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279 lines
11 KiB
Python
279 lines
11 KiB
Python
"""
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Copyright (c) 2009-2010 Arizona Board of Regents. All Rights Reserved.
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Contact: Lina Karam (karam@asu.edu) and Niranjan Narvekar (nnarveka@asu.edu)
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Image, Video, and Usabilty (IVU) Lab, http://ivulab.asu.edu , Arizona State University
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This copyright statement may not be removed from any file containing it or from modifications to these files.
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This copyright notice must also be included in any file or product that is derived from the source files.
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Redistribution and use of this code in source and binary forms, with or without modification, are permitted provided that the
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following conditions are met:
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- Redistribution's of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
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- Redistribution's in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer
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in the documentation and/or other materials provided with the distribution.
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- The Image, Video, and Usability Laboratory (IVU Lab, http://ivulab.asu.edu) is acknowledged in any publication that
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reports research results using this code, copies of this code, or modifications of this code.
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The code and our papers are to be cited in the bibliography as:
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N. D. Narvekar and L. J. Karam, "CPBD Sharpness Metric Software", http://ivulab.asu.edu/Quality/CPBD
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N. D. Narvekar and L. J. Karam, "A No-Reference Image Blur Metric Based on the Cumulative
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Probability of Blur Detection (CPBD)," accepted and to appear in the IEEE Transactions on Image Processing, 2011.
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N. D. Narvekar and L. J. Karam, "An Improved No-Reference Sharpness Metric Based on the Probability of Blur Detection," International Workshop on Video Processing and Quality Metrics for Consumer Electronics (VPQM), January 2010, http://www.vpqm.org (pdf)
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N. D. Narvekar and L. J. Karam, "A No Reference Perceptual Quality Metric based on Cumulative Probability of Blur Detection," First International Workshop on the Quality of Multimedia Experience (QoMEX), pp. 87-91, July 2009.
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DISCLAIMER:
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This software is provided by the copyright holders and contributors "as is" and any express or implied warranties, including, but not limited to, the implied warranties of merchantability and fitness for a particular purpose are disclaimed. In no event shall the Arizona Board of Regents, Arizona State University, IVU Lab members, authors or contributors be liable for any direct, indirect, incidental, special, exemplary, or consequential damages (including, but not limited to, procurement of substitute
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goods or services; loss of use, data, or profits; or business interruption) however caused and on any theory of liability, whether in contract, strict liability, or tort (including negligence or otherwise) arising in any way out of the use of this software, even if advised of the possibility of such damage.
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"""
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import numpy as np
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import cv2
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from math import atan2, pi
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def sobel(image):
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# type: (numpy.ndarray) -> numpy.ndarray
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"""
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Find edges using the Sobel approximation to the derivatives.
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Inspired by the [Octave implementation](https://sourceforge.net/p/octave/image/ci/default/tree/inst/edge.m#l196).
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"""
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from skimage.filters.edges import HSOBEL_WEIGHTS
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h1 = np.array(HSOBEL_WEIGHTS)
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h1 /= np.sum(abs(h1)) # normalize h1
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from scipy.ndimage import convolve
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strength2 = np.square(convolve(image, h1.T))
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# Note: https://sourceforge.net/p/octave/image/ci/default/tree/inst/edge.m#l59
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thresh2 = 2 * np.sqrt(np.mean(strength2))
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strength2[strength2 <= thresh2] = 0
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return _simple_thinning(strength2)
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def _simple_thinning(strength):
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# type: (numpy.ndarray) -> numpy.ndarray
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"""
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Perform a very simple thinning.
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Inspired by the [Octave implementation](https://sourceforge.net/p/octave/image/ci/default/tree/inst/edge.m#l512).
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"""
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num_rows, num_cols = strength.shape
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zero_column = np.zeros((num_rows, 1))
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zero_row = np.zeros((1, num_cols))
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x = (
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(strength > np.c_[zero_column, strength[:, :-1]]) &
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(strength > np.c_[strength[:, 1:], zero_column])
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)
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y = (
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(strength > np.r_[zero_row, strength[:-1, :]]) &
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(strength > np.r_[strength[1:, :], zero_row])
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)
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return x | y
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# threshold to characterize blocks as edge/non-edge blocks
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THRESHOLD = 0.002
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# fitting parameter
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BETA = 3.6
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# block size
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BLOCK_HEIGHT, BLOCK_WIDTH = (64, 64)
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# just noticeable widths based on the perceptual experiments
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WIDTH_JNB = np.concatenate([5*np.ones(51), 3*np.ones(205)])
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def compute(image):
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# type: (numpy.ndarray) -> float
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"""Compute the sharpness metric for the given data."""
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# convert the image to double for further processing
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image = image.astype(np.float64)
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# edge detection using canny and sobel canny edge detection is done to
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# classify the blocks as edge or non-edge blocks and sobel edge
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# detection is done for the purpose of edge width measurement.
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from skimage.feature import canny
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canny_edges = canny(image)
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sobel_edges = sobel(image)
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# edge width calculation
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marziliano_widths = marziliano_method(sobel_edges, image)
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# sharpness metric calculation
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return _calculate_sharpness_metric(image, canny_edges, marziliano_widths)
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def marziliano_method(edges, image):
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# type: (numpy.ndarray, numpy.ndarray) -> numpy.ndarray
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"""
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Calculate the widths of the given edges.
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:return: A matrix with the same dimensions as the given image with 0's at
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non-edge locations and edge-widths at the edge locations.
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"""
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# `edge_widths` consists of zero and non-zero values. A zero value
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# indicates that there is no edge at that position and a non-zero value
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# indicates that there is an edge at that position and the value itself
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# gives the edge width.
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edge_widths = np.zeros(image.shape)
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# find the gradient for the image
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gradient_y, gradient_x = np.gradient(image)
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# dimensions of the image
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img_height, img_width = image.shape
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# holds the angle information of the edges
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edge_angles = np.zeros(image.shape)
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# calculate the angle of the edges
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for row in range(img_height):
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for col in range(img_width):
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if gradient_x[row, col] != 0:
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edge_angles[row, col] = atan2(gradient_y[row, col], gradient_x[row, col]) * (180 / pi)
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elif gradient_x[row, col] == 0 and gradient_y[row, col] == 0:
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edge_angles[row,col] = 0
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elif gradient_x[row, col] == 0 and gradient_y[row, col] == pi/2:
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edge_angles[row, col] = 90
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if np.any(edge_angles):
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# quantize the angle
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quantized_angles = 45 * np.round(edge_angles / 45)
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for row in range(1, img_height - 1):
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for col in range(1, img_width - 1):
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if edges[row, col] == 1:
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# gradient angle = 180 or -180
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if quantized_angles[row, col] == 180 or quantized_angles[row, col] == -180:
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for margin in range(100 + 1):
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inner_border = (col - 1) - margin
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outer_border = (col - 2) - margin
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# outside image or intensity increasing from left to right
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if outer_border < 0 or (image[row, outer_border] - image[row, inner_border]) <= 0:
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break
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width_left = margin + 1
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for margin in range(100 + 1):
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inner_border = (col + 1) + margin
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outer_border = (col + 2) + margin
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# outside image or intensity increasing from left to right
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if outer_border >= img_width or (image[row, outer_border] - image[row, inner_border]) >= 0:
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break
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width_right = margin + 1
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edge_widths[row, col] = width_left + width_right
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# gradient angle = 0
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if quantized_angles[row, col] == 0:
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for margin in range(100 + 1):
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inner_border = (col - 1) - margin
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outer_border = (col - 2) - margin
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# outside image or intensity decreasing from left to right
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if outer_border < 0 or (image[row, outer_border] - image[row, inner_border]) >= 0:
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break
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width_left = margin + 1
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for margin in range(100 + 1):
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inner_border = (col + 1) + margin
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outer_border = (col + 2) + margin
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# outside image or intensity decreasing from left to right
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if outer_border >= img_width or (image[row, outer_border] - image[row, inner_border]) <= 0:
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break
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width_right = margin + 1
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edge_widths[row, col] = width_right + width_left
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return edge_widths
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def _calculate_sharpness_metric(image, edges, edge_widths):
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# type: (numpy.array, numpy.array, numpy.array) -> numpy.float64
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# get the size of image
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img_height, img_width = image.shape
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total_num_edges = 0
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hist_pblur = np.zeros(101)
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# maximum block indices
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num_blocks_vertically = int(img_height / BLOCK_HEIGHT)
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num_blocks_horizontally = int(img_width / BLOCK_WIDTH)
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# loop over the blocks
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for i in range(num_blocks_vertically):
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for j in range(num_blocks_horizontally):
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# get the row and col indices for the block pixel positions
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rows = slice(BLOCK_HEIGHT * i, BLOCK_HEIGHT * (i + 1))
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cols = slice(BLOCK_WIDTH * j, BLOCK_WIDTH * (j + 1))
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if is_edge_block(edges[rows, cols], THRESHOLD):
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block_widths = edge_widths[rows, cols]
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# rotate block to simulate column-major boolean indexing
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block_widths = np.rot90(np.flipud(block_widths), 3)
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block_widths = block_widths[block_widths != 0]
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block_contrast = get_block_contrast(image[rows, cols])
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block_jnb = WIDTH_JNB[block_contrast]
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# calculate the probability of blur detection at the edges
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# detected in the block
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prob_blur_detection = 1 - np.exp(-abs(block_widths/block_jnb) ** BETA)
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# update the statistics using the block information
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for probability in prob_blur_detection:
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bucket = int(round(probability * 100))
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hist_pblur[bucket] += 1
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total_num_edges += 1
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# normalize the pdf
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if total_num_edges > 0:
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hist_pblur = hist_pblur / total_num_edges
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# calculate the sharpness metric
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return np.sum(hist_pblur[:64])
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def is_edge_block(block, threshold):
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# type: (numpy.ndarray, float) -> bool
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"""Decide whether the given block is an edge block."""
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return np.count_nonzero(block) > (block.size * threshold)
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def get_block_contrast(block):
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# type: (numpy.ndarray) -> int
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return int(np.max(block) - np.min(block))
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def estimate_sharpness(image):
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if image.ndim == 3:
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if image.shape[2] > 1:
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image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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else:
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image = image[...,0]
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return compute(image)
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