def process(self, rect): y1 = rect.top() y2 = rect.bottom() x1 = rect.left() x2 = rect.right() roi = self.image[y1:y2, x1:x2] if self.equalize_radio.isChecked(): self.centile_spin.setEnabled(False) self.channel_check.setEnabled(False) roi = equalize_img(roi) self.last_radio = self.equalize_radio elif self.contrast_radio.isChecked(): self.centile_spin.setEnabled(True) self.channel_check.setEnabled(True) centile = self.centile_spin.value() / 200 if self.channel_check.isChecked(): roi = cv.merge( [cv.LUT(c, auto_lut(c, centile)) for c in cv.split(roi)]) else: roi = cv.LUT( roi, auto_lut(cv.cvtColor(roi, cv.COLOR_BGR2GRAY), centile)) self.last_radio = self.contrast_radio else: self.last_radio.setChecked(True) return processed = np.copy(self.image) processed[y1:y2, x1:x2] = roi self.viewer.update_processed(processed)
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 process(self): start = time() intensity = int(self.intensity_spin.value() / 100 * 127) invert = self.invert_check.isChecked() equalize = self.equalize_check.isChecked() self.intensity_spin.setEnabled(not equalize) blue_mode = self.blue_combo.currentIndex() if invert: dx = (-self.dx).astype(np.float32) dy = (-self.dy).astype(np.float32) else: dx = (+self.dx).astype(np.float32) dy = (+self.dy).astype(np.float32) dx_abs = np.abs(dx) dy_abs = np.abs(dy) red = ((dx / np.max(dx_abs) * 127) + 127).astype(np.uint8) green = ((dy / np.max(dy_abs) * 127) + 127).astype(np.uint8) if blue_mode == 0: blue = np.zeros_like(red) elif blue_mode == 1: blue = np.full_like(red, 255) elif blue_mode == 2: blue = norm_mat(dx_abs + dy_abs) elif blue_mode == 3: blue = norm_mat(np.linalg.norm(cv.merge((red, green)), axis=2)) else: blue = None gradient = cv.merge([blue, green, red]) if equalize: gradient = equalize_img(gradient) elif intensity > 0: gradient = cv.LUT(gradient, create_lut(intensity, intensity)) self.viewer.update_processed(gradient) self.info_message.emit( self.tr(f"Luminance Gradient = {elapsed_time(start)}"))
def process(self): start = time() grayscale = self.gray_check.isChecked() if grayscale: original = cv.cvtColor(self.image, cv.COLOR_BGR2GRAY) else: original = self.image radius = self.radius_spin.value() kernel = radius * 2 + 1 sigma = self.sigma_spin.value() choice = self.mode_combo.currentText() if choice == self.tr('Median'): self.sigma_spin.setEnabled(False) denoised = cv.medianBlur(original, kernel) elif choice == self.tr('Gaussian'): self.sigma_spin.setEnabled(False) denoised = cv.GaussianBlur(original, (kernel, kernel), 0) elif choice == self.tr('BoxBlur'): self.sigma_spin.setEnabled(False) denoised = cv.blur(original, (kernel, kernel)) elif choice == self.tr('Bilateral'): self.sigma_spin.setEnabled(True) denoised = cv.bilateralFilter(original, kernel, sigma, sigma) elif choice == self.tr('NonLocal'): if grayscale: denoised = cv.fastNlMeansDenoising(original, None, kernel) else: denoised = cv.fastNlMeansDenoisingColored( original, None, kernel, kernel) else: denoised = None if self.denoised_check.isChecked(): self.levels_spin.setEnabled(False) result = denoised else: self.levels_spin.setEnabled(True) noise = cv.absdiff(original, denoised) levels = self.levels_spin.value() if levels == 0: if grayscale: result = cv.equalizeHist(noise) else: result = equalize_img(noise) else: result = cv.LUT(noise, create_lut(0, 255 - levels)) if grayscale: result = cv.cvtColor(result, cv.COLOR_GRAY2BGR) self.viewer.update_processed(result) self.info_message.emit( self.tr('Noise estimation = {}'.format(elapsed_time(start))))
def process(self): index = 3 * self.component_combo.currentIndex() if self.distance_radio.isChecked(): output = self.output[index] self.last_radio = self.distance_radio elif self.project_radio.isChecked(): output = self.output[index + 1] self.last_radio = self.project_radio elif self.crossprod_radio.isChecked(): output = self.output[index + 2] self.last_radio = self.crossprod_radio else: self.last_radio.setChecked(True) return if self.invert_check.isChecked(): output = cv.bitwise_not(output) if self.equalize_check.isChecked(): output = equalize_img(output) self.viewer.update_processed(output)
def process(self): start = time() quality = self.quality_spin.value() scale = self.scale_spin.value() / 20 contrast = int(self.contrast_spin.value() / 100 * 128) equalize = self.equalize_check.isChecked() grayscale = self.gray_check.isChecked() self.scale_spin.setEnabled(not equalize) self.contrast_spin.setEnabled(not equalize) compressed = compress_jpeg(self.image, quality).astype(np.float32) / 255 difference = cv.absdiff(self.original, compressed) if equalize: ela = equalize_img((difference * 255).astype(np.uint8)) else: ela = cv.convertScaleAbs(cv.sqrt(difference) * 255, None, scale) ela = cv.LUT(ela, create_lut(contrast, contrast)) if grayscale: ela = desaturate(ela) self.viewer.update_processed(ela) self.info_message.emit(self.tr('Error Level Analysis = {}'.format(elapsed_time(start))))