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import cv2
import numpy as np
class StripeRemover:
def __init__(self):
pass
def fourier_method(self, image):
"""傅里叶变换去除条纹"""
# 转换为灰度图像
if len(image.shape) > 2:
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
else:
gray = image.copy()
# 傅里叶变换
dft = cv2.dft(np.float32(gray), flags=cv2.DFT_COMPLEX_OUTPUT)
dft_shift = np.fft.fftshift(dft)
# 创建掩模
rows, cols = gray.shape
crow, ccol = rows // 2, cols // 2
mask = np.ones((rows, cols, 2), np.uint8)
mask[crow-5:crow+5, :] = 0 # 调整带宽以匹配条纹频率
# 应用掩模和逆变换
fshift = dft_shift * mask
f_ishift = np.fft.ifftshift(fshift)
img_back = cv2.idft(f_ishift)
img_back = cv2.magnitude(img_back[:,:,0], img_back[:,:,1])
# 归一化处理
img_back = cv2.normalize(img_back, None, 0, 255, cv2.NORM_MINMAX)
return np.uint8(img_back)
def morphological_method(self, image):
"""形态学操作去除条纹"""
# Convert to grayscale if needed
if len(image.shape) > 2:
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
else:
gray = image.copy()
# Create horizontal kernel (adjust size if needed)
horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (25,1))
# Detect lines using morphological operations
detected_lines = cv2.morphologyEx(gray, cv2.MORPH_OPEN, horizontal_kernel)
# Remove detected lines and normalize
result = cv2.subtract(gray, detected_lines)
# Normalize and enhance contrast
result = cv2.normalize(result, None, 0, 255, cv2.NORM_MINMAX).astype(np.uint8)
# Convert back to BGR for display
return cv2.cvtColor(result, cv2.COLOR_GRAY2BGR)
def adaptive_threshold_method(self, image):
"""自适应阈值处理"""
if len(image.shape) > 2:
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
else:
gray = image.copy()
# 自适应阈值处理
thresh = cv2.adaptiveThreshold(gray, 255,
cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
cv2.THRESH_BINARY, 11, 2)
# 中值滤波去噪
result = cv2.medianBlur(thresh, 3)
return result
def enhance_image(self, image):
"""图像增强处理"""
# CLAHE对比度增强
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
enhanced = clahe.apply(image)
# 锐化处理
kernel = np.array([[-1,-1,-1],
[-1, 9,-1],
[-1,-1,-1]])
sharpened = cv2.filter2D(enhanced, -1, kernel)
return sharpened
def adaptive_enhance(self, image):
"""自适应阈值 + 图像增强"""
thresh_result = self.adaptive_threshold_method(image)
return self.enhance_image(thresh_result)
def fourier_enhance(self, image):
"""傅里叶变换 + 图像增强"""
fourier_result = self.fourier_method(image)
return self.enhance_image(fourier_result)
def morphological_enhance(self, image):
"""形态学操作 + 图像增强"""
morph_result = self.morphological_method(image)
# Convert BGR to gray for enhance_image
gray = cv2.cvtColor(morph_result, cv2.COLOR_BGR2GRAY)
enhanced = self.enhance_image(gray)
return cv2.cvtColor(enhanced, cv2.COLOR_GRAY2BGR)
def adaptive_fourier(self, image):
"""自适应阈值 + 傅里叶变换"""
thresh_result = self.adaptive_threshold_method(image)
return self.fourier_method(thresh_result)
def morphological_adaptive(self, image):
"""形态学操作 + 自适应阈值"""
morph_result = self.morphological_method(image)
gray = cv2.cvtColor(morph_result, cv2.COLOR_BGR2GRAY)
return self.adaptive_threshold_method(gray)
def fourier_morphological(self, image):
"""傅里叶变换 + 形态学操作"""
fourier_result = self.fourier_method(image)
return self.morphological_method(fourier_result) |