class FrequencyWidget(ToolWidget):
def __init__(self, image, parent=None):
super(FrequencyWidget, self).__init__(parent)
self.split_spin = QSpinBox()
self.split_spin.setRange(0, 100)
self.split_spin.setValue(15)
self.split_spin.setSuffix(self.tr(" %"))
self.smooth_spin = QSpinBox()
self.smooth_spin.setRange(0, 100)
self.smooth_spin.setValue(25)
self.smooth_spin.setSuffix(self.tr(" %"))
self.smooth_spin.setSpecialValueText(self.tr("Off"))
self.thr_spin = QSpinBox()
self.thr_spin.setRange(0, 100)
self.thr_spin.setValue(0)
self.thr_spin.setSuffix(self.tr(" %"))
self.thr_spin.setSpecialValueText(self.tr("Off"))
self.zero_label = QLabel()
modify_font(self.zero_label, italic=True)
self.filter_spin = QSpinBox()
self.filter_spin.setRange(0, 15)
self.filter_spin.setValue(0)
self.filter_spin.setSuffix(self.tr(" px"))
self.filter_spin.setSpecialValueText(self.tr("Off"))
self.split_spin.valueChanged.connect(self.process)
self.smooth_spin.valueChanged.connect(self.process)
self.thr_spin.valueChanged.connect(self.process)
self.filter_spin.valueChanged.connect(self.postprocess)
self.image = image
gray = cv.cvtColor(self.image, cv.COLOR_BGR2GRAY)
rows, cols = gray.shape
height = cv.getOptimalDFTSize(rows)
width = cv.getOptimalDFTSize(cols)
padded = cv.copyMakeBorder(gray, 0, height - rows, 0, width - cols, cv.BORDER_CONSTANT)
self.dft = np.fft.fftshift(cv.dft(padded.astype(np.float32), flags=cv.DFT_COMPLEX_OUTPUT))
self.magnitude0, self.phase0 = cv.cartToPolar(self.dft[:, :, 0], self.dft[:, :, 1])
self.magnitude0 = cv.normalize(cv.log(self.magnitude0), None, 0, 255, cv.NORM_MINMAX)
self.phase0 = cv.normalize(self.phase0, None, 0, 255, cv.NORM_MINMAX)
self.magnitude = self.phase = None
self.low_viewer = ImageViewer(self.image, self.image, self.tr("Low frequency"), export=True)
self.high_viewer = ImageViewer(self.image, self.image, self.tr("High frequency"), export=True)
self.mag_viewer = ImageViewer(self.image, None, self.tr("DFT Magnitude"), export=True)
self.phase_viewer = ImageViewer(self.image, None, self.tr("DFT Phase"), export=True)
self.process()
self.low_viewer.viewChanged.connect(self.high_viewer.changeView)
self.high_viewer.viewChanged.connect(self.low_viewer.changeView)
self.mag_viewer.viewChanged.connect(self.phase_viewer.changeView)
self.phase_viewer.viewChanged.connect(self.mag_viewer.changeView)
top_layout = QHBoxLayout()
top_layout.addWidget(QLabel(self.tr("Separation:")))
top_layout.addWidget(self.split_spin)
top_layout.addWidget(QLabel(self.tr("Smooth:")))
top_layout.addWidget(self.smooth_spin)
top_layout.addWidget(QLabel(self.tr("Threshold:")))
top_layout.addWidget(self.thr_spin)
top_layout.addWidget(QLabel(self.tr("Filter:")))
top_layout.addWidget(self.filter_spin)
top_layout.addWidget(self.zero_label)
top_layout.addStretch()
center_layout = QGridLayout()
center_layout.addWidget(self.low_viewer, 0, 0)
center_layout.addWidget(self.high_viewer, 0, 1)
center_layout.addWidget(self.mag_viewer, 1, 0)
center_layout.addWidget(self.phase_viewer, 1, 1)
main_layout = QVBoxLayout()
main_layout.addLayout(top_layout)
main_layout.addLayout(center_layout)
self.setLayout(main_layout)
def process(self):
start = time()
rows, cols, _ = self.dft.shape
mask = np.zeros((rows, cols), np.float32)
half = np.sqrt(rows ** 2 + cols ** 2) / 2
radius = int(half * self.split_spin.value() / 100)
mask = cv.circle(mask, (cols // 2, rows // 2), radius, 1, cv.FILLED)
kernel = 2 * int(half * self.smooth_spin.value() / 100) + 1
mask = cv.GaussianBlur(mask, (kernel, kernel), 0)
mask /= np.max(mask)
threshold = int(self.thr_spin.value() / 100 * 255)
if threshold > 0:
mask[self.magnitude0 < threshold] = 0
zeros = (mask.size - np.count_nonzero(mask)) / mask.size * 100
else:
zeros = 0
self.zero_label.setText(self.tr("(zeroed coefficients = {:.2f}%)").format(zeros))
mask2 = np.repeat(mask[:, :, np.newaxis], 2, axis=2)
rows0, cols0, _ = self.image.shape
low = cv.idft(np.fft.ifftshift(self.dft * mask2), flags=cv.DFT_SCALE)
low = norm_mat(cv.magnitude(low[:, :, 0], low[:, :, 1])[:rows0, :cols0], to_bgr=True)
self.low_viewer.update_processed(low)
high = cv.idft(np.fft.ifftshift(self.dft * (1 - mask2)), flags=cv.DFT_SCALE)
high = norm_mat(cv.magnitude(high[:, :, 0], high[:, :, 1]), to_bgr=True)
self.high_viewer.update_processed(np.copy(high[: self.image.shape[0], : self.image.shape[1]]))
self.magnitude = (self.magnitude0 * mask).astype(np.uint8)
self.phase = (self.phase0 * mask).astype(np.uint8)
self.postprocess()
self.info_message.emit(self.tr(f"Frequency Split = {elapsed_time(start)}"))
def postprocess(self):
kernel = 2 * self.filter_spin.value() + 1
if kernel >= 3:
magnitude = cv.GaussianBlur(self.magnitude, (kernel, kernel), 0)
phase = cv.GaussianBlur(self.phase, (kernel, kernel), 0)
# phase = cv.medianBlur(self.phase, kernel)
else:
magnitude = self.magnitude
phase = self.phase
self.mag_viewer.update_original(cv.cvtColor(magnitude, cv.COLOR_GRAY2BGR))
self.phase_viewer.update_original(cv.cvtColor(phase, cv.COLOR_GRAY2BGR))
class FrequencyWidget(ToolWidget):
def __init__(self, image, parent=None):
super(FrequencyWidget, self).__init__(parent)
self.ampl_radio = QRadioButton(self.tr('Amplitude'))
self.ampl_radio.setChecked(True)
self.phase_radio = QRadioButton(self.tr('Phase'))
self.dct_radio = QRadioButton(self.tr('DCT Map'))
self.last_radio = self.ampl_radio
self.thr_spin = QSpinBox()
self.thr_spin.setRange(0, 255)
self.thr_spin.setSpecialValueText(self.tr('Off'))
self.ratio_label = QLabel()
self.filter_check = QCheckBox(self.tr('Filter'))
self.ampl_radio.clicked.connect(self.process)
self.phase_radio.clicked.connect(self.process)
self.dct_radio.clicked.connect(self.process)
self.thr_spin.valueChanged.connect(self.process)
self.filter_check.stateChanged.connect(self.process)
gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
rows, cols = gray.shape
height = cv.getOptimalDFTSize(rows)
width = cv.getOptimalDFTSize(cols)
padded = cv.copyMakeBorder(gray, 0, height - rows, 0, width - cols,
cv.BORDER_CONSTANT).astype(np.float32)
planes = cv.merge([padded, np.zeros_like(padded)])
dft = cv.split(np.fft.fftshift(cv.dft(planes)))
mag, phase = cv.cartToPolar(dft[0], dft[1])
dct = cv.dct(padded)
self.result = [
normalize_mat(img)
for img in [cv.log(mag), phase, cv.log(dct)]
]
self.image = image
self.viewer = ImageViewer(self.image, None)
self.process()
top_layout = QHBoxLayout()
top_layout.addWidget(QLabel(self.tr('Coefficients:')))
top_layout.addWidget(self.ampl_radio)
top_layout.addWidget(self.phase_radio)
top_layout.addWidget(self.dct_radio)
top_layout.addWidget(self.filter_check)
top_layout.addStretch()
top_layout.addWidget(QLabel(self.tr('Threshold:')))
top_layout.addWidget(self.thr_spin)
top_layout.addWidget(self.ratio_label)
main_layout = QVBoxLayout()
main_layout.addLayout(top_layout)
main_layout.addWidget(self.viewer)
self.setLayout(main_layout)
def process(self):
if self.ampl_radio.isChecked():
output = self.result[0]
self.last_radio = self.ampl_radio
elif self.phase_radio.isChecked():
output = self.result[1]
self.last_radio = self.phase_radio
elif self.dct_radio.isChecked():
output = self.result[2]
self.last_radio = self.dct_radio
else:
self.last_radio.setChecked(True)
return
if self.filter_check.isChecked():
output = cv.medianBlur(output, 3)
thr = self.thr_spin.value()
if thr > 0:
_, output = cv.threshold(output, thr, 0, cv.THRESH_TOZERO)
zeros = (1.0 - cv.countNonZero(output) / output.size) * 100
else:
zeros = 0
self.ratio_label.setText('(masked = {:.1f}%)'.format(zeros))
self.viewer.update_original(cv.cvtColor(output, cv.COLOR_GRAY2BGR))
class StereoWidget(ToolWidget):
def __init__(self, image, parent=None):
super(StereoWidget, self).__init__(parent)
gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
small = cv.resize(gray, None, None, 1, 0.5)
start = 10
end = small.shape[1] // 3
diff = np.fromiter([
cv.mean(cv.absdiff(small[:, i:], small[:, :-i]))[0]
for i in range(start, end)
], np.float32)
_, maximum, _, argmax = cv.minMaxLoc(np.ediff1d(diff))
if maximum < 2:
error_label = QLabel(self.tr("Unable to detect stereogram!"))
modify_font(error_label, bold=True)
error_label.setStyleSheet("color: #FF0000")
error_label.setAlignment(Qt.AlignCenter)
main_layout = QVBoxLayout()
main_layout.addWidget(error_label)
self.setLayout(main_layout)
return
offset = argmax[1] + start
a = image[:, offset:]
b = image[:, :-offset]
self.pattern = norm_img(cv.absdiff(a, b))
temp = cv.cvtColor(self.pattern, cv.COLOR_BGR2GRAY)
thr, _ = cv.threshold(temp, 0, 255, cv.THRESH_TRIANGLE)
self.silhouette = cv.medianBlur(
gray_to_bgr(cv.threshold(temp, thr, 255, cv.THRESH_BINARY)[1]), 3)
a = cv.cvtColor(a, cv.COLOR_BGR2GRAY)
b = cv.cvtColor(b, cv.COLOR_BGR2GRAY)
flow = cv.calcOpticalFlowFarneback(a, b, None, 0.5, 5, 15, 5, 5, 1.2,
cv.OPTFLOW_FARNEBACK_GAUSSIAN)[:, :,
0]
self.depth = gray_to_bgr(norm_mat(flow))
flow = np.repeat(cv.normalize(flow, None, 0, 1,
cv.NORM_MINMAX)[:, :, np.newaxis],
3,
axis=2)
self.shaded = cv.normalize(
self.pattern.astype(np.float32) * flow, None, 0, 255,
cv.NORM_MINMAX).astype(np.uint8)
self.viewer = ImageViewer(self.pattern, None, export=True)
self.pattern_radio = QRadioButton(self.tr("Pattern"))
self.pattern_radio.setChecked(True)
self.pattern_radio.setToolTip(
self.tr("Difference between raw and aligned image"))
self.silhouette_radio = QRadioButton(self.tr("Silhouette"))
self.silhouette_radio.setToolTip(
self.tr("Apply threshold to discovered pattern"))
self.depth_radio = QRadioButton(self.tr("Depth"))
self.depth_radio.setToolTip(
self.tr("Estimate 3D depth using optical flow"))
self.shaded_radio = QRadioButton(self.tr("Shaded"))
self.shaded_radio.setToolTip(
self.tr("Combine pattern and depth information"))
self.silhouette_radio.clicked.connect(self.process)
self.pattern_radio.clicked.connect(self.process)
self.depth_radio.clicked.connect(self.process)
self.shaded_radio.clicked.connect(self.process)
top_layout = QHBoxLayout()
top_layout.addWidget(QLabel(self.tr("Mode:")))
top_layout.addWidget(self.pattern_radio)
top_layout.addWidget(self.silhouette_radio)
top_layout.addWidget(self.depth_radio)
top_layout.addWidget(self.shaded_radio)
top_layout.addStretch()
main_layout = QVBoxLayout()
main_layout.addLayout(top_layout)
main_layout.addWidget(self.viewer)
self.setLayout(main_layout)
def process(self):
if self.pattern_radio.isChecked():
output = self.pattern
elif self.silhouette_radio.isChecked():
output = self.silhouette
elif self.depth_radio.isChecked():
output = self.depth
elif self.shaded_radio.isChecked():
output = self.shaded
else:
return
self.viewer.update_original(output)
class ComparisonWidget(ToolWidget):
def __init__(self, filename, image, parent=None):
super(ComparisonWidget, self).__init__(parent)
load_button = QPushButton(self.tr("Load reference image..."))
self.comp_label = QLabel(self.tr("Comparison:"))
self.normal_radio = QRadioButton(self.tr("Normal"))
self.normal_radio.setToolTip(self.tr("Show reference (raw pixels)"))
self.normal_radio.setChecked(True)
self.difference_radio = QRadioButton(self.tr("Difference"))
self.difference_radio.setToolTip(
self.tr("Show evidence/reference difference"))
self.ssim_radio = QRadioButton(self.tr("SSIM Map"))
self.ssim_radio.setToolTip(self.tr("Structure similarity quality map"))
self.butter_radio = QRadioButton(self.tr("Butteraugli"))
self.butter_radio.setToolTip(
self.tr("Butteraugli spatial changes heatmap"))
self.gray_check = QCheckBox(self.tr("Grayscale"))
self.gray_check.setToolTip(self.tr("Show desaturated output"))
self.equalize_check = QCheckBox(self.tr("Equalized"))
self.equalize_check.setToolTip(self.tr("Apply histogram equalization"))
self.last_radio = self.normal_radio
self.metric_button = QPushButton(self.tr("Compute metrics"))
self.metric_button.setToolTip(
self.tr("Image quality assessment metrics"))
self.evidence = image
self.reference = self.difference = self.ssim_map = self.butter_map = None
basename = os.path.basename(filename)
self.evidence_viewer = ImageViewer(self.evidence, None,
self.tr(f"Evidence: {basename}"))
self.reference_viewer = ImageViewer(np.full_like(self.evidence, 127),
None, self.tr("Reference"))
self.table_widget = QTableWidget(20, 3)
self.table_widget.setHorizontalHeaderLabels(
[self.tr("Metric"),
self.tr("Value"),
self.tr("Better")])
self.table_widget.setItem(0, 0, QTableWidgetItem(self.tr("RMSE")))
self.table_widget.setItem(
0, 2, QTableWidgetItem(QIcon("icons/low.svg"), "(0)"))
self.table_widget.item(0, 0).setToolTip(
self.
tr("Root Mean Square Error (RMSE) is commonly used to compare \n"
"the difference between the reference and evidence images \n"
"by directly computing the variation in pixel values. \n"
"The combined image is close to the reference image when \n"
"RMSE value is zero. RMSE is a good indicator of the spectral \n"
"quality of the reference image."))
self.table_widget.setItem(1, 0, QTableWidgetItem(self.tr("SAM")))
self.table_widget.setItem(
1, 2, QTableWidgetItem(QIcon("icons/low.svg"), "(0)"))
self.table_widget.item(1, 0).setToolTip(
self.
tr("It computes the spectral angle between the pixel, vector of the \n"
"evidence image and reference image. It is worked out in either \n"
"degrees or radians. It is performed on a pixel-by-pixel base. \n"
"A SAM equal to zero denotes the absence of spectral distortion."
))
self.table_widget.setItem(2, 0, QTableWidgetItem(self.tr("ERGAS")))
self.table_widget.setItem(
2, 2, QTableWidgetItem(QIcon("icons/low.svg"), "(0)"))
self.table_widget.item(2, 0).setToolTip(
self.
tr("It is used to compute the quality of reference image in terms \n"
"of normalized average error of each band of the reference image. \n"
"Increase in the value of ERGAS indicates distortion in the \n"
"reference image, lower value of ERGAS indicates that it is \n"
"similar to the reference image."))
self.table_widget.setItem(3, 0, QTableWidgetItem(self.tr("MB")))
self.table_widget.setItem(
3, 2, QTableWidgetItem(QIcon("icons/low.svg"), "(0)"))
self.table_widget.item(3, 0).setToolTip(
self.
tr("Mean Bias is the difference between the mean of the evidence \n"
"image and reference image. The ideal value is zero and indicates \n"
"that the evidence and reference images are similar. Mean value \n"
"refers to the grey level of pixels in an image."))
self.table_widget.setItem(4, 0, QTableWidgetItem(self.tr("PFE")))
self.table_widget.setItem(
4, 2, QTableWidgetItem(QIcon("icons/low.svg"), "(0)"))
self.table_widget.item(4, 0).setToolTip(
self.
tr("It computes the norm of the difference between the corresponding \n"
"pixels of the reference and fused image to the norm of the reference \n"
"image. When the calculated value is zero, it indicates that both the \n"
"reference and fused images are similar and value will be increased \n"
"when the merged image is not similar to the reference image."))
self.table_widget.setItem(5, 0, QTableWidgetItem(self.tr("PSNR")))
self.table_widget.setItem(
5, 2,
QTableWidgetItem(QIcon("icons/high.svg"), "(+" + "\u221e" + ")"))
self.table_widget.item(5, 0).setToolTip(
self.
tr("It is widely used metric it is computed by the number of gray levels \n"
"in the image divided by the corresponding pixels in the evidence and \n"
"the reference images. When the value is high, both images are similar."
))
# self.table_widget.setItem(6, 0, QTableWidgetItem(self.tr('PSNR-B')))
# self.table_widget.setItem(6, 2, QTableWidgetItem(QIcon('icons/high.svg'), '(+' + u'\u221e' + ')'))
# self.table_widget.item(6, 0).setToolTip(self.tr('PSNR with Blocking Effect Factor.'))
self.table_widget.setItem(6, 0, QTableWidgetItem(self.tr("SSIM")))
self.table_widget.setItem(
6, 2, QTableWidgetItem(QIcon("icons/high.svg"), "(1)"))
self.table_widget.item(6, 0).setToolTip(
self.
tr("SSIM is used to compare the local patterns of pixel intensities between \n"
" the reference and fused images. The range varies between -1 to 1. \n"
"The value 1 indicates the reference and fused images are similar."
))
self.table_widget.setItem(7, 0, QTableWidgetItem(self.tr("MS-SSIM")))
self.table_widget.setItem(
7, 2, QTableWidgetItem(QIcon("icons/high.svg"), "(1)"))
self.table_widget.item(7, 0).setToolTip(
self.tr("Multiscale version of SSIM."))
self.table_widget.setItem(8, 0, QTableWidgetItem(self.tr("RASE")))
self.table_widget.setItem(
8, 2, QTableWidgetItem(QIcon("icons/low.svg"), "(0)"))
self.table_widget.item(8, 0).setToolTip(
self.tr("Relative average spectral error"))
self.table_widget.setItem(9, 0, QTableWidgetItem(self.tr("SCC")))
self.table_widget.setItem(
9, 2, QTableWidgetItem(QIcon("icons/high.svg"), "(1)"))
self.table_widget.item(9, 0).setToolTip(
self.tr("Spatial Correlation Coefficient"))
self.table_widget.setItem(10, 0, QTableWidgetItem(self.tr("UQI")))
self.table_widget.setItem(
10, 2, QTableWidgetItem(QIcon("icons/high.svg"), "(1)"))
self.table_widget.item(10, 0).setToolTip(
self.tr("Universal Image Quality Index"))
self.table_widget.setItem(11, 0, QTableWidgetItem(self.tr("VIF-P")))
self.table_widget.setItem(
11, 2, QTableWidgetItem(QIcon("icons/high.svg"), "(1)"))
self.table_widget.item(11, 0).setToolTip(
self.tr("Pixel-based Visual Information Fidelity"))
self.table_widget.setItem(12, 0,
QTableWidgetItem(self.tr("SSIMulacra")))
self.table_widget.setItem(
12, 2, QTableWidgetItem(QIcon("icons/low.svg"), "(0)"))
self.table_widget.item(12, 0).setToolTip(
self.tr("Structural SIMilarity Unveiling Local "
"And Compression Related Artifacts"))
self.table_widget.setItem(13, 0,
QTableWidgetItem(self.tr("Butteraugli")))
self.table_widget.setItem(
13, 2, QTableWidgetItem(QIcon("icons/low.svg"), "(0)"))
self.table_widget.item(13, 0).setToolTip(
self.tr("Estimate psychovisual error"))
self.table_widget.setItem(14, 0,
QTableWidgetItem(self.tr("Correlation")))
self.table_widget.setItem(
14, 2, QTableWidgetItem(QIcon("icons/high.svg"), "(1)"))
self.table_widget.item(14,
0).setToolTip(self.tr("Histogram correlation"))
self.table_widget.setItem(15, 0,
QTableWidgetItem(self.tr("Chi-Square")))
self.table_widget.setItem(
15, 2, QTableWidgetItem(QIcon("icons/low.svg"), "(0)"))
self.table_widget.item(15,
0).setToolTip(self.tr("Histogram Chi-Square"))
self.table_widget.setItem(16, 0,
QTableWidgetItem(self.tr("Chi-Square 2")))
self.table_widget.setItem(
16, 2, QTableWidgetItem(QIcon("icons/low.svg"), "(0)"))
self.table_widget.item(16,
0).setToolTip(self.tr("Alternative Chi-Square"))
self.table_widget.setItem(17, 0,
QTableWidgetItem(self.tr("Intersection")))
self.table_widget.setItem(
17, 2,
QTableWidgetItem(QIcon("icons/high.svg"), "(+" + "\u221e" + ")"))
self.table_widget.item(17,
0).setToolTip(self.tr("Histogram intersection"))
self.table_widget.setItem(18, 0,
QTableWidgetItem(self.tr("Hellinger")))
self.table_widget.setItem(
18, 2, QTableWidgetItem(QIcon("icons/low.svg"), "(0)"))
self.table_widget.item(18, 0).setToolTip(
self.tr("Histogram Hellinger distance"))
self.table_widget.setItem(19, 0,
QTableWidgetItem(self.tr("Divergence")))
self.table_widget.setItem(
19, 2, QTableWidgetItem(QIcon("icons/low.svg"), "(0)"))
self.table_widget.item(19, 0).setToolTip(
self.tr("Kullback-Leibler divergence"))
for i in range(self.table_widget.rowCount()):
modify_font(self.table_widget.item(i, 0), bold=True)
self.table_widget.setSelectionMode(QAbstractItemView.SingleSelection)
self.table_widget.setEditTriggers(QAbstractItemView.NoEditTriggers)
self.table_widget.resizeColumnsToContents()
self.table_widget.setMaximumWidth(250)
self.table_widget.setAlternatingRowColors(True)
self.stopped = False
self.comp_label.setEnabled(False)
self.normal_radio.setEnabled(False)
self.difference_radio.setEnabled(False)
self.ssim_radio.setEnabled(False)
self.butter_radio.setEnabled(False)
self.gray_check.setEnabled(False)
self.equalize_check.setEnabled(False)
self.metric_button.setEnabled(False)
self.table_widget.setEnabled(False)
load_button.clicked.connect(self.load)
self.normal_radio.clicked.connect(self.change)
self.difference_radio.clicked.connect(self.change)
self.butter_radio.clicked.connect(self.change)
self.gray_check.stateChanged.connect(self.change)
self.equalize_check.stateChanged.connect(self.change)
self.ssim_radio.clicked.connect(self.change)
self.evidence_viewer.viewChanged.connect(
self.reference_viewer.changeView)
self.reference_viewer.viewChanged.connect(
self.evidence_viewer.changeView)
self.metric_button.clicked.connect(self.metrics)
top_layout = QHBoxLayout()
top_layout.addWidget(load_button)
top_layout.addStretch()
top_layout.addWidget(self.comp_label)
top_layout.addWidget(self.normal_radio)
top_layout.addWidget(self.difference_radio)
top_layout.addWidget(self.ssim_radio)
top_layout.addWidget(self.butter_radio)
top_layout.addWidget(self.gray_check)
top_layout.addWidget(self.equalize_check)
metric_layout = QVBoxLayout()
index_label = QLabel(self.tr("Image Quality Assessment"))
index_label.setAlignment(Qt.AlignCenter)
modify_font(index_label, bold=True)
metric_layout.addWidget(index_label)
metric_layout.addWidget(self.table_widget)
metric_layout.addWidget(self.metric_button)
center_layout = QHBoxLayout()
center_layout.addWidget(self.evidence_viewer)
center_layout.addWidget(self.reference_viewer)
center_layout.addLayout(metric_layout)
main_layout = QVBoxLayout()
main_layout.addLayout(top_layout)
main_layout.addLayout(center_layout)
self.setLayout(main_layout)
def load(self):
filename, basename, reference = load_image(self)
if filename is None:
return
if reference.shape != self.evidence.shape:
QMessageBox.critical(
self, self.tr("Error"),
self.tr("Evidence and reference must have the same size!"))
return
self.reference = reference
self.reference_viewer.set_title(self.tr(f"Reference: {basename}"))
self.difference = norm_mat(cv.absdiff(self.evidence, self.reference))
self.comp_label.setEnabled(True)
self.normal_radio.setEnabled(True)
self.difference_radio.setEnabled(True)
self.ssim_radio.setEnabled(False)
self.butter_radio.setEnabled(False)
self.gray_check.setEnabled(True)
self.equalize_check.setEnabled(True)
self.metric_button.setEnabled(True)
for i in range(self.table_widget.rowCount()):
self.table_widget.setItem(i, 1, QTableWidgetItem())
self.normal_radio.setChecked(True)
self.table_widget.setEnabled(False)
self.change()
def change(self):
if self.normal_radio.isChecked():
result = self.reference
self.gray_check.setEnabled(False)
self.equalize_check.setEnabled(False)
self.last_radio = self.normal_radio
elif self.difference_radio.isChecked():
result = self.difference
self.gray_check.setEnabled(True)
self.equalize_check.setEnabled(True)
self.last_radio = self.difference_radio
elif self.ssim_radio.isChecked():
result = self.ssim_map
self.gray_check.setEnabled(False)
self.equalize_check.setEnabled(True)
self.last_radio = self.ssim_radio
elif self.butter_radio.isChecked():
result = self.butter_map
self.gray_check.setEnabled(True)
self.equalize_check.setEnabled(False)
self.last_radio = self.butter_radio
else:
self.last_radio.setChecked(True)
return
if self.equalize_check.isChecked():
result = equalize_img(result)
if self.gray_check.isChecked():
result = desaturate(result)
self.reference_viewer.update_original(result)
def metrics(self):
progress = QProgressDialog(self.tr("Computing metrics..."),
self.tr("Cancel"), 1,
self.table_widget.rowCount(), self)
progress.canceled.connect(self.cancel)
progress.setWindowModality(Qt.WindowModal)
img1 = cv.cvtColor(self.evidence, cv.COLOR_BGR2GRAY)
img2 = cv.cvtColor(self.reference, cv.COLOR_BGR2GRAY)
x = img1.astype(np.float64)
y = img2.astype(np.float64)
rmse = self.rmse(x, y)
progress.setValue(1)
if self.stopped:
return
sam = sewar.sam(img1, img2)
progress.setValue(2)
if self.stopped:
return
ergas = sewar.ergas(img1, img2)
progress.setValue(3)
if self.stopped:
return
mb = self.mb(x, y)
progress.setValue(4)
if self.stopped:
return
pfe = self.pfe(x, y)
progress.setValue(5)
if self.stopped:
return
psnr = self.psnr(x, y)
progress.setValue(6)
if self.stopped:
return
try:
psnrb = sewar.psnrb(img1, img2)
except NameError:
# FIXME: C'\`e un bug in psnrb (https://github.com/andrewekhalel/sewar/issues/17)
psnrb = 0
progress.setValue(7)
if self.stopped:
return
ssim, self.ssim_map = self.ssim(x, y)
progress.setValue(8)
if self.stopped:
return
mssim = sewar.msssim(img1, img2).real
progress.setValue(9)
if self.stopped:
return
rase = sewar.rase(img1, img2)
progress.setValue(10)
if self.stopped:
return
scc = sewar.scc(img1, img2)
progress.setValue(11)
if self.stopped:
return
uqi = sewar.uqi(img1, img2)
progress.setValue(12)
if self.stopped:
return
vifp = sewar.vifp(img1, img2)
progress.setValue(13)
if self.stopped:
return
ssimul = self.ssimul(img1, img2)
progress.setValue(14)
if self.stopped:
return
butter, self.butter_map = self.butter(img1, img2)
progress.setValue(15)
if self.stopped:
return
sizes = [256, 256, 256]
ranges = [0, 256] * 3
channels = [0, 1, 2]
hist1 = cv.calcHist([self.evidence], channels, None, sizes, ranges)
hist2 = cv.calcHist([self.reference], channels, None, sizes, ranges)
correlation = cv.compareHist(hist1, hist2, cv.HISTCMP_CORREL)
progress.setValue(16)
if self.stopped:
return
chi_square = cv.compareHist(hist1, hist2, cv.HISTCMP_CHISQR)
progress.setValue(17)
if self.stopped:
return
chi_square2 = cv.compareHist(hist1, hist2, cv.HISTCMP_CHISQR_ALT)
progress.setValue(18)
if self.stopped:
return
intersection = cv.compareHist(hist1, hist2, cv.HISTCMP_INTERSECT)
progress.setValue(19)
if self.stopped:
return
hellinger = cv.compareHist(hist1, hist2, cv.HISTCMP_HELLINGER)
progress.setValue(20)
if self.stopped:
return
divergence = cv.compareHist(hist1, hist2, cv.HISTCMP_KL_DIV)
progress.setValue(21)
self.table_widget.setItem(0, 1, QTableWidgetItem(f"{rmse:.2f}"))
self.table_widget.setItem(1, 1, QTableWidgetItem(f"{sam:.4f}"))
self.table_widget.setItem(2, 1, QTableWidgetItem(f"{ergas:.2f}"))
self.table_widget.setItem(3, 1, QTableWidgetItem(f"{mb:.4f}"))
self.table_widget.setItem(4, 1, QTableWidgetItem(f"{pfe:.2f}"))
if psnr > 0:
self.table_widget.setItem(5, 1, QTableWidgetItem(f"{psnr:.2f} dB"))
else:
self.table_widget.setItem(5, 1,
QTableWidgetItem("+" + "\u221e" + " dB"))
# self.table_widget.setItem(6, 1, QTableWidgetItem('{:.2f}'.format(psnrb)))
self.table_widget.setItem(6, 1, QTableWidgetItem(f"{ssim:.4f}"))
self.table_widget.setItem(7, 1, QTableWidgetItem(f"{mssim:.4f}"))
self.table_widget.setItem(8, 1, QTableWidgetItem(f"{rase:.2f}"))
self.table_widget.setItem(9, 1, QTableWidgetItem(f"{scc:.4f}"))
self.table_widget.setItem(10, 1, QTableWidgetItem(f"{uqi:.4f}"))
self.table_widget.setItem(11, 1, QTableWidgetItem(f"{vifp:.4f}"))
self.table_widget.setItem(12, 1, QTableWidgetItem(f"{ssimul:.4f}"))
self.table_widget.setItem(13, 1, QTableWidgetItem(f"{butter:.2f}"))
self.table_widget.setItem(14, 1,
QTableWidgetItem(f"{correlation:.2f}"))
self.table_widget.setItem(15, 1, QTableWidgetItem(f"{chi_square:.2f}"))
self.table_widget.setItem(16, 1,
QTableWidgetItem(f"{chi_square2:.2f}"))
self.table_widget.setItem(17, 1,
QTableWidgetItem(f"{intersection:.2f}"))
self.table_widget.setItem(18, 1, QTableWidgetItem(f"{hellinger:.2f}"))
self.table_widget.setItem(19, 1, QTableWidgetItem(f"{divergence:.2f}"))
self.table_widget.resizeColumnsToContents()
self.table_widget.setEnabled(True)
self.metric_button.setEnabled(False)
self.ssim_radio.setEnabled(True)
self.butter_radio.setEnabled(True)
def cancel(self):
self.stopped = True
@staticmethod
def rmse(x, y):
return np.sqrt(np.mean(np.square(x - y)))
@staticmethod
def mb(x, y):
mx = np.mean(x)
my = np.mean(y)
return (mx - my) / mx
@staticmethod
def pfe(x, y):
return np.linalg.norm(x - y) / np.linalg.norm(x) * 100
@staticmethod
def ssim(x, y):
c1 = 6.5025
c2 = 58.5225
k = (11, 11)
s = 1.5
x2 = x**2
y2 = y**2
xy = x * y
mu_x = cv.GaussianBlur(x, k, s)
mu_y = cv.GaussianBlur(y, k, s)
mu_x2 = mu_x**2
mu_y2 = mu_y**2
mu_xy = mu_x * mu_y
s_x2 = cv.GaussianBlur(x2, k, s) - mu_x2
s_y2 = cv.GaussianBlur(y2, k, s) - mu_y2
s_xy = cv.GaussianBlur(xy, k, s) - mu_xy
t1 = 2 * mu_xy + c1
t2 = 2 * s_xy + c2
t3 = t1 * t2
t1 = mu_x2 + mu_y2 + c1
t2 = s_x2 + s_y2 + c2
t1 *= t2
ssim_map = cv.divide(t3, t1)
ssim = cv.mean(ssim_map)[0]
return ssim, 255 - norm_mat(ssim_map, to_bgr=True)
@staticmethod
def corr(x, y):
return np.corrcoef(x, y)[0, 1]
@staticmethod
def psnr(x, y):
k = np.mean(np.square(x - y))
if k == 0:
return -1
return 20 * math.log10((255**2) / k)
@staticmethod
def butter(x, y):
try:
exe = butter_exe()
if exe is None:
raise FileNotFoundError
temp_dir = QTemporaryDir()
if temp_dir.isValid():
filename1 = os.path.join(temp_dir.path(), "img1.png")
cv.imwrite(filename1, x)
filename2 = os.path.join(temp_dir.path(), "img2.png")
cv.imwrite(filename2, y)
filename3 = os.path.join(temp_dir.path(), "map.ppm")
p = run([exe, filename1, filename2, filename3], stdout=PIPE)
if p.returncode == 0:
value = float(p.stdout)
heatmap = cv.imread(filename3, cv.IMREAD_COLOR)
else:
raise ValueError
return value, heatmap
except (FileNotFoundError, ValueError) as _:
return -1, cv.cvtColor(np.full_like(x, 127), cv.COLOR_GRAY2BGR)
@staticmethod
def ssimul(x, y):
try:
exe = ssimul_exe()
if exe is None:
raise FileNotFoundError
temp_dir = QTemporaryDir()
if temp_dir.isValid():
filename1 = os.path.join(temp_dir.path(), "img1.png")
cv.imwrite(filename1, x)
filename2 = os.path.join(temp_dir.path(), "img2.png")
cv.imwrite(filename2, y)
p = run([exe, filename1, filename2], stdout=PIPE)
if p.returncode == 0:
value = float(p.stdout)
else:
raise ValueError
return value
except (FileNotFoundError, ValueError) as _:
return -1
class ComparisonWidget(ToolWidget):
def __init__(self, filename, image, parent=None):
super(ComparisonWidget, self).__init__(parent)
load_button = QPushButton(self.tr('Load reference...'))
self.comp_label = QLabel(self.tr('Comparison:'))
self.normal_radio = QRadioButton(self.tr('Normal'))
self.normal_radio.setChecked(True)
self.diff_radio = QRadioButton(self.tr('Difference'))
self.ssim_radio = QRadioButton(self.tr('SSIM Map'))
self.butter_radio = QRadioButton(self.tr('Butteraugli'))
self.gray_check = QCheckBox(self.tr('Grayscale'))
self.equal_check = QCheckBox(self.tr('Equalized'))
self.last_radio = self.normal_radio
self.metric_button = QPushButton(self.tr('Compute'))
self.evidence = image
self.reference = self.difference = self.ssim_map = self.butter_map = None
basename = os.path.basename(filename)
self.evidence_viewer = ImageViewer(
self.evidence, None, self.tr('Evidence: {}'.format(basename)))
self.reference_viewer = ImageViewer(np.full_like(self.evidence, 127),
None, self.tr('Reference'))
self.table_widget = QTableWidget(15, 3)
self.table_widget.setHorizontalHeaderLabels(
[self.tr('Metric'),
self.tr('Value'),
self.tr('Better')])
self.table_widget.setItem(0, 0, QTableWidgetItem(self.tr('RMSE')))
self.table_widget.setItem(
0, 2, QTableWidgetItem(QIcon('icons/low.svg'), '(0)'))
self.table_widget.item(0, 0).setToolTip(
self.
tr('Root Mean Square Error (RMSE) is commonly used to compare \n'
'the difference between the reference and evidence images \n'
'by directly computing the variation in pixel values. \n'
'The combined image is close to the reference image when \n'
'RMSE value is zero. RMSE is a good indicator of the spectral \n'
'quality of the reference image.'))
self.table_widget.setItem(1, 0, QTableWidgetItem(self.tr('SAM')))
self.table_widget.setItem(
1, 2, QTableWidgetItem(QIcon('icons/low.svg'), '(0)'))
self.table_widget.item(1, 0).setToolTip(
self.
tr('It computes the spectral angle between the pixel, vector of the \n'
'evidence image and reference image. It is worked out in either \n'
'degrees or radians. It is performed on a pixel-by-pixel base. \n'
'A SAM equal to zero denotes the absence of spectral distortion.'
))
self.table_widget.setItem(2, 0, QTableWidgetItem(self.tr('ERGAS')))
self.table_widget.setItem(
2, 2, QTableWidgetItem(QIcon('icons/low.svg'), '(0)'))
self.table_widget.item(2, 0).setToolTip(
self.
tr('It is used to compute the quality of reference image in terms \n'
'of normalized average error of each band of the reference image. \n'
'Increase in the value of ERGAS indicates distortion in the \n'
'reference image, lower value of ERGAS indicates that it is \n'
'similar to the reference image.'))
self.table_widget.setItem(3, 0, QTableWidgetItem(self.tr('MB')))
self.table_widget.setItem(
3, 2, QTableWidgetItem(QIcon('icons/low.svg'), '(0)'))
self.table_widget.item(3, 0).setToolTip(
self.
tr('Mean Bias is the difference between the mean of the evidence \n'
'image and reference image. The ideal value is zero and indicates \n'
'that the evidence and reference images are similar. Mean value \n'
'refers to the grey level of pixels in an image.'))
self.table_widget.setItem(4, 0, QTableWidgetItem(self.tr('PFE')))
self.table_widget.setItem(
4, 2, QTableWidgetItem(QIcon('icons/low.svg'), '(0)'))
self.table_widget.item(4, 0).setToolTip(
self.
tr('It computes the norm of the difference between the corresponding \n'
'pixels of the reference and fused image to the norm of the reference \n'
'image. When the calculated value is zero, it indicates that both the \n'
'reference and fused images are similar and value will be increased \n'
'when the merged image is not similar to the reference image.'))
self.table_widget.setItem(5, 0, QTableWidgetItem(self.tr('PSNR')))
self.table_widget.setItem(
5, 2,
QTableWidgetItem(QIcon('icons/high.svg'), '(+' + u'\u221e' + ')'))
self.table_widget.item(5, 0).setToolTip(
self.
tr('It is widely used metric it is computed by the number of gray levels \n'
'in the image divided by the corresponding pixels in the evidence and \n'
'the reference images. When the value is high, both images are similar.'
))
self.table_widget.setItem(6, 0, QTableWidgetItem(self.tr('PSNR-B')))
self.table_widget.setItem(
6, 2,
QTableWidgetItem(QIcon('icons/high.svg'), '(+' + u'\u221e' + ')'))
self.table_widget.item(6, 0).setToolTip(
self.tr('PSNR with Blocking Effect Factor.'))
self.table_widget.setItem(7, 0, QTableWidgetItem(self.tr('SSIM')))
self.table_widget.setItem(
7, 2, QTableWidgetItem(QIcon('icons/high.svg'), '(1)'))
self.table_widget.item(7, 0).setToolTip(
self.
tr('SSIM is used to compare the local patterns of pixel intensities between \n'
' the reference and fused images. The range varies between -1 to 1. \n'
'The value 1 indicates the reference and fused images are similar.'
))
self.table_widget.setItem(8, 0, QTableWidgetItem(self.tr('MS-SSIM')))
self.table_widget.setItem(
8, 2, QTableWidgetItem(QIcon('icons/high.svg'), '(1)'))
self.table_widget.item(8, 0).setToolTip(
self.tr('Multiscale version of SSIM.'))
self.table_widget.setItem(9, 0, QTableWidgetItem(self.tr('RASE')))
self.table_widget.setItem(
9, 2, QTableWidgetItem(QIcon('icons/low.svg'), '(0)'))
self.table_widget.item(9, 0).setToolTip(
self.tr('Relative average spectral error'))
self.table_widget.setItem(10, 0, QTableWidgetItem(self.tr('SCC')))
self.table_widget.setItem(
10, 2, QTableWidgetItem(QIcon('icons/high.svg'), '(1)'))
self.table_widget.item(10, 0).setToolTip(
self.tr('Spatial Correlation Coefficient'))
self.table_widget.setItem(11, 0, QTableWidgetItem(self.tr('UQI')))
self.table_widget.setItem(
11, 2, QTableWidgetItem(QIcon('icons/high.svg'), '(1)'))
self.table_widget.item(11, 0).setToolTip(
self.tr('Universal Image Quality Index'))
self.table_widget.setItem(12, 0, QTableWidgetItem(self.tr('VIF-P')))
self.table_widget.setItem(
12, 2, QTableWidgetItem(QIcon('icons/high.svg'), '(1)'))
self.table_widget.item(12, 0).setToolTip(
self.tr('Pixel-based Visual Information Fidelity'))
self.table_widget.setItem(13, 0,
QTableWidgetItem(self.tr('SSIMulacra')))
self.table_widget.setItem(
13, 2, QTableWidgetItem(QIcon('icons/low.svg'), '(0)'))
self.table_widget.item(13, 0).setToolTip(
self.tr('Structural SIMilarity Unveiling Local '
'And Compression Related Artifacts'))
self.table_widget.setItem(14, 0,
QTableWidgetItem(self.tr('Butteraugli')))
self.table_widget.setItem(
14, 2, QTableWidgetItem(QIcon('icons/low.svg'), '(0)'))
self.table_widget.item(14, 0).setToolTip(
self.tr('Estimate psychovisual error'))
for i in range(self.table_widget.rowCount()):
modify_font(self.table_widget.item(i, 0), bold=True)
self.table_widget.setSelectionMode(QAbstractItemView.SingleSelection)
self.table_widget.setEditTriggers(QAbstractItemView.NoEditTriggers)
self.table_widget.resizeColumnsToContents()
self.table_widget.setAlternatingRowColors(True)
self.stopped = False
self.comp_label.setEnabled(False)
self.normal_radio.setEnabled(False)
self.diff_radio.setEnabled(False)
self.ssim_radio.setEnabled(False)
self.butter_radio.setEnabled(False)
self.gray_check.setEnabled(False)
self.equal_check.setEnabled(False)
self.metric_button.setEnabled(False)
self.table_widget.setEnabled(False)
load_button.clicked.connect(self.load)
self.normal_radio.clicked.connect(self.change)
self.diff_radio.clicked.connect(self.change)
self.butter_radio.clicked.connect(self.change)
self.gray_check.stateChanged.connect(self.change)
self.equal_check.stateChanged.connect(self.change)
self.ssim_radio.clicked.connect(self.change)
self.evidence_viewer.viewChanged.connect(
self.reference_viewer.change_view)
self.reference_viewer.viewChanged.connect(
self.evidence_viewer.change_view)
self.metric_button.clicked.connect(self.metrics)
top_layout = QHBoxLayout()
top_layout.addWidget(load_button)
top_layout.addStretch()
top_layout.addWidget(self.comp_label)
top_layout.addWidget(self.normal_radio)
top_layout.addWidget(self.diff_radio)
top_layout.addWidget(self.ssim_radio)
top_layout.addWidget(self.butter_radio)
top_layout.addWidget(self.gray_check)
top_layout.addWidget(self.equal_check)
metric_layout = QVBoxLayout()
index_label = QLabel(self.tr('Image Quality Assessment'))
index_label.setAlignment(Qt.AlignCenter)
modify_font(index_label, bold=True)
metric_layout.addWidget(index_label)
metric_layout.addWidget(self.table_widget)
metric_layout.addWidget(self.metric_button)
center_layout = QHBoxLayout()
center_layout.addWidget(self.evidence_viewer)
center_layout.addWidget(self.reference_viewer)
center_layout.addLayout(metric_layout)
main_layout = QVBoxLayout()
main_layout.addLayout(top_layout)
main_layout.addLayout(center_layout)
self.setLayout(main_layout)
def load(self):
filename, basename, reference = load_image(self)
if filename is None:
return
if reference.shape != self.evidence.shape:
QMessageBox.critical(
self, self.tr('Error'),
self.tr('Evidence and reference must have the same size!'))
return
self.reference = reference
self.reference_viewer.set_title(
self.tr('Reference: {}'.format(basename)))
self.difference = normalize_mat(
cv.absdiff(self.evidence, self.reference))
self.comp_label.setEnabled(True)
self.normal_radio.setEnabled(True)
self.diff_radio.setEnabled(True)
self.ssim_radio.setEnabled(False)
self.butter_radio.setEnabled(False)
self.gray_check.setEnabled(True)
self.equal_check.setEnabled(True)
self.metric_button.setEnabled(True)
for i in range(self.table_widget.rowCount()):
self.table_widget.setItem(i, 1, QTableWidgetItem())
self.normal_radio.setChecked(True)
self.table_widget.setEnabled(False)
self.change()
def change(self):
if self.normal_radio.isChecked():
result = self.reference
self.gray_check.setEnabled(False)
self.equal_check.setEnabled(False)
self.last_radio = self.normal_radio
elif self.diff_radio.isChecked():
result = self.difference
self.gray_check.setEnabled(True)
self.equal_check.setEnabled(True)
self.last_radio = self.diff_radio
elif self.ssim_radio.isChecked():
result = self.ssim_map
self.gray_check.setEnabled(False)
self.equal_check.setEnabled(True)
self.last_radio = self.ssim_radio
elif self.butter_radio.isChecked():
result = self.butter_map
self.gray_check.setEnabled(True)
self.equal_check.setEnabled(False)
self.last_radio = self.butter_radio
else:
self.last_radio.setChecked(True)
return
if self.equal_check.isChecked():
result = equalize_image(result)
if self.gray_check.isChecked():
result = desaturate(result)
self.reference_viewer.update_original(result)
def metrics(self):
progress = QProgressDialog(self.tr('Computing metrics...'),
self.tr('Cancel'), 1,
self.table_widget.rowCount(), self)
progress.canceled.connect(self.cancel)
progress.setWindowModality(Qt.WindowModal)
img1 = cv.cvtColor(self.evidence, cv.COLOR_BGR2GRAY)
img2 = cv.cvtColor(self.reference, cv.COLOR_BGR2GRAY)
x = img1.astype(np.float64)
y = img2.astype(np.float64)
rmse = self.rmse(x, y)
progress.setValue(1)
if self.stopped:
return
sam = sewar.sam(img1, img2)
progress.setValue(2)
if self.stopped:
return
ergas = sewar.ergas(img1, img2)
progress.setValue(3)
if self.stopped:
return
mb = self.mb(x, y)
progress.setValue(4)
if self.stopped:
return
pfe = self.pfe(x, y)
progress.setValue(5)
if self.stopped:
return
psnr = self.psnr(x, y)
progress.setValue(6)
if self.stopped:
return
psnrb = sewar.psnrb(img1, img2)
progress.setValue(7)
if self.stopped:
return
ssim, self.ssim_map = self.ssim(x, y)
progress.setValue(8)
if self.stopped:
return
mssim = sewar.msssim(img1, img2).real
progress.setValue(9)
if self.stopped:
return
rase = sewar.rase(img1, img2)
progress.setValue(10)
if self.stopped:
return
scc = sewar.scc(img1, img2)
progress.setValue(11)
if self.stopped:
return
uqi = sewar.uqi(img1, img2)
progress.setValue(12)
if self.stopped:
return
vifp = sewar.vifp(img1, img2)
progress.setValue(13)
if self.stopped:
return
ssimul = self.ssimul(img1, img2)
progress.setValue(14)
if self.stopped:
return
butter, self.butter_map = self.butter(img1, img2)
progress.setValue(15)
self.table_widget.setItem(0, 1,
QTableWidgetItem('{:.4f}'.format(rmse)))
self.table_widget.setItem(1, 1, QTableWidgetItem('{:.4f}'.format(sam)))
self.table_widget.setItem(2, 1,
QTableWidgetItem('{:.4f}'.format(ergas)))
self.table_widget.setItem(3, 1, QTableWidgetItem('{:.4f}'.format(mb)))
self.table_widget.setItem(4, 1, QTableWidgetItem('{:.4f}'.format(pfe)))
if psnr > 0:
self.table_widget.setItem(
5, 1, QTableWidgetItem('{:.4f} dB'.format(psnr)))
else:
self.table_widget.setItem(
5, 1, QTableWidgetItem('+' + u'\u221e' + ' dB'))
self.table_widget.setItem(6, 1,
QTableWidgetItem('{:.4f}'.format(psnrb)))
self.table_widget.setItem(7, 1,
QTableWidgetItem('{:.4f}'.format(ssim)))
self.table_widget.setItem(8, 1,
QTableWidgetItem('{:.4f}'.format(mssim)))
self.table_widget.setItem(9, 1,
QTableWidgetItem('{:.4f}'.format(rase)))
self.table_widget.setItem(10, 1,
QTableWidgetItem('{:.4f}'.format(scc)))
self.table_widget.setItem(11, 1,
QTableWidgetItem('{:.4f}'.format(uqi)))
self.table_widget.setItem(12, 1,
QTableWidgetItem('{:.4f}'.format(vifp)))
self.table_widget.setItem(13, 1,
QTableWidgetItem('{:.4f}'.format(ssimul)))
self.table_widget.setItem(14, 1,
QTableWidgetItem('{:.4f}'.format(butter)))
self.table_widget.resizeColumnsToContents()
self.table_widget.setEnabled(True)
self.metric_button.setEnabled(False)
self.ssim_radio.setEnabled(True)
self.butter_radio.setEnabled(True)
def cancel(self):
self.stopped = True
@staticmethod
def rmse(x, y):
return np.sqrt(np.mean(np.square(x - y)))
@staticmethod
def mb(x, y):
mx = np.mean(x)
my = np.mean(y)
return (mx - my) / mx
@staticmethod
def pfe(x, y):
return np.linalg.norm(x - y) / np.linalg.norm(x) * 100
@staticmethod
def ssim(x, y):
c1 = 6.5025
c2 = 58.5225
k = (11, 11)
s = 1.5
x2 = x**2
y2 = y**2
xy = x * y
mu_x = cv.GaussianBlur(x, k, s)
mu_y = cv.GaussianBlur(y, k, s)
mu_x2 = mu_x**2
mu_y2 = mu_y**2
mu_xy = mu_x * mu_y
s_x2 = cv.GaussianBlur(x2, k, s) - mu_x2
s_y2 = cv.GaussianBlur(y2, k, s) - mu_y2
s_xy = cv.GaussianBlur(xy, k, s) - mu_xy
t1 = 2 * mu_xy + c1
t2 = 2 * s_xy + c2
t3 = t1 * t2
t1 = mu_x2 + mu_y2 + c1
t2 = s_x2 + s_y2 + c2
t1 *= t2
ssim_map = cv.divide(t3, t1)
ssim = cv.mean(ssim_map)[0]
return ssim, 255 - normalize_mat(ssim_map, to_bgr=True)
@staticmethod
def corr(x, y):
return np.corrcoef(x, y)[0, 1]
@staticmethod
def psnr(x, y):
k = np.mean(np.square(x - y))
if k == 0:
return -1
return 20 * math.log10((255**2) / k)
@staticmethod
def butter(x, y):
try:
exe = butter_exe()
if exe is None:
raise FileNotFoundError
temp_dir = QTemporaryDir()
if temp_dir.isValid():
filename1 = os.path.join(temp_dir.path(), 'img1.png')
cv.imwrite(filename1, x)
filename2 = os.path.join(temp_dir.path(), 'img2.png')
cv.imwrite(filename2, y)
filename3 = os.path.join(temp_dir.path(), 'map.ppm')
p = run([exe, filename1, filename2, filename3], stdout=PIPE)
value = float(p.stdout)
heatmap = cv.imread(filename3, cv.IMREAD_COLOR)
return value, heatmap
except FileNotFoundError:
return -1, cv.cvtColor(np.full_like(x, 127), cv.COLOR_GRAY2BGR)
@staticmethod
def ssimul(x, y):
try:
exe = ssimul_exe()
if exe is None:
raise FileNotFoundError
temp_dir = QTemporaryDir()
if temp_dir.isValid():
filename1 = os.path.join(temp_dir.path(), 'img1.png')
cv.imwrite(filename1, x)
filename2 = os.path.join(temp_dir.path(), 'img2.png')
cv.imwrite(filename2, y)
p = run([exe, filename1, filename2], stdout=PIPE)
value = float(p.stdout)
return value
except FileNotFoundError:
return -1
class ComparisonWidget(ToolWidget):
def __init__(self, image, parent=None):
super(ComparisonWidget, self).__init__(parent)
load_button = QPushButton(self.tr('Load reference...'))
self.file_label = QLabel()
modify_font(self.file_label, bold=True)
self.comp_label = QLabel(self.tr('Comparison:'))
self.normal_radio = QRadioButton(self.tr('Normal'))
self.normal_radio.setChecked(True)
self.diff_radio = QRadioButton(self.tr('Difference'))
self.gray_check = QCheckBox(self.tr('Grayscale'))
self.equal_check = QCheckBox(self.tr('Equalized'))
self.map_radio = QRadioButton(self.tr('SSIM Map'))
self.last_radio = self.normal_radio
self.comp_label.setEnabled(False)
self.normal_radio.setEnabled(False)
self.diff_radio.setEnabled(False)
self.gray_check.setEnabled(False)
self.equal_check.setEnabled(False)
self.map_radio.setEnabled(False)
self.evidence = image
self.reference = np.full_like(image, 127)
self.difference = np.full_like(image, 127)
self.equalized = np.full_like(image, 127)
self.indexmap = np.full_like(image, 127)
self.evidence_viewer = ImageViewer(self.evidence, None,
self.tr('Evidence'))
self.reference_viewer = ImageViewer(self.reference, None,
self.tr('Reference'))
self.table_widget = QTableWidget(5, 2)
self.table_widget.setHorizontalHeaderLabels(
[self.tr('Index'), self.tr('Value')])
self.table_widget.setItem(0, 0, QTableWidgetItem(self.tr('MSE')))
self.table_widget.setItem(1, 0, QTableWidgetItem(self.tr('COVAR')))
self.table_widget.setItem(2, 0, QTableWidgetItem(self.tr('PSNR')))
self.table_widget.setItem(3, 0, QTableWidgetItem(self.tr('SSIM')))
self.table_widget.setItem(4, 0, QTableWidgetItem(self.tr('CORR')))
for i in range(self.table_widget.rowCount()):
modify_font(self.table_widget.item(i, 0), bold=True)
self.table_widget.setEnabled(False)
load_button.clicked.connect(self.load)
self.normal_radio.toggled.connect(self.change)
self.diff_radio.toggled.connect(self.change)
self.gray_check.stateChanged.connect(self.change)
self.equal_check.stateChanged.connect(self.change)
self.map_radio.toggled.connect(self.change)
self.evidence_viewer.view_changed.connect(
self.reference_viewer.change_view)
self.reference_viewer.view_changed.connect(
self.evidence_viewer.change_view)
top_layout = QHBoxLayout()
top_layout.addWidget(load_button)
top_layout.addWidget(self.file_label)
top_layout.addStretch()
top_layout.addWidget(self.comp_label)
top_layout.addWidget(self.normal_radio)
top_layout.addWidget(self.map_radio)
top_layout.addWidget(self.diff_radio)
top_layout.addWidget(self.gray_check)
top_layout.addWidget(self.equal_check)
index_layout = QVBoxLayout()
index_label = QLabel(self.tr('Image Quality Assessment'))
modify_font(index_label, bold=True)
index_layout.addWidget(index_label)
index_layout.addWidget(self.table_widget)
center_layout = QHBoxLayout()
center_layout.addWidget(self.evidence_viewer)
center_layout.addWidget(self.reference_viewer)
center_layout.addLayout(index_layout)
main_layout = QVBoxLayout()
main_layout.addLayout(top_layout)
main_layout.addLayout(center_layout)
self.setLayout(main_layout)
def load(self):
filename, basename, image = load_image(self)
if filename is None:
return
if image.shape != self.evidence.shape:
QMessageBox.critical(
self, self.tr('Error'),
self.tr('Evidence and reference must have the same size!'))
return
self.file_label.setText(basename)
self.reference = image
self.difference = normalize_mat(
cv.absdiff(self.evidence, self.reference))
self.equalized = cv.merge(
[cv.equalizeHist(c) for c in cv.split(self.difference)])
x = cv.cvtColor(self.evidence, cv.COLOR_BGR2GRAY).astype(np.float32)
y = cv.cvtColor(self.reference, cv.COLOR_BGR2GRAY).astype(np.float32)
mse = self.mse(x, y)
covar = self.covar(x, y)
psnr = self.psnr(mse)
ssim, self.indexmap = self.ssim(x, y)
corr = self.corr(x, y)
self.table_widget.setItem(0, 1, QTableWidgetItem('{:.4f}'.format(mse)))
self.table_widget.setItem(1, 1,
QTableWidgetItem('{:.4f}'.format(covar)))
if psnr > 0:
self.table_widget.setItem(
2, 1, QTableWidgetItem('{:.2f} dB'.format(psnr)))
else:
self.table_widget.setItem(
2, 1, QTableWidgetItem('+' + u'\u221e' + ' dB'))
self.table_widget.setItem(3, 1,
QTableWidgetItem('{:.4f}'.format(ssim)))
self.table_widget.setItem(4, 1,
QTableWidgetItem('{:.4f}'.format(corr)))
self.table_widget.setEnabled(True)
self.comp_label.setEnabled(True)
self.normal_radio.setEnabled(True)
self.diff_radio.setEnabled(True)
self.gray_check.setEnabled(True)
self.equal_check.setEnabled(True)
self.map_radio.setEnabled(True)
self.change()
def change(self):
self.gray_check.setEnabled(False)
self.equal_check.setEnabled(False)
if self.normal_radio.isChecked():
self.reference_viewer.update_original(self.reference)
self.last_radio = self.normal_radio
elif self.diff_radio.isChecked():
self.gray_check.setEnabled(True)
self.equal_check.setEnabled(True)
if self.equal_check.isChecked():
result = self.equalized
else:
result = self.difference
if self.gray_check.isChecked():
result = desaturate(result)
self.reference_viewer.update_original(result)
self.last_radio = self.diff_radio
elif self.map_radio.isChecked():
self.reference_viewer.update_original(self.indexmap)
self.last_radio = self.map_radio
else:
self.last_radio.setChecked(True)
@staticmethod
def ssim(x, y):
c1 = 6.5025
c2 = 58.5225
k = (11, 11)
s = 1.5
x2 = x**2
y2 = y**2
xy = x * y
mu_x = cv.GaussianBlur(x, k, s)
mu_y = cv.GaussianBlur(y, k, s)
mu_x2 = mu_x**2
mu_y2 = mu_y**2
mu_xy = mu_x * mu_y
s_x2 = cv.GaussianBlur(x2, k, s) - mu_x2
s_y2 = cv.GaussianBlur(y2, k, s) - mu_y2
s_xy = cv.GaussianBlur(xy, k, s) - mu_xy
t1 = 2 * mu_xy + c1
t2 = 2 * s_xy + c2
t3 = t1 * t2
t1 = mu_x2 + mu_y2 + c1
t2 = s_x2 + s_y2 + c2
t1 *= t2
indexmap = cv.divide(t3, t1)
ssim = cv.mean(indexmap)[0]
return ssim, 255 - normalize_mat(indexmap, to_bgr=True)
@staticmethod
def corr(x, y):
return np.corrcoef(x, y)[0, 1]
@staticmethod
def covar(x, y):
return np.std(cv.absdiff(x, y))
@staticmethod
def mse(x, y):
return cv.mean(cv.pow(x - y, 2))[0]
@staticmethod
def psnr(mse):
k = math.sqrt(mse)
if k == 0:
return -1
return 20 * math.log10((255**2) / k)