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Update Lighting_and_Color.py

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Added auto_analysis mode that automatically adjusts the color and contrast to your own settings.
This commit is contained in:
ǝunboɯiɔs
2025-08-04 19:17:05 +09:00
committed by GitHub
parent 746a590b16
commit dc6891f2a0
1 changed files with 321 additions and 82 deletions
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Lighting_and_Color.py
+321 -82
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@@ -24,7 +24,7 @@ def tensor2pil(tensor):
def pil2tensor(image, original_shape):
"""
PIL画像をPyTorchテンソルに変換し、元の形状に合わせる。
BHWC形式で値が[0, 1]の範囲のテンソルを返す。
BHWC形状で値が[0, 1]の範囲のテンソルを返す。
"""
# numpy配列に変換し、[0, 1]に正規化
img_array = np.array(image).astype(np.float32) / 255.0
@@ -52,37 +52,27 @@ def medianFilter(image, radius, num_samples, threshold):
class FluxLightingAndColor:
"""
FluxLightingAndColor
画像の照明と色調を調整するためのノードクラス
FluxLightingAndColor - Enhanced Version
画像の照明と色調を自動解析し調整するためのノードクラス
主な機能:
- 彩度調整
- 被写界深度(DoF)処理
- 最適化された処理順序
- デバッグ出力
新機能:
- 画像解析による自動パラメーター補完
- 色温度分析
- 輝度分布解析
- 彩度解析
- カスタマイズ可能な自動調整
"""
@classmethod
def INPUT_TYPES(s):
"""
入力パラメータの定義
required: 必須パラメータ
- image: 入力画像
- black/mid/white_level: レベル調整用パラメータ
- red/green/blue_level: 各色チャンネルの強度
- brightness: 明るさ
- saturation: 彩度
optional: オプションパラメータ
- depth: 深度マップ画像
- dof_mode: 被写界深度エフェクトモード
- dof_radius: ぼかしの半径
- dof_samples: サンプル数
- debug_mode: デバッグ出力の有無
"""
return {
"required": {
"image": ("IMAGE",),
"auto_analysis": ("BOOLEAN", {"default": True}),
"analysis_strength": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.1}),
"black_level": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"mid_level": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"white_level": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
@@ -97,25 +87,224 @@ class FluxLightingAndColor:
"dof_mode": (["none", "mock", "gaussian", "box"],),
"dof_radius": ("INT", {"default": 8, "min": 1, "max": 128, "step": 1}),
"dof_samples": ("INT", {"default": 1, "min": 1, "max": 3, "step": 1}),
"auto_brightness": ("BOOLEAN", {"default": True}),
"auto_color_balance": ("BOOLEAN", {"default": True}),
"auto_saturation": ("BOOLEAN", {"default": True}),
"auto_contrast": ("BOOLEAN", {"default": True}),
"debug_mode": ("BOOLEAN", {"default": False}),
}
}
RETURN_TYPES = ("IMAGE",)
RETURN_TYPES = ("IMAGE", "STRING")
RETURN_NAMES = ("image", "analysis_report")
FUNCTION = "apply_lighting_and_color"
CATEGORY = "image/adjustments"
def analyze_image_characteristics(self, img_array, debug=False):
"""
画像の特性を解析し、最適な調整パラメーターを算出する
Returns:
- dict: 解析結果と推奨パラメーター
"""
analysis = {}
# 輝度分析
luminance = 0.299 * img_array[:,:,0] + 0.587 * img_array[:,:,1] + 0.114 * img_array[:,:,2]
# 基本統計
analysis['mean_luminance'] = float(np.mean(luminance))
analysis['std_luminance'] = float(np.std(luminance))
analysis['min_luminance'] = float(np.min(luminance))
analysis['max_luminance'] = float(np.max(luminance))
# ヒストグラム分析
hist, bins = np.histogram(luminance.flatten(), bins=256, range=(0, 1))
analysis['histogram'] = hist
# 色温度分析（簡易版）
mean_r = float(np.mean(img_array[:,:,0]))
mean_g = float(np.mean(img_array[:,:,1]))
mean_b = float(np.mean(img_array[:,:,2]))
analysis['mean_rgb'] = [mean_r, mean_g, mean_b]
# 色温度推定 (青が強い = 冷たい、赤が強い = 暖かい)
if mean_b > mean_r:
analysis['color_temperature'] = 'cool'
analysis['temp_bias'] = mean_b - mean_r
else:
analysis['color_temperature'] = 'warm'
analysis['temp_bias'] = mean_r - mean_b
# 彩度分析
hsv_array = cv.cvtColor((img_array * 255).astype(np.uint8), cv.COLOR_RGB2HSV)
saturation_values = hsv_array[:,:,1] / 255.0
analysis['mean_saturation'] = float(np.mean(saturation_values))
analysis['std_saturation'] = float(np.std(saturation_values))
# コントラスト分析
analysis['contrast_ratio'] = analysis['max_luminance'] - analysis['min_luminance']
# 推奨パラメーター計算
recommendations = self.calculate_recommendations(analysis, debug)
analysis['recommendations'] = recommendations
if debug:
print("\n=== Image Analysis Results ===")
print(f"Mean Luminance: {analysis['mean_luminance']:.3f}")
print(f"Luminance Range: {analysis['min_luminance']:.3f} - {analysis['max_luminance']:.3f}")
print(f"Color Temperature: {analysis['color_temperature']} (bias: {analysis['temp_bias']:.3f})")
print(f"Mean Saturation: {analysis['mean_saturation']:.3f}")
print(f"Contrast Ratio: {analysis['contrast_ratio']:.3f}")
print("Recommendations:", recommendations)
print("===============================\n")
return analysis
def calculate_recommendations(self, analysis, debug=False):
"""
解析結果に基づいて推奨パラメーターを計算する
"""
recommendations = {}
# 明るさ調整の推奨値
target_luminance = 0.5
current_luminance = analysis['mean_luminance']
if current_luminance < 0.3: # 暗い画像
recommendations['brightness'] = 1.2 + (0.3 - current_luminance) * 2
elif current_luminance > 0.7: # 明るい画像
recommendations['brightness'] = 0.8 + (0.7 - current_luminance) * 0.5
else:
recommendations['brightness'] = 1.0
# コントラスト調整
if analysis['contrast_ratio'] < 0.5: # 低コントラスト
recommendations['black_level'] = max(0, analysis['min_luminance'] - 0.05)
recommendations['white_level'] = min(1, analysis['max_luminance'] + 0.05)
else:
recommendations['black_level'] = 0.0
recommendations['white_level'] = 1.0
recommendations['mid_level'] = 0.5
# 色温度補正
temp_bias = analysis['temp_bias']
if analysis['color_temperature'] == 'cool' and temp_bias > 0.05:
# 冷たすぎる場合、赤を強化、青を減少
recommendations['red_level'] = 1.0 + min(temp_bias * 2, 0.3)
recommendations['green_level'] = 1.0
recommendations['blue_level'] = 1.0 - min(temp_bias * 1.5, 0.2)
elif analysis['color_temperature'] == 'warm' and temp_bias > 0.05:
# 暖かすぎる場合、青を強化、赤を減少
recommendations['red_level'] = 1.0 - min(temp_bias * 1.5, 0.2)
recommendations['green_level'] = 1.0
recommendations['blue_level'] = 1.0 + min(temp_bias * 2, 0.3)
else:
recommendations['red_level'] = 1.0
recommendations['green_level'] = 1.0
recommendations['blue_level'] = 1.0
# 彩度調整
mean_sat = analysis['mean_saturation']
if mean_sat < 0.4: # 彩度が低い
recommendations['saturation'] = 1.0 + (0.4 - mean_sat) * 2
elif mean_sat > 0.8: # 彩度が高い
recommendations['saturation'] = 1.0 - (mean_sat - 0.8) * 1.5
else:
recommendations['saturation'] = 1.0
# 値を適切な範囲にクリップ
recommendations['brightness'] = np.clip(recommendations['brightness'], 0.0, 2.0)
recommendations['saturation'] = np.clip(recommendations['saturation'], 0.0, 2.0)
recommendations['red_level'] = np.clip(recommendations['red_level'], 0.0, 2.0)
recommendations['green_level'] = np.clip(recommendations['green_level'], 0.0, 2.0)
recommendations['blue_level'] = np.clip(recommendations['blue_level'], 0.0, 2.0)
return recommendations
def blend_parameters(self, user_params, recommendations, strength, auto_flags, debug=False):
"""
ユーザー設定と推奨値をブレンドする
Parameters:
- user_params: ユーザーが設定したパラメーター
- recommendations: 解析による推奨パラメーター
- strength: 自動調整の強度 (0.0-1.0)
- auto_flags: 各調整項目の自動有効フラグ
"""
blended = {}
param_mapping = {
'brightness': 'auto_brightness',
'saturation': 'auto_saturation',
'red_level': 'auto_color_balance',
'green_level': 'auto_color_balance',
'blue_level': 'auto_color_balance',
'black_level': 'auto_contrast',
'white_level': 'auto_contrast',
'mid_level': 'auto_contrast'
}
for param_name in ['brightness', 'saturation', 'red_level', 'green_level', 'blue_level',
'black_level', 'white_level', 'mid_level']:
user_value = user_params.get(param_name, 1.0)
recommended_value = recommendations.get(param_name, user_value)
auto_flag_name = param_mapping.get(param_name, 'auto_brightness')
if auto_flags.get(auto_flag_name, True):
# 自動調整が有効な場合、ユーザー値と推奨値をブレンド
blended[param_name] = user_value * (1 - strength) + recommended_value * strength
else:
# 自動調整が無効な場合、ユーザー値をそのまま使用
blended[param_name] = user_value
if debug:
print("\n=== Parameter Blending ===")
for param_name in blended:
user_val = user_params.get(param_name, 1.0)
rec_val = recommendations.get(param_name, user_val)
final_val = blended[param_name]
auto_flag = param_mapping.get(param_name, 'auto_brightness')
enabled = auto_flags.get(auto_flag, True)
print(f"{param_name}: User={user_val:.3f}, Rec={rec_val:.3f}, Final={final_val:.3f} (Auto: {enabled})")
print("=========================\n")
return blended
def generate_analysis_report(self, analysis, blended_params, user_params):
"""
解析結果のレポートを生成する
"""
report = []
report.append("=== Image Analysis Report ===")
report.append(f"Mean Luminance: {analysis['mean_luminance']:.3f}")
report.append(f"Luminance Range: {analysis['min_luminance']:.3f} - {analysis['max_luminance']:.3f}")
report.append(f"Color Temperature: {analysis['color_temperature']} (bias: {analysis['temp_bias']:.3f})")
report.append(f"Mean Saturation: {analysis['mean_saturation']:.3f}")
report.append(f"Contrast Ratio: {analysis['contrast_ratio']:.3f}")
report.append("")
report.append("=== Applied Adjustments ===")
for param_name in ['brightness', 'saturation', 'red_level', 'green_level', 'blue_level']:
user_val = user_params.get(param_name, 1.0)
final_val = blended_params.get(param_name, user_val)
change = final_val - user_val
if abs(change) > 0.01:
sign = "+" if change > 0 else ""
report.append(f"{param_name}: {user_val:.3f} → {final_val:.3f} ({sign}{change:.3f})")
else:
report.append(f"{param_name}: {final_val:.3f} (no change)")
report.append("=============================")
return "\n".join(report)
def apply_dof(self, img, depth_map=None, mode='none', radius=8, samples=1, debug=False):
"""
被写界深度(DoF)エフェクトを適用する
Parameters:
- img: 元画像
- depth_map: 深度マップ
- mode: エフェクトモード (none/mock/gaussian/box)
- radius: ぼかしの半径
- samples: サンプル数
- debug: デバッグ出力フラグ
"""
if mode == 'none' or depth_map is None:
if debug:
@@ -158,117 +347,167 @@ class FluxLightingAndColor:
def adjust_levels(self, img_array, black_level, mid_level, white_level, debug=False):
"""
画像のレベル調整を行う
Parameters:
- img_array: 画像配列
- black_level: 黒レベル
- mid_level: 中間トーン
- white_level: 白レベル
- debug: デバッグ出力フラグ
"""
if debug:
print(f"Levels: Adjusting (black: {black_level}, mid: {mid_level}, white: {white_level})")
print(f"Levels: Adjusting (black: {black_level:.3f}, mid: {mid_level:.3f}, white: {white_level:.3f})")
# Apply level adjustments
img_array = (img_array - black_level) / (white_level - black_level)
img_array = np.clip(img_array, 0, 1)
# Apply gamma correction based on mid level
if mid_level != 0.5:
gamma = np.log(0.5) / np.log(mid_level + 1e-8) # avoid division by zero
img_array = np.power(img_array, gamma)
return img_array
def adjust_channels(self, img_array, red_level, green_level, blue_level, debug=False):
"""
RGB各チャンネルの強度を調整する
Parameters:
- img_array: 画像配列
- red_level: 赤チャンネルの強度
- green_level: 緑チャンネルの強度
- blue_level: 青チャンネルの強度
- debug: デバッグ出力フラグ
"""
if debug:
print(f"Channels: Adjusting (R: {red_level}, G: {green_level}, B: {blue_level})")
print(f"Channels: Adjusting (R: {red_level:.3f}, G: {green_level:.3f}, B: {blue_level:.3f})")
# Split and adjust each channel
img_array[:,:,0] = np.clip(img_array[:,:,0] * red_level, 0, 1)
img_array[:,:,1] = np.clip(img_array[:,:,1] * green_level, 0, 1)
img_array[:,:,2] = np.clip(img_array[:,:,2] * blue_level, 0, 1)
return img_array
def apply_lighting_and_color(self, image, black_level, mid_level, white_level,
def apply_lighting_and_color(self, image, auto_analysis, analysis_strength,
black_level, mid_level, white_level,
red_level, green_level, blue_level, brightness, saturation,
depth=None, dof_mode="none", dof_radius=8, dof_samples=1,
debug_mode=False):
auto_brightness=True, auto_color_balance=True,
auto_saturation=True, auto_contrast=True, debug_mode=False):
"""
メインの処理関数。以下の順序で画像処理を実行:
1. 入力テンソルをPIL画像に変換
2. 被写界深度エフェクトの適用（深度マップがある場合）
3. numpy配列に変換
4. 明るさの調整
5. レベル調整
6. チャンネル調整
7. PIL画像に再変換
8. 彩度の調整
9. 最終的なコントラスト調整
10. テンソルに再変換して返却
メインの処理関数。画像解析と自動調整機能を含む。
"""
try:
if debug_mode:
print("\n=== FluxLightingAndColor Starting ===")
print("\n=== FluxLightingAndColor Enhanced Starting ===")
print(f"Input tensor shape: {image.shape}, dtype: {image.dtype}")
print(f"Auto analysis: {auto_analysis}, Strength: {analysis_strength}")
# 1. 入力テンソルをPIL画像に変換
img_pil = tensor2pil(image)
if debug_mode:
print("Step 1: Converted input tensor to PIL image")
# 2. 深度マップがある場合はDoFを適用
# 2. 画像解析（自動調整が有効な場合）
analysis_report = "No analysis performed"
final_params = {
'black_level': black_level,
'mid_level': mid_level,
'white_level': white_level,
'red_level': red_level,
'green_level': green_level,
'blue_level': blue_level,
'brightness': brightness,
'saturation': saturation
}
if auto_analysis:
img_array_for_analysis = np.array(img_pil).astype(float) / 255.0
analysis = self.analyze_image_characteristics(img_array_for_analysis, debug_mode)
user_params = {
'black_level': black_level,
'mid_level': mid_level,
'white_level': white_level,
'red_level': red_level,
'green_level': green_level,
'blue_level': blue_level,
'brightness': brightness,
'saturation': saturation
}
auto_flags = {
'auto_brightness': auto_brightness,
'auto_color_balance': auto_color_balance,
'auto_saturation': auto_saturation,
'auto_contrast': auto_contrast
}
final_params = self.blend_parameters(
user_params, analysis['recommendations'],
analysis_strength, auto_flags, debug_mode
)
analysis_report = self.generate_analysis_report(analysis, final_params, user_params)
if debug_mode:
print("Step 2: Completed image analysis and parameter blending")
# 3. 深度マップがある場合はDoFを適用
if depth is not None and dof_mode != "none":
depth_pil = tensor2pil(depth)
img_pil = self.apply_dof(img_pil, depth_pil, dof_mode, dof_radius, dof_samples, debug_mode)
if debug_mode:
print("Step 2: Applied depth of field effect")
print("Step 3: Applied depth of field effect")
# 3. 処理用にnumpy配列に変換
# 4. 処理用にnumpy配列に変換
img_array = np.array(img_pil).astype(float) / 255.0
# 4. トーン調整を適用
# 5. 明るさ調整を適用
if debug_mode:
print(f"Step 4: Applying brightness boost: {brightness}")
img_array = np.power(img_array, 0.7) * brightness
print(f"Step 5: Applying brightness: {final_params['brightness']:.3f}")
img_array = np.power(img_array, 0.7) * final_params['brightness']
# 5. レベル調整を適用
img_array = self.adjust_levels(img_array, black_level, mid_level, white_level, debug_mode)
# 6. レベル調整を適用
img_array = self.adjust_levels(
img_array,
final_params['black_level'],
final_params['mid_level'],
final_params['white_level'],
debug_mode
)
# 6. チャンネル調整を適用
img_array = self.adjust_channels(img_array, red_level, green_level, blue_level, debug_mode)
# 7. チャンネル調整を適用
img_array = self.adjust_channels(
img_array,
final_params['red_level'],
final_params['green_level'],
final_params['blue_level'],
debug_mode
)
# 7. エンハンス処理用にPIL画像に再変換
# 8. エンハンス処理用にPIL画像に再変換
processed = Image.fromarray((np.clip(img_array * 255.0, 0, 255)).astype(np.uint8))
# 8. 彩度を適用
# 9. 彩度を適用
if debug_mode:
print(f"Step 8: Applying saturation: {saturation}")
print(f"Step 9: Applying saturation: {final_params['saturation']:.3f}")
enhancer = ImageEnhance.Color(processed)
processed = enhancer.enhance(saturation)
processed = enhancer.enhance(final_params['saturation'])
# 9. 最終的なコントラストを適用
# 10. 最終的なコントラストを適用
contrast_boost = 1.3
if auto_analysis and auto_contrast:
# 既にコントラストが高い場合は控えめに
if analysis['contrast_ratio'] > 0.7:
contrast_boost = 1.1
if debug_mode:
print("Step 9: Applying final contrast boost (1.3)")
print(f"Step 10: Applying final contrast boost: {contrast_boost}")
enhancer = ImageEnhance.Contrast(processed)
processed = enhancer.enhance(1.3)
processed = enhancer.enhance(contrast_boost)
# 10. テンソルに再変換
# 11. テンソルに再変換
result = pil2tensor(processed, image.shape)
if debug_mode:
print(f"Output tensor shape: {result.shape}, dtype: {result.dtype}")
print("=== Processing complete ===\n")
print("=== Enhanced Processing Complete ===\n")
return (result,)
return (result, analysis_report)
except Exception as e:
print(f"Error in apply_lighting_and_color: {str(e)}")
import traceback
traceback.print_exc()
return (image,)
return (image, f"Error occurred: {str(e)}")
# Node registration
NODE_CLASS_MAPPINGS = {
@@ -276,5 +515,5 @@ NODE_CLASS_MAPPINGS = {
}
NODE_DISPLAY_NAME_MAPPINGS = {
"FluxLightingAndColor": "Flux Lighting & Color"
"FluxLightingAndColor": "Flux Lighting & Color (Enhanced)"
}