309 lines
10 KiB
Python
309 lines
10 KiB
Python
import cv2
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import numpy as np
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import torch
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from PIL import Image
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import torchvision
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import functools
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import inspect
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# 套用裝飾器到現有函數
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def unsharp_mask(image, kernel_size=(5, 5), sigma=1.0, amount=1.0, threshold=0):
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"""使用OpenCV實現的Unsharp Mask銳化處理
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參數:
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image: PIL.Image對象(RGB格式)
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kernel_size: 高斯模糊的核大小，必須是奇數
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sigma: 高斯模糊的標準差
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amount: 銳化程度，值越大效果越強
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threshold: 邊緣檢測閾值，僅在邊緣處進行銳化
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返回:
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銳化後的PIL.Image對象
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"""
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# 轉換PIL圖像為numpy數組
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numpy_img = np.array(image, dtype=np.uint8)
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# 對原圖進行高斯模糊
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blurred = cv2.GaussianBlur(numpy_img, kernel_size, sigma)
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# 計算銳化後的圖像
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sharpened = cv2.addWeighted(numpy_img, 1 + amount, blurred, -amount, 0)
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# 如果設置了threshold，只在邊緣處應用銳化
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if threshold > 0:
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low_contrast_mask = np.absolute(numpy_img - blurred) < threshold
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np.copyto(sharpened, numpy_img, where=low_contrast_mask)
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# 確保像素值在有效範圍內
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sharpened = np.clip(sharpened, 0, 255).astype(np.uint8)
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# 轉回PIL圖像
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return Image.fromarray(sharpened)
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def histogram_equalization(image):
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"""GPU加速的一般直方圖等化
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參數:
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image: PIL.Image對象(RGB格式)
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返回:
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直方圖等化後的PIL.Image對象
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"""
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# 轉換為numpy數組並轉為PyTorch張量
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numpy_img = np.array(image)
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tensor_img = torch.from_numpy(numpy_img).float().to('cuda')
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# 分離通道並進行直方圖等化
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result = torch.zeros_like(tensor_img)
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for i in range(3): # 對RGB三個通道分別處理
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channel = tensor_img[..., i]
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# 計算直方圖
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hist = torch.histc(channel, bins=256, min=0, max=255)
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# 計算累積分布函數(CDF)
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cdf = torch.cumsum(hist, dim=0)
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cdf_normalized = ((cdf - cdf.min()) * 255) / (cdf.max() - cdf.min())
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# 應用直方圖等化
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result[..., i] = cdf_normalized[channel.long()]
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# 轉回CPU和numpy數組
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result = torch.clamp(result, 0, 255).byte()
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result_np = result.cpu().numpy()
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return Image.fromarray(result_np)
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def Contrast_Limited_Adaptive_Histogram_Equalization(image, clip_limit=3.0, tile_size=(8, 8)):
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"""使用OpenCV實現的對比度限制自適應直方圖均衡化(CLAHE)
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參數:
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image: PIL.Image對象(RGB格式)
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clip_limit: 剪切限制，用於限制對比度增強的程度，較大的值會產生更強的對比度
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tile_size: 圖像分塊大小的元組(height, width)，較小的值會產生更局部的增強效果
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返回:
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CLAHE處理後的PIL.Image對象
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"""
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# 將PIL圖像轉換為OpenCV格式（BGR）
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numpy_img = np.array(image)
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bgr_img = cv2.cvtColor(numpy_img, cv2.COLOR_RGB2BGR)
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# 轉換到LAB色彩空間
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lab_img = cv2.cvtColor(bgr_img, cv2.COLOR_BGR2LAB)
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# 創建CLAHE對象
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clahe = cv2.createCLAHE(clipLimit=clip_limit, tileGridSize=tile_size)
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# 分離LAB通道
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l, a, b = cv2.split(lab_img)
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# 對L通道應用CLAHE
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l_clahe = clahe.apply(l)
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# 合併處理後的L通道與原始的a和b通道
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lab_output = cv2.merge([l_clahe, a, b])
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# 將LAB轉回BGR，然後轉換為RGB
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bgr_output = cv2.cvtColor(lab_output, cv2.COLOR_LAB2BGR)
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rgb_output = cv2.cvtColor(bgr_output, cv2.COLOR_BGR2RGB)
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# 轉換為PIL圖像並返回
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return Image.fromarray(rgb_output)
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def adaptive_histogram_equalization_without_limit(image, tile_size=(8, 8)):
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"""使用OpenCV實現的自適應直方圖均衡化(AHE)
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參數:
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image: PIL.Image對象(RGB格式)
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tile_size: 圖像分塊大小的元組(height, width)，較小的值會產生更局部的增強效果
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返回:
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AHE處理後的PIL.Image對象
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"""
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# 將PIL圖像轉換為OpenCV格式（BGR）
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numpy_img = np.array(image)
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bgr_img = cv2.cvtColor(numpy_img, cv2.COLOR_RGB2BGR)
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# 轉換到LAB色彩空間
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lab_img = cv2.cvtColor(bgr_img, cv2.COLOR_BGR2LAB)
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# 分離LAB通道
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l, a, b = cv2.split(lab_img)
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# 創建AHE對象（不設置clip limit）
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clahe = cv2.createCLAHE(clipLimit=None, tileGridSize=tile_size)
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# 對L通道應用AHE
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l_ahe = clahe.apply(l)
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# 合併處理後的L通道與原始的a和b通道
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lab_output = cv2.merge([l_ahe, a, b])
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# 將LAB轉回BGR，然後轉換為RGB
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bgr_output = cv2.cvtColor(lab_output, cv2.COLOR_LAB2BGR)
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rgb_output = cv2.cvtColor(bgr_output, cv2.COLOR_BGR2RGB)
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# 轉換為PIL圖像並返回
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return Image.fromarray(rgb_output)
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def laplacian_sharpen(image):
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"""
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GPU加速的拉普拉斯銳化處理函數
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參數:
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image: PIL.Image對象(RGB格式)
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返回:
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銳化後的PIL.Image對象
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"""
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# 轉換為numpy數組並轉為PyTorch張量
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numpy_img = np.array(image)
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tensor_img = torch.from_numpy(numpy_img).float().to('cuda')
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# 創建拉普拉斯算子
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laplacian_kernel = torch.tensor([
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[0, 1, 0],
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[1, -4, 1],
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[0, 1, 0]
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], dtype=torch.float32, device='cuda').unsqueeze(0).unsqueeze(0)
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# 對每個通道進行處理
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result = torch.zeros_like(tensor_img)
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for i in range(3): # RGB三個通道
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channel = tensor_img[..., i]
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# 添加批次和通道維度
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channel = channel.unsqueeze(0).unsqueeze(0)
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# 應用拉普拉斯算子
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laplacian = torch.nn.functional.conv2d(channel, laplacian_kernel, padding=1)
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# 移除批次和通道維度
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laplacian = laplacian.squeeze()
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# 銳化處理：原圖 - 拉普拉斯
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result[..., i] = channel.squeeze() - laplacian
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# 確保像素值在合理範圍內
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result = torch.clamp(result, 0, 255).byte()
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# 轉回CPU和numpy數組
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result_np = result.cpu().numpy()
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return Image.fromarray(result_np)
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def adjust_hsv(image, v_adjustment=0):
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"""調整圖像的HSV色彩空間中的H和V通道
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參數:
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image: PIL.Image對象(RGB格式)
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v_adjustment: V通道的調整值，範圍建議在[-255, 255]之間
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返回:
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HSV調整後的PIL.Image對象
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"""
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# 將PIL圖像轉換為OpenCV格式（BGR）
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numpy_img = np.array(image)
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bgr_img = cv2.cvtColor(numpy_img, cv2.COLOR_RGB2BGR)
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# 轉換到HSV色彩空間
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hsv_img = cv2.cvtColor(bgr_img, cv2.COLOR_BGR2HSV)
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# 調整V通道
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hsv_img[..., 2] = np.clip(hsv_img[..., 2] + v_adjustment, 0, 255)
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# 將HSV轉回BGR，然後轉換為RGB
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bgr_output = cv2.cvtColor(hsv_img, cv2.COLOR_HSV2BGR)
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rgb_output = cv2.cvtColor(bgr_output, cv2.COLOR_BGR2RGB)
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# 轉換為PIL圖像並返回
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return Image.fromarray(rgb_output)
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def gamma_correction(image, gamma=1.0):
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"""對圖像進行伽馬校正
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參數:
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image: PIL.Image對象(RGB格式)
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gamma: 伽馬值，gamma > 1 時圖像變暗，gamma < 1 時圖像變亮，gamma = 1 時保持不變
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返回:
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伽馬校正後的PIL.Image對象
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"""
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# 將PIL圖像轉換為numpy數組
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numpy_img = np.array(image)
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# 將像素值歸一化到[0, 1]範圍
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normalized = numpy_img.astype(float) / 255.0
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# 應用伽馬校正
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corrected = np.power(normalized, gamma)
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# 將值縮放回[0, 255]範圍
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output = np.clip(corrected * 255.0, 0, 255).astype(np.uint8)
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# 轉換回PIL圖像並返回
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return Image.fromarray(output)
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def Hight_Light(image, Threshold):
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image = np.array(image)
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gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
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# 使用閾值檢測高光點（白色液滴）
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_, thresh = cv2.threshold(gray, Threshold, 255, cv2.THRESH_BINARY)
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# 使用形態學操作（膨脹）來擴大遮罩區域
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kernel = np.ones((5, 5), np.uint8)
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dilated = cv2.dilate(thresh, kernel, iterations=1)
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# 使用 inpaint 修復高光點
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image_inpaint = cv2.inpaint(image, dilated, 3, cv2.INPAINT_TELEA)
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return Image.fromarray(image_inpaint)
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def median_filter(image: Image.Image, kernel_size: int = 3):
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"""
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中值濾波（Median Filter）實現
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參數:
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image: PIL.Image對象(RGB格式)
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kernel_size: 濾波核大小，必須是奇數
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返回:
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濾波後的PIL.Image對象
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"""
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# 確保kernel_size是奇數
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if kernel_size % 2 == 0:
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kernel_size += 1
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# 轉換PIL圖像為numpy數組
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numpy_img = np.array(image, dtype=np.uint8)
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# 對每個通道應用中值濾波
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result = np.zeros_like(numpy_img)
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for i in range(3): # 對RGB三個通道分別處理
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result[:, :, i] = cv2.medianBlur(numpy_img[:, :, i], kernel_size)
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# 確保像素值在有效範圍內
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result = np.clip(result, 0, 255).astype(np.uint8)
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# 轉回PIL圖像
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return Image.fromarray(result)
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def mean_filter(image: Image.Image, kernel_size: int = 3):
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"""
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均質濾波（Mean Filter）實現
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參數:
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image: PIL.Image對象(RGB格式)
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kernel_size: 濾波核大小，必須是奇數
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返回:
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濾波後的PIL.Image對象
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"""
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# 確保kernel_size是奇數
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if kernel_size % 2 == 0:
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kernel_size += 1
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# 轉換PIL圖像為numpy數組
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numpy_img = np.array(image, dtype=np.uint8)
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# 創建均質濾波核（所有元素都是1/(kernel_size*kernel_size)）
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kernel = np.ones((kernel_size, kernel_size), np.float32) / (kernel_size * kernel_size)
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# 對每個通道應用均質濾波
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result = np.zeros_like(numpy_img)
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for i in range(3): # 對RGB三個通道分別處理
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result[:, :, i] = cv2.filter2D(numpy_img[:, :, i], -1, kernel)
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# 確保像素值在有效範圍內
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result = np.clip(result, 0, 255).astype(np.uint8)
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# 轉回PIL圖像
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return Image.fromarray(result) |