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BlurDetector.py
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BlurDetector.py
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'''
@inproceedings{golestaneh2017spatially,
title={Spatially-Varying Blur Detection Based on Multiscale Fused and Sorted Transform Coefficients of Gradient Magnitudes},
author={Golestaneh, S Alireza and Karam, Lina J},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
year={2017}
}
'''
import cv2
import numpy as np
import os
from skimage.filters.rank import entropy
from skimage.morphology import square
import copy
import time
class BlurDetector(object):
def __init__(self, downsampling_factor=4, num_scales=4, scale_start=3, entropy_filt_kernel_sze=7, sigma_s_RF_filter=15, sigma_r_RF_filter=0.25, num_iterations_RF_filter=3, show_progress = True):
self.downsampling_factor = downsampling_factor
self.num_scales = num_scales
self.scale_start = scale_start
self.entropy_filt_kernel_sze = entropy_filt_kernel_sze
self.sigma_s_RF_filter = sigma_s_RF_filter
self.sigma_r_RF_filter = sigma_r_RF_filter
self.num_iterations_RF_filter = num_iterations_RF_filter
self.scales = self.createScalePyramid()
self.__freqBands = []
self.__dct_matrices = []
self.freq_index = []
self.show_progress = show_progress
def disp_progress(self, i, rows, old_progress):
progress_dict = {10:'[| ] 10%',
20:'[| | ] 20%',
30:'[| | | ] 30%',
40:'[| | | | ] 40%',
50:'[| | | | | ] 50%',
60:'[| | | | | | ] 60%',
70:'[| | | | | | | ] 70%',
80:'[| | | | | | | | ] 80%',
90:'[| | | | | | | | | ] 90%',
100:'[| | | | | | | | | |] 100%'}
i_done = i / rows * 100
p_done = round(i_done / 10) * 10
if(p_done != old_progress):
os.system('cls' if os.name == 'nt' else 'clear')
print(progress_dict[p_done])
old_progress = p_done
return(p_done)
def createScalePyramid(self):
scales = []
for i in range(self.num_scales):
scales.append((2**(self.scale_start + i)) - 1) # Scales would be 7, 15, 31, 63 ...
return(scales)
def computeImageGradientMagnitude(self, img):
__sobelx = cv2.Sobel(img, cv2.CV_64F, 1, 0, borderType=cv2.BORDER_REFLECT) # Find x and y gradients
__sobely = cv2.Sobel(img, cv2.CV_64F, 0, 1, borderType=cv2.BORDER_REFLECT)
# Find gradient magnitude
__magnitude = np.sqrt(__sobelx ** 2.0 + __sobely ** 2.0)
return(__magnitude)
def __computeFrequencyBands(self):
for current_scale in self.scales:
matrixInds = np.zeros((current_scale, current_scale))
for i in range(current_scale):
matrixInds[0 : max(0, int(((current_scale-1)/2) - i +1)), i] = 1
for i in range(current_scale):
if (current_scale-((current_scale-1)/2) - i) <= 0:
matrixInds[0:current_scale - i - 1, i] = 2
else:
matrixInds[int(current_scale - ((current_scale - 1) / 2) - i - 1): int(current_scale - i - 1), i]=2;
matrixInds[0, 0] = 3
self.__freqBands.append(matrixInds)
def __dctmtx(self, n):
[mesh_cols, mesh_rows] = np.meshgrid(np.linspace(0, n-1, n), np.linspace(0, n-1, n))
dct_matrix = np.sqrt(2/n) * np.cos(np.pi * np.multiply((2 * mesh_cols + 1), mesh_rows) / (2*n));
dct_matrix[0, :] = dct_matrix[0, :] / np.sqrt(2)
return(dct_matrix)
def __createDCT_Matrices(self):
if(len(self.__dct_matrices) > 0):
raise TypeError("dct matrices are already defined. Redefinition is not allowed.")
for curr_scale in self.scales:
dct_matrix = self.__dctmtx(curr_scale)
self.__dct_matrices.append(dct_matrix)
def __getDCTCoefficients(self, img_blk, ind):
rows, cols = np.shape(img_blk)
# D = self.__dctmtx(rows)
D = self.__dct_matrices[ind]
dct_coeff = np.matmul(np.matmul(D, img_blk), np.transpose(D))
return(dct_coeff)
def entropyFilt(self, img):
return(entropy(img, square(self.entropy_filt_kernel_sze)))
def computeScore(self, weighted_local_entropy, T_max):
# normalize weighted T max matrix
min_val = weighted_local_entropy.min()
weighted_T_Max = weighted_local_entropy - min_val
max_val = weighted_local_entropy.max()
weighted_T_Max = weighted_local_entropy / max_val
score = np.median(weighted_local_entropy)
return(score)
def TransformedDomainRecursiveFilter_Horizontal(self, I, D, sigma):
# Feedback Coefficient (Appendix of the paper)
a = np.exp(-np.sqrt(2) / sigma)
F = copy.deepcopy(I)
V = a ** D
rows, cols = np.shape(I)
# Left --> Right Filter
for i in range(1, cols):
F[:, i] = F[:, i] + np.multiply(V[:, i], (F[:, i-1] - F[:, i]))
# Right --> Left Filter
for i in range(cols-2, 1, -1):
F[:, i] = F[:, i] + np.multiply(V[:, i+1], (F[:, i + 1] - F[:, i]))
return(F)
def RF(self, img, joint_img):
if(len(joint_img) == 0):
joint_img = img
joint_img = joint_img.astype('float64')
joint_img = joint_img / 255
if(len(np.shape(joint_img)) == 2):
cols, rows = np.shape(joint_img)
channels = 1
elif(len(np.shape(joint_img)) == 3):
cols, rows, channels = np.shape(joint_img)
# Estimate horizontal and vertical partial derivatives using finite differences.
dIcdx = np.diff(joint_img, n=1, axis=1)
dIcdy = np.diff(joint_img, n=1, axis=0)
dIdx = np.zeros((cols, rows));
dIdy = np.zeros((cols, rows));
# Compute the l1 - norm distance of neighbor pixels.
dIdx[:, 1::] = abs(dIcdx)
dIdy[1::, :] = abs(dIcdy)
dHdx = (1 + self.sigma_s_RF_filter / self.sigma_r_RF_filter * dIdx)
dVdy = (1 + self.sigma_s_RF_filter / self.sigma_r_RF_filter * dIdy)
dVdy = np.transpose(dVdy)
N = self.num_iterations_RF_filter
F = copy.deepcopy(img)
for i in range(self.num_iterations_RF_filter):
# Compute the sigma value for this iteration (Equation 14 of our paper).
sigma_H_i = self.sigma_s_RF_filter * np.sqrt(3) * 2 ** (N - (i + 1)) / np.sqrt(4 ** N - 1)
F = self.TransformedDomainRecursiveFilter_Horizontal(F, dHdx, sigma_H_i)
F = np.transpose(F)
F = self.TransformedDomainRecursiveFilter_Horizontal(F, dVdy, sigma_H_i)
F = np.transpose(F)
return(F)
def detectBlur(self, img):
ori_rows, ori_cols = np.shape(img)
# perform initial gausssian smoothing
InputImageGaus = cv2.GaussianBlur(img, (3, 3), sigmaX=0.5, sigmaY=0.5)
__gradient_image = self.computeImageGradientMagnitude(InputImageGaus)
total_num_layers = 1 + sum(self.scales)
# create all dct_matrices beforehand to save computation time
self.__createDCT_Matrices()
# Create Frequency Labels at all the scalesv
self.__computeFrequencyBands()
# Compute the indices of the high frequency content inside each frequency band
for i in range(self.num_scales):
curr_freq_band = self.__freqBands[i]
self.freq_index.append(np.where(curr_freq_band == 0))
__padded_image = np.pad(__gradient_image, int(np.floor(max(self.scales)/2)), mode='constant')
rows, cols = np.shape(__padded_image)
L = []
total_num_points = len([i for i in range(int(max(self.scales)/2), rows - int(max(self.scales)/2), self.downsampling_factor)]) * len([j for j in range(int(max(self.scales) / 2), cols - int(max(self.scales) / 2), self.downsampling_factor)])
L = np.zeros((total_num_points, total_num_layers))
iter = 0
n = 0
old_progress = 0
for i in range(int(max(self.scales)/2), rows - int(max(self.scales)/2), self.downsampling_factor):
if(self.show_progress):
old_progress = self.disp_progress(i, rows, old_progress)
m = 0
n += 1
for j in range(int(max(self.scales) / 2), cols - int(max(self.scales) / 2), self.downsampling_factor):
m += 1
high_freq_components = []
for ind, curr_scale in enumerate(self.scales):
Patch = __padded_image[i-int(curr_scale/2) : i+int(curr_scale/2) + 1, j-int(curr_scale/2) : j+int(curr_scale/2) + 1]
dct_coefficients = np.abs(self.__getDCTCoefficients(Patch, ind))
# store all high frequency components
high_freq_components.append(dct_coefficients[self.freq_index[ind]])
# Find the first `total_num_layers` smallest values in all the high frequency components - we must not sort the entire array since that is very inefficient
high_freq_components = np.hstack(high_freq_components)
result = np.argpartition(high_freq_components, total_num_layers)
L[iter, :] = high_freq_components[result[:total_num_layers]]
iter += 1
L = np.array(L)
# normalize the L matrix
for i in range(total_num_layers):
max_val = max(L[:, i])
L[:, i] = L[:, i] / max_val
# perform max pooling on the normalized frequencies
ind1d = 0
T_max = np.zeros((n, m))
max_val = 0
min_val = 99999
for i in range(n):
for j in range(m):
T_max[i][j] = max(L[ind1d, :])
max_val = max(max_val, T_max[i][j])
min_val = min(min_val, T_max[i][j])
ind1d += 1
# Final Map and Post Processing
local_entropy = self.entropyFilt(T_max)
weighted_local_entropy = np.multiply(local_entropy, T_max)
score = self.computeScore(weighted_local_entropy, T_max)
rows, cols = np.shape(weighted_local_entropy)
# resize the input image to match the size of local_entropy matrix
resized_input_image = cv2.resize(InputImageGaus, (cols, rows))
aSmooth = cv2.GaussianBlur(resized_input_image, (3, 3), sigmaX=1, sigmaY=1)
final_map = self.RF(weighted_local_entropy, aSmooth)
# resize the map to the original resolution
final_map = cv2.resize(final_map, (ori_cols, ori_rows))
# normalize the map
final_map = final_map / np.max(final_map)
return(final_map)