【数字图像处理】python-opencv实现同态滤波

刺骨的言语ヽ痛彻心扉 2022-12-03 09:04 278阅读 0赞

# Homomorphic filter class
    class HomomorphicFilter:
        """Homomorphic filter implemented with diferents filters and an option to an external filter.
        
        High-frequency filters implemented:
            butterworth
            gaussian
        Attributes:
            a, b: Floats used on emphasis filter:
                H = a + b*H
            
            .
        """
    
        def __init__(self, a = 0.5, b = 1.5):
            self.a = float(a)
            self.b = float(b)
    
        # Filters
        def __butterworth_filter(self, I_shape, filter_params):
            P = I_shape[0]/2
            Q = I_shape[1]/2
            U, V = np.meshgrid(range(I_shape[0]), range(I_shape[1]), sparse=False, indexing='ij')
            Duv = (((U-P)**2+(V-Q)**2)).astype(float)
            H = 1/(1+(Duv/filter_params[0]**2)**filter_params[1])
            return (1 - H)
    
        def __gaussian_filter(self, I_shape, filter_params):
            P = I_shape[0]/2
            Q = I_shape[1]/2
            H = np.zeros(I_shape)
            U, V = np.meshgrid(range(I_shape[0]), range(I_shape[1]), sparse=False, indexing='ij')
            Duv = (((U-P)**2+(V-Q)**2)).astype(float)
            H = np.exp((-Duv/(2*(filter_params[0])**2)))
            return (1 - H)
    
        # Methods
        def __apply_filter(self, I, H):
            H = np.fft.fftshift(H)
            I_filtered = (self.a + self.b*H)*I
            return I_filtered
    
        def filter(self, I, filter_params, filter='butterworth', H = None):
            """
            Method to apply homormophic filter on an image
            Attributes:
                I: Single channel image
                filter_params: Parameters to be used on filters:
                    butterworth:
                        filter_params[0]: Cutoff frequency 
                        filter_params[1]: Order of filter
                    gaussian:
                        filter_params[0]: Cutoff frequency
                filter: Choose of the filter, options:
                    butterworth
                    gaussian
                    external
                H: Used to pass external filter
            """
    
            #  Validating image
            if len(I.shape) is not 2:
                raise Exception('Improper image')
    
            # Take the image to log domain and then to frequency domain 
            I_log = np.log1p(np.array(I, dtype="float"))
            I_fft = np.fft.fft2(I_log)
    
            # Filters
            if filter=='butterworth':
                H = self.__butterworth_filter(I_shape = I_fft.shape, filter_params = filter_params)
            elif filter=='gaussian':
                H = self.__gaussian_filter(I_shape = I_fft.shape, filter_params = filter_params)
            elif filter=='external':
                print('external')
                if len(H.shape) is not 2:
                    raise Exception('Invalid external filter')
            else:
                raise Exception('Selected filter not implemented')
            
            # Apply filter on frequency domain then take the image back to spatial domain
            I_fft_filt = self.__apply_filter(I = I_fft, H = H)
            I_filt = np.fft.ifft2(I_fft_filt)
            I = np.exp(np.real(I_filt))-1
            return np.uint8(I)
    # End of class HomomorphicFilter

img = cv2.imread('a1.jpg',0)
    homo_filter = HomomorphicFilter(a = 0.75, b = 1.25) # a = 0.75, b = 1.25
    img_filtered = homo_filter.filter(I=img, filter_params=[30,2])
    cv2.imwrite('./output/a1_1.jpg', img_filtered)