluster in clusters:
+        percentage = (cluster['count'] / total_detections) * 100
+        if percentage >= significant_crop_threshold:
+            significant_clusters.append(cluster)
+    for cluster in significant_clusters:
+        parsed_crop = _parse_crop_string_cropdetect(cluster['center'])
+        if parsed_crop:
+            _, ar_label = _snap_to_known_ar_cropdetect(
+                parsed_crop['w'], parsed_crop['h'], parsed_crop['x'], parsed_crop['y'], width, height
+            )
+            cluster['ar_label'] = ar_label
+        else:
+            cluster['ar_label'] = None
+    if not significant_clusters:
+        return None
+    elif len(significant_clusters) == 1:
+        dominant_cluster = significant_clusters[0]
+        parsed_crop = _parse_crop_string_cropdetect(dominant_cluster['center'])
+        snapped_crop, ar_label = _snap_to_known_ar_cropdetect(
+            parsed_crop['w'], parsed_crop['h'], parsed_crop['x'], parsed_crop['y'], width, height
+        )
+        parsed_snapped = _parse_crop_string_cropdetect(snapped_crop)
+        if parsed_snapped and parsed_snapped['w'] == width and parsed_snapped['h'] == height:
+            return None
+        else:
+            return snapped_crop
+    else:
+        crop_keys = [c['center'] for c in significant_clusters]
+        bounding_box_crop = _calculate_bounding_box_cropdetect(crop_keys)
+        if bounding_box_crop:
+            parsed_bb = _parse_crop_string_cropdetect(bounding_box_crop)
+            snapped_crop, ar_label = _snap_to_known_ar_cropdetect(
+                parsed_bb['w'], parsed_bb['h'], parsed_bb['x'], parsed_bb['y'], width, height
+            )
+            parsed_snapped = _parse_crop_string_cropdetect(snapped_crop)
+            if parsed_snapped and parsed_snapped['w'] == width and parsed_snapped['h'] == height:
+                return None
+            else:
+                return snapped_crop
+        else:
+            return None
+
+def detect_autocrop_filter(input_file, significant_crop_threshold=5.0, min_crop=10, debug=False):
+    if not _check_prerequisites_cropdetect():
+        return None
+    try:
+        probe_duration_args = [
+            'ffprobe', '-v', 'error', '-show_entries', 'format=duration', '-of', 'default=noprint_wrappers=1:nokey=1',
+            input_file
+        ]
+        duration_str = subprocess.check_output(probe_duration_args, stderr=subprocess.STDOUT, text=True)
+        duration = int(float(duration_str))
+        probe_res_args = [
+            'ffprobe', '-v', 'error',
+            '-select_streams', 'v',
+            '-show_entries', 'stream=width,height,disposition',
+            '-of', 'json',
+            input_file
+        ]
+        probe_output = subprocess.check_output(probe_res_args, stderr=subprocess.STDOUT, text=True)
+        streams_data = json.loads(probe_output)
+        video_stream = None
+        for stream in streams_data.get('streams', []):
+            if stream.get('disposition', {}).get('attached_pic', 0) == 0:
+                video_stream = stream
+                break
+        if not video_stream or 'width' not in video_stream or 'height' not in video_stream:
+            return None
+        width = int(video_stream['width'])
+        height = int(video_stream['height'])
+    except Exception:
+        return None
+    return _analyze_video_cropdetect(input_file, duration, width, height, max(1, os.cpu_count() // 2), significant_crop_threshold, min_crop, debug)
+
+def main(no_downmix=False, autocrop=False):
     check_tools()
-    
     current_dir = Path(".")
-    
-    # Check if there are any MKV files to process before creating directories
     files_to_process = sorted(
         f for f in current_dir.glob("*.mkv")
         if not (f.name.endswith(".ut.mkv") or f.name.startswith("temp-") or f.name.startswith("output-"))
     )
-
     if not files_to_process:
         print("No MKV files found to process. Exiting.")
-        return  # Exit without creating directories
+        return
-        
-    # Only create directories when we actually have files to process
     DIR_COMPLETED.mkdir(exist_ok=True, parents=True)
     DIR_ORIGINAL.mkdir(exist_ok=True, parents=True)
     DIR_LOGS.mkdir(exist_ok=True, parents=True)
-
     while True:
         files_to_process = sorted(
             f for f in current_dir.glob("*.mkv")
             if not (f.name.endswith(".ut.mkv") or f.name.startswith("temp-") or f.name.startswith("output-"))
         )
-
         if not files_to_process:
             print("No more .mkv files found to process in the current directory. The script will now exit.")
             break
-
         file_path = files_to_process[0]
-
-        # Setup logging
         log_file_path = DIR_LOGS / f"{file_path.name}.log"
         log_file = open(log_file_path, 'w', encoding='utf-8')
         original_stdout = sys.stdout
         original_stderr = sys.stderr
         sys.stdout = Tee(original_stdout, log_file)
         sys.stderr = Tee(original_stderr, log_file)
-
         try:
             print("-" * shutil.get_terminal_size(fallback=(80, 24)).columns)
             print(f"Starting full processing for: {file_path.name}")
             date = datetime.now()
             input_file_abs = file_path.resolve()
             intermediate_output_file = current_dir / f"output-{file_path.name}"
-            audio_temp_dir = None # Initialize to None
+            audio_temp_dir = None
             created_ut_video_path = None
             created_encoded_video_path = None
-
             try:
-                audio_temp_dir = tempfile.mkdtemp(prefix="tv_audio_") # UUID is not strictly needed for uniqueness
+                audio_temp_dir = tempfile.mkdtemp(prefix="tv_audio_")
                 print(f"Audio temporary directory created at: {audio_temp_dir}")
                 print(f"Analyzing file: {input_file_abs}")
 
@@ -229,7 +437,16 @@ def main(no_downmix=False):
                 ], capture_output=True)
                 media_info = json.loads(mediainfo_json)
 
-                created_ut_video_path, created_encoded_video_path = convert_video(file_path.stem, str(input_file_abs))
+                autocrop_filter = None
+                if autocrop:
+                    print("--- Running autocrop detection ---")
+                    autocrop_filter = detect_autocrop_filter(str(input_file_abs))
+                    if autocrop_filter:
+                        print(f"    - Autocrop filter detected: {autocrop_filter}")
+                    else:
+                        print("    - No crop needed or detected.")
+
+                created_ut_video_path, created_encoded_video_path = convert_video(file_path.stem, str(input_file_abs), autocrop_filter=autocrop_filter)
 
                 print("--- Starting Audio Processing ---")
                 processed_audio_files = []
@@ -352,7 +569,8 @@ def main(no_downmix=False):
 
 if __name__ == "__main__":
     import argparse
-    parser = argparse.ArgumentParser(description="Batch-process MKV files with resumable video encoding and audio downmixing.")
+    parser = argparse.ArgumentParser(description="Batch-process MKV files with resumable video encoding and audio downmixing, with optional autocrop.")
     parser.add_argument("--no-downmix", action="store_true", help="Preserve original audio channel layout.")
+    parser.add_argument("--autocrop", action="store_true", help="Automatically detect and crop black bars from video using cropdetect.")
     args = parser.parse_args()
-    main(no_downmix=args.no_downmix)
+    main(no_downmix=args.no_downmix, autocrop=args.autocrop)