451 lines
18 KiB
Python
451 lines
18 KiB
Python
#!/usr/bin/env python3
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import argparse
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import subprocess
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import sys
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import os
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import re
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from collections import Counter
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import shutil
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import multiprocessing
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import json
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# ANSI color codes
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COLOR_GREEN = "\033[92m"
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COLOR_RED = "\033[91m"
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COLOR_YELLOW = "\033[93m"
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COLOR_RESET = "\033[0m"
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def check_prerequisites():
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"""Checks if required tools are available."""
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print("--- Prerequisite Check ---")
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all_found = True
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for tool in ['ffmpeg', 'ffprobe']:
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if not shutil.which(tool):
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print(f"Error: '{tool}' command not found. Is it installed and in your PATH?")
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all_found = False
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if not all_found:
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sys.exit(1)
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print("All required tools found.")
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def analyze_segment(task_args):
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"""Function to be run by each worker process. Analyzes one video segment."""
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seek_time, input_file, width, height = task_args
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ffmpeg_args = [
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'ffmpeg', '-hide_banner',
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'-ss', str(seek_time),
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'-i', input_file, '-t', '1', '-vf', 'cropdetect',
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'-f', 'null', '-'
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]
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result = subprocess.run(ffmpeg_args, capture_output=True, text=True, encoding='utf-8')
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if result.returncode != 0:
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return [] # Return empty list on error
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crop_detections = re.findall(r'crop=(\d+):(\d+):(\d+):(\d+)', result.stderr)
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significant_crops = []
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for w_str, h_str, x_str, y_str in crop_detections:
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w, h, x, y = map(int, [w_str, h_str, x_str, y_str])
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# Return the crop string along with the timestamp it was found at
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significant_crops.append((f"crop={w}:{h}:{x}:{y}", seek_time))
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return significant_crops
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def get_frame_luma(input_file, seek_time):
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"""Analyzes a single frame at a given timestamp to get its average luma."""
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ffmpeg_args = [
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'ffmpeg', '-hide_banner',
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'-ss', str(seek_time),
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'-i', input_file,
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'-t', '1',
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'-vf', 'signalstats',
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'-f', 'null', '-'
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]
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result = subprocess.run(ffmpeg_args, capture_output=True, text=True, encoding='utf-8')
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if result.returncode != 0:
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return None # Error during analysis
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# Find the average luma (YAVG) for the frame
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match = re.search(r'YAVG:([0-9.]+)', result.stderr)
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if match:
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return float(match.group(1))
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return None
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def check_luma_for_group(task_args):
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"""Worker function to check the luma for a single group."""
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group_key, sample_ts, input_file, luma_threshold = task_args
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luma = get_frame_luma(input_file, sample_ts)
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is_bright = luma is not None and luma >= luma_threshold
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return (group_key, is_bright)
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KNOWN_ASPECT_RATIOS = [
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{"name": "HDTV (16:9)", "ratio": 16/9},
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{"name": "Widescreen (Scope)", "ratio": 2.39},
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{"name": "Widescreen (Flat)", "ratio": 1.85},
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{"name": "IMAX Digital (1.90:1)", "ratio": 1.90},
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{"name": "Fullscreen (4:3)", "ratio": 4/3},
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{"name": "IMAX 70mm (1.43:1)", "ratio": 1.43},
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]
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def snap_to_known_ar(w, h, x, y, video_w, video_h, tolerance=0.03):
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"""Snaps a crop rectangle to the nearest standard aspect ratio if it's close enough."""
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if h == 0: return f"crop={w}:{h}:{x}:{y}", None
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detected_ratio = w / h
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best_match = None
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smallest_diff = float('inf')
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for ar in KNOWN_ASPECT_RATIOS:
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diff = abs(detected_ratio - ar['ratio'])
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if diff < smallest_diff:
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smallest_diff = diff
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best_match = ar
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# If the best match is not within the tolerance, return the original
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if not best_match or (smallest_diff / best_match['ratio']) >= tolerance:
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return f"crop={w}:{h}:{x}:{y}", None
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# Match found, now snap the dimensions.
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# Heuristic: if width is close to full video width, it's letterboxed.
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if abs(w - video_w) < 16:
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new_h = round(video_w / best_match['ratio'])
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# Round height up to the nearest multiple of 8 for cleaner dimensions and less aggressive cropping.
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if new_h % 8 != 0:
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new_h = new_h + (8 - (new_h % 8))
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new_y = round((video_h - new_h) / 2)
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# Ensure y offset is an even number for compatibility.
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if new_y % 2 != 0:
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new_y -= 1
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return f"crop={video_w}:{new_h}:0:{new_y}", best_match['name']
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# Heuristic: if height is close to full video height, it's pillarboxed.
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if abs(h - video_h) < 16:
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new_w = round(video_h * best_match['ratio'])
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# Round width up to the nearest multiple of 8.
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if new_w % 8 != 0:
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new_w = new_w + (8 - (new_w % 8))
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new_x = round((video_w - new_w) / 2)
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# Ensure x offset is an even number.
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if new_x % 2 != 0:
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new_x -= 1
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return f"crop={new_w}:{video_h}:{new_x}:0", best_match['name']
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# If not clearly letterboxed or pillarboxed, don't snap.
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return f"crop={w}:{h}:{x}:{y}", None
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def cluster_crop_values(crop_counts, tolerance=8):
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"""Groups similar crop values into clusters based on the top-left corner."""
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clusters = []
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temp_counts = crop_counts.copy()
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while temp_counts:
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# Get the most frequent remaining crop as the new cluster center
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center_str, _ = temp_counts.most_common(1)[0]
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try:
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_, values = center_str.split('=')
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cw, ch, cx, cy = map(int, values.split(':'))
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except (ValueError, IndexError):
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del temp_counts[center_str] # Skip malformed strings
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continue
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cluster_total_count = 0
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crops_to_remove = []
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# Find all crops "close" to the center
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for crop_str, count in temp_counts.items():
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try:
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_, values = crop_str.split('=')
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w, h, x, y = map(int, values.split(':'))
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if abs(x - cx) <= tolerance and abs(y - cy) <= tolerance:
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cluster_total_count += count
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crops_to_remove.append(crop_str)
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except (ValueError, IndexError):
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continue
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if cluster_total_count > 0:
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clusters.append({'center': center_str, 'count': cluster_total_count})
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# Remove the clustered crops from the temporary counter
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for crop_str in crops_to_remove:
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del temp_counts[crop_str]
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clusters.sort(key=lambda c: c['count'], reverse=True)
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return clusters
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def parse_crop_string(crop_str):
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"""Parses a 'crop=w:h:x:y' string into a dictionary of integers."""
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try:
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_, values = crop_str.split('=')
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w, h, x, y = map(int, values.split(':'))
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return {'w': w, 'h': h, 'x': x, 'y': y}
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except (ValueError, IndexError):
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return None
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def calculate_bounding_box(crop_keys):
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"""Calculates a bounding box that contains all given crop rectangles."""
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min_x = min_w = min_y = min_h = float('inf')
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max_x = max_w = max_y = max_h = float('-inf')
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for key in crop_keys:
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parsed = parse_crop_string(key)
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if not parsed:
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continue
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w, h, x, y = parsed['w'], parsed['h'], parsed['x'], parsed['y']
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min_x = min(min_x, x)
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min_y = min(min_y, y)
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max_x = max(max_x, x + w)
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max_y = max(max_y, y + h)
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min_w = min(min_w, w)
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min_h = min(min_h, h)
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max_w = max(max_w, w)
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max_h = max(max_h, h)
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# Heuristic: if the bounding box is very close to the min/max, it means all crops were similar
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if (max_x - min_x) <= 2 and (max_y - min_y) <= 2:
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return None # Too uniform, don't create a bounding box
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# Create a crop that spans the entire bounding box
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bounding_crop = f"crop={max_x - min_x}:{max_y - min_y}:{min_x}:{min_y}"
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return bounding_crop
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def is_major_crop(crop_str, video_w, video_h, min_crop_size):
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"""Checks if a crop is significant enough to be recommended by checking if any side is cropped by at least min_crop_size pixels."""
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parsed = parse_crop_string(crop_str)
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if not parsed:
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return False
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w, h, x, y = parsed['w'], parsed['h'], parsed['x'], parsed['y']
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# Calculate how much is cropped from each side
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crop_top = y
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crop_bottom = video_h - (y + h)
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crop_left = x
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crop_right = video_w - (x + w)
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# Return True if the largest crop on any single side meets the threshold
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if max(crop_top, crop_bottom, crop_left, crop_right) >= min_crop_size:
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return True
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return False
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def analyze_video(input_file, duration, width, height, num_workers, significant_crop_threshold, min_crop, debug=False):
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"""Main analysis function for the video."""
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print(f"\n--- Analyzing Video: {os.path.basename(input_file)} ---")
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# Step 1: Analyze video in segments to detect crops
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num_tasks = num_workers * 4
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segment_duration = max(1, duration // num_tasks)
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tasks = [(i * segment_duration, input_file, width, height) for i in range(num_tasks)]
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print(f"Analyzing {len(tasks)} segments across {num_workers} worker(s)...")
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crop_results = []
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with multiprocessing.Pool(processes=num_workers) as pool:
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total_tasks = len(tasks)
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results_iterator = pool.imap_unordered(analyze_segment, tasks)
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for i, result in enumerate(results_iterator, 1):
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crop_results.append(result)
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progress_message = f"Analyzing Segments: {i}/{total_tasks} completed..."
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sys.stdout.write(f"\r{progress_message}")
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sys.stdout.flush()
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print()
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all_crops_with_ts = [crop for sublist in crop_results for crop in sublist]
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all_crop_strings = [item[0] for item in all_crops_with_ts]
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if not all_crop_strings:
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print(f"\n{COLOR_GREEN}Analysis complete. No black bars detected.{COLOR_RESET}")
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return
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crop_counts = Counter(all_crop_strings)
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if debug:
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print("\n--- Debug: Most Common Raw Detections ---")
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for crop_str, count in crop_counts.most_common(10):
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print(f" - {crop_str} (Count: {count})")
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# Step 2: Cluster similar crop values
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clusters = cluster_crop_values(crop_counts)
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total_detections = sum(c['count'] for c in clusters)
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if debug:
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print("\n--- Debug: Detected Clusters ---")
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for cluster in clusters:
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percentage = (cluster['count'] / total_detections) * 100
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print(f" - Center: {cluster['center']}, Count: {cluster['count']} ({percentage:.1f}%)")
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# Step 3: Filter clusters that are below the significance threshold
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significant_clusters = []
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for cluster in clusters:
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percentage = (cluster['count'] / total_detections) * 100
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if percentage >= significant_crop_threshold:
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significant_clusters.append(cluster)
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# Step 4: Determine final recommendation based on significant clusters
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print("\n--- Determining Final Crop Recommendation ---")
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for cluster in significant_clusters:
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parsed_crop = parse_crop_string(cluster['center'])
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if parsed_crop:
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_, ar_label = snap_to_known_ar(
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parsed_crop['w'], parsed_crop['h'], parsed_crop['x'], parsed_crop['y'], width, height
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)
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cluster['ar_label'] = ar_label
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else:
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cluster['ar_label'] = None
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if not significant_clusters:
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print(f"{COLOR_RED}No single crop value meets the {significant_crop_threshold}% significance threshold.{COLOR_RESET}")
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print("Recommendation: Do not crop. Try lowering the -sct threshold.")
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elif len(significant_clusters) == 1:
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dominant_cluster = significant_clusters[0]
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parsed_crop = parse_crop_string(dominant_cluster['center'])
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snapped_crop, ar_label = snap_to_known_ar(
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parsed_crop['w'], parsed_crop['h'], parsed_crop['x'], parsed_crop['y'], width, height
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)
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print("A single dominant aspect ratio was found.")
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if ar_label:
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print(f"The detected crop snaps to the '{ar_label}' aspect ratio.")
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# Check if the final crop is a no-op (i.e., matches source dimensions)
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parsed_snapped = parse_crop_string(snapped_crop)
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if parsed_snapped and parsed_snapped['w'] == width and parsed_snapped['h'] == height:
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print(f"\n{COLOR_GREEN}The detected crop matches the source resolution. No crop is needed.{COLOR_RESET}")
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else:
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print(f"\n{COLOR_GREEN}Recommended crop filter: -vf {snapped_crop}{COLOR_RESET}")
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else: # len > 1, mixed AR case
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print(f"{COLOR_YELLOW}Mixed aspect ratios detected (e.g., IMAX scenes).{COLOR_RESET}")
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print("Calculating a safe 'master' crop to contain all significant scenes.")
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crop_keys = [c['center'] for c in significant_clusters]
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bounding_box_crop = calculate_bounding_box(crop_keys)
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if bounding_box_crop:
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parsed_bb = parse_crop_string(bounding_box_crop)
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snapped_crop, ar_label = snap_to_known_ar(
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parsed_bb['w'], parsed_bb['h'], parsed_bb['x'], parsed_bb['y'], width, height
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)
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print("\n--- Detected Significant Ratios ---")
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for cluster in significant_clusters:
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percentage = (cluster['count'] / total_detections) * 100
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label = f"'{cluster['ar_label']}'" if cluster['ar_label'] else "Custom AR"
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print(f" - {label} ({cluster['center']}) was found in {percentage:.1f}% of samples.")
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print(f"\n{COLOR_GREEN}Analysis complete.{COLOR_RESET}")
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if ar_label:
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print(f"The calculated master crop snaps to the '{ar_label}' aspect ratio.")
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# Check if the final crop is a no-op
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parsed_snapped = parse_crop_string(snapped_crop)
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if parsed_snapped and parsed_snapped['w'] == width and parsed_snapped['h'] == height:
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print(f"{COLOR_GREEN}The final calculated crop matches the source resolution. No crop is needed.{COLOR_RESET}")
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else:
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print(f"{COLOR_GREEN}Recommended safe crop filter: -vf {snapped_crop}{COLOR_RESET}")
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else:
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print(f"{COLOR_RED}Could not calculate a bounding box. Manual review is required.{COLOR_RESET}")
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def main():
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parser = argparse.ArgumentParser(
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description="Analyzes a video file to detect black bars and recommend crop values. "
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"Handles mixed aspect ratios by calculating a safe bounding box.",
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formatter_class=argparse.RawTextHelpFormatter
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)
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parser.add_argument("input", help="Input video file")
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parser.add_argument("-n", "--num_workers", type=int, default=max(1, multiprocessing.cpu_count() // 2), help="Number of worker threads. Defaults to half of available cores.")
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parser.add_argument("-sct", "--significant_crop_threshold", type=float, default=5.0, help="Percentage a crop must be present to be considered 'significant'. Default is 5.0.")
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parser.add_argument("-mc", "--min_crop", type=int, default=10, help="Minimum pixels to crop on any side for it to be considered a 'major' crop. Default is 10.")
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parser.add_argument("--debug", action="store_true", help="Enable detailed debug logging.")
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args = parser.parse_args()
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input_file = args.input
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num_workers = args.num_workers
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significant_crop_threshold = args.significant_crop_threshold
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min_crop = args.min_crop
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# Validate input file
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if not os.path.isfile(input_file):
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print(f"{COLOR_RED}Error: Input file does not exist.{COLOR_RESET}")
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sys.exit(1)
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# Always probe the video file for metadata
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print("--- Probing video file for metadata ---")
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try:
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probe_duration_args = [
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'ffprobe', '-v', 'error', '-show_entries', 'format=duration', '-of', 'default=noprint_wrappers=1:nokey=1',
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input_file
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]
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duration_str = subprocess.check_output(probe_duration_args, stderr=subprocess.STDOUT, text=True)
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duration = int(float(duration_str))
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print(f"Detected duration: {duration}s")
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# Probe for resolution, handling multiple video streams (e.g., with cover art)
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probe_res_args = [
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'ffprobe', '-v', 'error',
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'-select_streams', 'v', # Select all video streams
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'-show_entries', 'stream=width,height,disposition',
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'-of', 'json',
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input_file
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]
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probe_output = subprocess.check_output(probe_res_args, stderr=subprocess.STDOUT, text=True)
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streams_data = json.loads(probe_output)
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video_stream = None
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# Find the first video stream that is NOT an attached picture
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for stream in streams_data.get('streams', []):
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if stream.get('disposition', {}).get('attached_pic', 0) == 0:
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video_stream = stream
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break
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if not video_stream or 'width' not in video_stream or 'height' not in video_stream:
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# If no suitable stream is found, raise an error.
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raise ValueError("Could not find a valid video stream to probe for resolution.")
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width = int(video_stream['width'])
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height = int(video_stream['height'])
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print(f"Detected resolution: {width}x{height}")
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except Exception as e:
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print(f"{COLOR_RED}Error probing video file: {e}{COLOR_RESET}")
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sys.exit(1)
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print(f"\n--- Video Analysis Parameters ---")
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print(f"Input File: {os.path.basename(input_file)}")
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print(f"Duration: {duration}s")
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print(f"Resolution: {width}x{height}")
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print(f"Number of Workers: {num_workers}")
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print(f"Significance Threshold: {significant_crop_threshold}%")
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print(f"Minimum Crop Size: {min_crop}px")
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# Check for required tools
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check_prerequisites()
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# Analyze the video
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analyze_video(input_file, duration, width, height, num_workers, significant_crop_threshold, min_crop, args.debug)
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if __name__ == "__main__":
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main()
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