# Author: XpucT # Script's homepage: https://boosty.to/xpuct import modules.scripts as scripts import gradio as gr import numpy as np import cv2 import math import random import modules.images as images from modules.processing import Processed from PIL import ImageEnhance, Image, ImageDraw, ImageFilter, ImageChops, ImageOps, ImageFont from blendmodes.blend import blendLayers, BlendType from typing import List def resetValues(saturationSlider, temperatureSlider, brightnessSlider, contrastSlider, sharpnessSlider, blurSlider, noiseSlider, vignetteSlider, exposureOffsetSlider, hdrSlider): saturationSlider = 1 temperatureSlider = 1 brightnessSlider = 1 contrastSlider = 1 sharpnessSlider = 0 blurSlider = 0 noiseSlider = 0 vignetteSlider = 0 exposureOffsetSlider = 0 hdrSlider = 0 return [saturationSlider, temperatureSlider, brightnessSlider, contrastSlider, sharpnessSlider, blurSlider, noiseSlider, vignetteSlider, exposureOffsetSlider, hdrSlider] def bestChoiceValues(saturationSlider, temperatureSlider, brightnessSlider, contrastSlider, sharpnessSlider, blurSlider, noiseSlider, vignetteSlider, exposureOffsetSlider, hdrSlider): saturationSlider = .98 temperatureSlider = 1.04 brightnessSlider = 1.01 contrastSlider = .97 sharpnessSlider = .02 blurSlider = 0 noiseSlider = .03 vignetteSlider = .05 exposureOffsetSlider = .1 hdrSlider = .16 return [saturationSlider, temperatureSlider, brightnessSlider, contrastSlider, sharpnessSlider, blurSlider, noiseSlider, vignetteSlider, exposureOffsetSlider, hdrSlider] def add_chromatic(im, strength: float = 1, no_blur: bool = False): if (im.size[0] % 2 == 0 or im.size[1] % 2 == 0): if (im.size[0] % 2 == 0): im = im.crop((0, 0, im.size[0] - 1, im.size[1])) im.load() if (im.size[1] % 2 == 0): im = im.crop((0, 0, im.size[0], im.size[1] - 1)) im.load() def cartesian_to_polar(data: np.ndarray) -> np.ndarray: width = data.shape[1] height = data.shape[0] assert (width > 2) assert (height > 2) assert (width % 2 == 1) assert (height % 2 == 1) perimeter = 2 * (width + height - 2) halfdiag = math.ceil(((width ** 2 + height ** 2) ** 0.5) / 2) halfw = width // 2 halfh = height // 2 ret = np.zeros((halfdiag, perimeter, 3)) ret[0:(halfw + 1), halfh] = data[halfh, halfw::-1] ret[0:(halfw + 1), height + width - 2 + halfh] = data[halfh, halfw:(halfw * 2 + 1)] ret[0:(halfh + 1), height - 1 + halfw] = data[halfh:(halfh * 2 + 1), halfw] ret[0:(halfh + 1), perimeter - halfw] = data[halfh::-1, halfw] for i in range(0, halfh): slope = (halfh - i) / (halfw) diagx = ((halfdiag ** 2) / (slope ** 2 + 1)) ** 0.5 unit_xstep = diagx / (halfdiag - 1) unit_ystep = diagx * slope / (halfdiag - 1) for row in range(halfdiag): ystep = round(row * unit_ystep) xstep = round(row * unit_xstep) if ((halfh >= ystep) and halfw >= xstep): ret[row, i] = data[halfh - ystep, halfw - xstep] ret[row, height - 1 - i] = data[halfh + ystep, halfw - xstep] ret[row, height + width - 2 + i] = data[halfh + ystep, halfw + xstep] ret[row, height + width + height - 3 - i] = data[halfh - ystep, halfw + xstep] else: break for j in range(1, halfw): slope = (halfh) / (halfw - j) diagx = ((halfdiag ** 2) / (slope ** 2 + 1)) ** 0.5 unit_xstep = diagx / (halfdiag - 1) unit_ystep = diagx * slope / (halfdiag - 1) for row in range(halfdiag): ystep = round(row * unit_ystep) xstep = round(row * unit_xstep) if (halfw >= xstep and halfh >= ystep): ret[row, height - 1 + j] = data[halfh + ystep, halfw - xstep] ret[row, height + width - 2 - j] = data[halfh + ystep, halfw + xstep] ret[row, height + width + height - 3 + j] = data[halfh - ystep, halfw + xstep] ret[row, perimeter - j] = data[halfh - ystep, halfw - xstep] else: break return ret def polar_to_cartesian(data: np.ndarray, width: int, height: int) -> np.ndarray: assert (width > 2) assert (height > 2) assert (width % 2 == 1) assert (height % 2 == 1) perimeter = 2 * (width + height - 2) halfdiag = math.ceil(((width ** 2 + height ** 2) ** 0.5) / 2) halfw = width // 2 halfh = height // 2 ret = np.zeros((height, width, 3)) def div0(): ret[halfh, halfw::-1] = data[0:(halfw + 1), halfh] ret[halfh, halfw:(halfw * 2 + 1)] = data[0:(halfw + 1), height + width - 2 + halfh] ret[halfh:(halfh * 2 + 1), halfw] = data[0:(halfh + 1), height - 1 + halfw] ret[halfh::-1, halfw] = data[0:(halfh + 1), perimeter - halfw] div0() def part1(): for i in range(0, halfh): slope = (halfh - i) / (halfw) diagx = ((halfdiag ** 2) / (slope ** 2 + 1)) ** 0.5 unit_xstep = diagx / (halfdiag - 1) unit_ystep = diagx * slope / (halfdiag - 1) for row in range(halfdiag): ystep = round(row * unit_ystep) xstep = round(row * unit_xstep) if ((halfh >= ystep) and halfw >= xstep): ret[halfh - ystep, halfw - xstep] = \ data[row, i] ret[halfh + ystep, halfw - xstep] = \ data[row, height - 1 - i] ret[halfh + ystep, halfw + xstep] = \ data[row, height + width - 2 + i] ret[halfh - ystep, halfw + xstep] = \ data[row, height + width + height - 3 - i] else: break part1() def part2(): for j in range(1, halfw): slope = (halfh) / (halfw - j) diagx = ((halfdiag ** 2) / (slope ** 2 + 1)) ** 0.5 unit_xstep = diagx / (halfdiag - 1) unit_ystep = diagx * slope / (halfdiag - 1) for row in range(halfdiag): ystep = round(row * unit_ystep) xstep = round(row * unit_xstep) if (halfw >= xstep and halfh >= ystep): ret[halfh + ystep, halfw - xstep] = \ data[row, height - 1 + j] ret[halfh + ystep, halfw + xstep] = \ data[row, height + width - 2 - j] ret[halfh - ystep, halfw + xstep] = \ data[row, height + width + height - 3 + j] ret[halfh - ystep, halfw - xstep] = \ data[row, perimeter - j] else: break part2() def set_zeros(): zero_mask = ret[1:-1, 1:-1] == 0 ret[1:-1, 1:-1] = np.where(zero_mask, (ret[:-2, 1:-1] + ret[2:, 1:-1]) / 2, ret[1:-1, 1:-1]) set_zeros() return ret def get_gauss(n: int) -> List[float]: sigma = 0.3 * (n / 2 - 1) + 0.8 r = range(-int(n / 2), int(n / 2) + 1) new_sum = sum([1 / (sigma * math.sqrt(2 * math.pi)) * math.exp(-float(x) ** 2 / (2 * sigma ** 2)) for x in r]) return [(1 / (sigma * math.sqrt(2 * math.pi)) * math.exp(-float(x) ** 2 / (2 * sigma ** 2))) / new_sum for x in r] def vertical_gaussian(data: np.ndarray, n: int) -> np.ndarray: padding = n - 1 width = data.shape[1] height = data.shape[0] padded_data = np.zeros((height + padding * 2, width)) padded_data[padding: -padding, :] = data ret = np.zeros((height, width)) kernel = None old_radius = - 1 for i in range(height): radius = round(i * padding / (height - 1)) + 1 if (radius != old_radius): old_radius = radius kernel = np.tile(get_gauss(1 + 2 * (radius - 1)), (width, 1)).transpose() ret[i, :] = np.sum(np.multiply( padded_data[padding + i - radius + 1:padding + i + radius, :], kernel), axis=0) return ret r, g, b = im.split() rdata = np.asarray(r) gdata = np.asarray(g) bdata = np.asarray(b) if no_blur: rfinal = r gfinal = g bfinal = b else: poles = cartesian_to_polar(np.stack([rdata, gdata, bdata], axis=-1)) rpolar, gpolar, bpolar = poles[:, :, 0], poles[:, :, 1], poles[:, :, 2], bluramount = (im.size[0] + im.size[1] - 2) / 100 * strength if round(bluramount) > 0: rpolar = vertical_gaussian(rpolar, round(bluramount)) gpolar = vertical_gaussian(gpolar, round(bluramount * 1.2)) bpolar = vertical_gaussian(bpolar, round(bluramount * 1.4)) rgbpolar = np.stack([rpolar, gpolar, bpolar], axis=-1) cartes = polar_to_cartesian( rgbpolar, width=rdata.shape[1], height=rdata.shape[0]) rcartes, gcartes, bcartes = cartes[:, :, 0], cartes[:, :, 1], cartes[:, :, 2], rfinal = Image.fromarray(np.uint8(rcartes), 'L') gfinal = Image.fromarray(np.uint8(gcartes), 'L') bfinal = Image.fromarray(np.uint8(bcartes), 'L') gfinal = gfinal.resize((round((1 + 0.018 * strength) * rdata.shape[1]), round((1 + 0.018 * strength) * rdata.shape[0])), Image.ANTIALIAS) bfinal = bfinal.resize((round((1 + 0.044 * strength) * rdata.shape[1]), round((1 + 0.044 * strength) * rdata.shape[0])), Image.ANTIALIAS) rwidth, rheight = rfinal.size gwidth, gheight = gfinal.size bwidth, bheight = bfinal.size rhdiff = (bheight - rheight) // 2 rwdiff = (bwidth - rwidth) // 2 ghdiff = (bheight - gheight) // 2 gwdiff = (bwidth - gwidth) // 2 im = Image.merge("RGB", ( rfinal.crop((-rwdiff, -rhdiff, bwidth - rwdiff, bheight - rhdiff)), gfinal.crop((-gwdiff, -ghdiff, bwidth - gwdiff, bheight - ghdiff)), bfinal)) return im.crop((rwdiff, rhdiff, rwidth + rwdiff, rheight + rhdiff)) def tilt_shift(im, dof=60, focus_height=None): above_focus, below_focus = im[:focus_height, :], im[focus_height:, :] above_focus = increasing_blur(above_focus[::-1, ...], dof)[::-1, ...] below_focus = increasing_blur(below_focus, dof) out = np.vstack((above_focus, below_focus)) return out def increasing_blur(im, dof=60): blur_region = cv2.GaussianBlur(im[dof:, :], ksize=(15, 15), sigmaX=0) if blur_region.shape[0] > dof: blur_region = increasing_blur(blur_region, dof) blend_col = np.linspace(1.0, 0, num=dof) blend_mask = np.tile(blend_col, (im.shape[1], 1)).T res = np.zeros_like(im) res[:dof, :] = im[:dof, :] dof_actual = min(dof, im.shape[0] - dof, blur_region.shape[0]) blend_mask = blend_mask[:dof_actual, :] res[dof:dof + dof_actual, :] = im[dof:dof + dof_actual, :] * blend_mask[:, :, None] + blur_region[:dof_actual, :] * (1 - blend_mask[:, :, None]) if dof + dof < im.shape[0]: res[dof + dof_actual:, :] = blur_region[dof_actual:] return res class Script(scripts.Script): def title(self): return 'Revision' def show(self, is_img2img): return scripts.AlwaysVisible def ui(self, is_img2img): with gr.Accordion('Revision', open=False): with gr.Tab(label='Options', id=1): enabled = gr.Checkbox(label="Enable") clearEXIFCheckbox = gr.Checkbox(label="Clear EXIF (all metadata)") flipImageCheckbox = gr.Checkbox(label="Flip image") dontShowOriginalCheckbox = gr.Checkbox(label="Don't show original image") with gr.Tab(label='Adjustments', id=2): saturationSlider = gr.Slider(0, 2, 1, label='Saturation') temperatureSlider = gr.Slider(0, 2, 1, label='Temperature') brightnessSlider = gr.Slider(0, 2, 1, label='Brightness') contrastSlider = gr.Slider(0, 2, 1, label='Contrast') sharpnessSlider = gr.Slider(0, 1, 0, label='Sharpness') blurSlider = gr.Slider(0, 1, 0, label='Blur') noiseSlider = gr.Slider(0, 1, 0, label='Noise') vignetteSlider = gr.Slider(0, 1, 0, step=.05, label='Vignette') exposureOffsetSlider = gr.Slider(0, 1, 0, step=.05, label='Exposure offset') hdrSlider = gr.Slider(0, 1, 0, label='HDR') bestChoiceButton = gr.Button(value="Best Choice") bestChoiceButton.click(bestChoiceValues, inputs=[saturationSlider, temperatureSlider, brightnessSlider, contrastSlider, sharpnessSlider, blurSlider, noiseSlider, vignetteSlider, exposureOffsetSlider, hdrSlider], outputs=[saturationSlider, temperatureSlider, brightnessSlider, contrastSlider, sharpnessSlider, blurSlider, noiseSlider, vignetteSlider, exposureOffsetSlider, hdrSlider]) resetSlidersButton = gr.Button(value="Reset Sliders") resetSlidersButton.click(resetValues, inputs=[saturationSlider, temperatureSlider, brightnessSlider, contrastSlider, sharpnessSlider, blurSlider, noiseSlider, vignetteSlider, exposureOffsetSlider, hdrSlider], outputs=[saturationSlider, temperatureSlider, brightnessSlider, contrastSlider, sharpnessSlider, blurSlider, noiseSlider, vignetteSlider, exposureOffsetSlider, hdrSlider]) with gr.Tab(label='Effects', id=3): lensDistortionRadioButton = gr.Radio(["None", "Lens Distortion", "Fish Eye"], label="Lens effect", value="None") chromaticAberrationSlider = gr.Slider(0, 1, 0, label='Chromatic aberration') snowfallSlider = gr.Slider(0, 3000, 0, step=1, label='Snowfall') asciiSlider = gr.Slider(0, 20, 0, step=1, label='ASCII') tiltShiftRadioButton = gr.Radio(["None", "Top", "Center", "Bottom"], label="Tilt Shift", value="None") glitchCheckbox = gr.Checkbox(label="Glitch") vhsCheckbox = gr.Checkbox(label="VHS") watermark = gr.Textbox(label="Watermark text") with gr.Tab(label='Custom EXIF', id=4): customEXIF = gr.TextArea( label="Here you can fill in your custom EXIF") return [enabled, saturationSlider, temperatureSlider, brightnessSlider, contrastSlider, sharpnessSlider, blurSlider, noiseSlider, vignetteSlider, exposureOffsetSlider, hdrSlider, clearEXIFCheckbox, flipImageCheckbox, dontShowOriginalCheckbox, lensDistortionRadioButton, chromaticAberrationSlider, customEXIF, tiltShiftRadioButton, glitchCheckbox, vhsCheckbox, snowfallSlider, asciiSlider, watermark] def postprocess(self, p, processed, enabled, saturationSlider, temperatureSlider, brightnessSlider, contrastSlider, sharpnessSlider, blurSlider, noiseSlider, vignetteSlider, exposureOffsetSlider, hdrSlider, clearEXIFCheckbox, flipImageCheckbox, dontShowOriginalCheckbox, lensDistortionRadioButton, chromaticAberrationSlider, customEXIF, tiltShiftRadioButton, glitchCheckbox, vhsCheckbox, snowfallSlider, asciiSlider, watermark): if not enabled: return proc = processed result = [] for i in range(len(proc.images)): image = proc.images[i] img = ImageEnhance.Color(image).enhance(saturationSlider) img = ImageEnhance.Brightness(img).enhance(brightnessSlider) img = ImageEnhance.Contrast(img).enhance(contrastSlider) if vignetteSlider > 0: width, height = img.size mask = Image.new("L", (width, height), 0) draw = ImageDraw.Draw(mask) padding = 100 - vignetteSlider * 100 draw.ellipse((-padding, -padding, width + padding, height + padding), fill=255) mask = mask.filter(ImageFilter.GaussianBlur(radius=100)) img = Image.composite(img, Image.new( "RGB", img.size, "black"), mask) if hdrSlider > 0: blurred = img.filter(ImageFilter.GaussianBlur(radius=2.8)) difference = ImageChops.difference(img, blurred) sharpEdges = Image.blend(img, difference, 1) convertedOriginalImage = np.array( image)[:, :, ::-1].copy().astype('float32') / 255.0 convertedSharped = np.array( sharpEdges)[:, :, ::-1].copy().astype('float32') / 255.0 colorDodge = convertedOriginalImage / (1 - convertedSharped) convertedColorDodge = ( 255 * colorDodge).clip(0, 255).astype(np.uint8) tempImage = Image.fromarray(cv2.cvtColor( convertedColorDodge, cv2.COLOR_BGR2RGB)) invertedColorDodge = ImageOps.invert(tempImage) blackWhiteColorDodge = ImageEnhance.Color( invertedColorDodge).enhance(0) hue = blendLayers(tempImage, blackWhiteColorDodge, BlendType.HUE) hdrImage = blendLayers(hue, tempImage, BlendType.NORMAL, .7) img = blendLayers(img, hdrImage, BlendType.NORMAL, hdrSlider * 2).convert("RGB") if sharpnessSlider > 0: img = ImageEnhance.Sharpness(img).enhance( (sharpnessSlider + 1) * 1.5) if blurSlider > 0: img = img.filter(ImageFilter.BoxBlur(blurSlider * 10)) if temperatureSlider != 1: pixels = img.load() for i in range(img.width): for j in range(img.height): (r, g, b) = pixels[i, j] if temperatureSlider > 1: r *= 1 + ((temperatureSlider - 1) / 4) b *= 1 - (((temperatureSlider - 1) / 4)) else: r *= 1 - (1 - temperatureSlider) / 4 b *= 1 + (((1 - temperatureSlider) / 4)) pixels[i, j] = (int(r), int(g), int(b)) if noiseSlider > 0: noise = np.random.randint(0, noiseSlider * 100, img.size, np.uint8) noise_img = Image.fromarray(noise, 'L').resize( img.size).convert(img.mode) img = ImageChops.add(img, noise_img) if exposureOffsetSlider > 0: np_img = np.array(img).astype(float) + exposureOffsetSlider * 75 np_img = np.clip(np_img, 0, 255).astype(np.uint8) img = Image.fromarray(np_img) img = ImageEnhance.Brightness(img).enhance( brightnessSlider - exposureOffsetSlider / 4) if flipImageCheckbox: img = Image.fromarray(np.fliplr(np.array(img))) if lensDistortionRadioButton != "None": def add_lens_distortion(img, k1, k2): img = np.array(img)[:, :, ::-1].copy() rows, cols = img.shape[:2] map_x, map_y = np.zeros((rows, cols), np.float32), np.zeros( (rows, cols), np.float32) for i in range(rows): for j in range(cols): r = np.sqrt((i - rows/2)**2 + (j - cols/2)**2) x = j + (j - cols/2) * (k1 * r**2 + k2 * r**4) y = i + (i - rows/2) * (k1 * r**2 + k2 * r**4) if x >= 0 and x < cols and y >= 0 and y < rows: map_x[i, j] = x map_y[i, j] = y return cv2.remap(img, map_x, map_y, cv2.INTER_LINEAR) if lensDistortionRadioButton == "Lens Distortion": img = add_lens_distortion(img, 1e-12, -1e-12) else: img = add_lens_distortion(img, 1e-12, 1e-12) img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) if chromaticAberrationSlider > 0: img = add_chromatic(img, chromaticAberrationSlider + .12, True) if tiltShiftRadioButton != "None": width, height = img.size ratio = 1/5 if tiltShiftRadioButton == "Top" else 1 / \ 2 if tiltShiftRadioButton == "Center" else 4/5 img = Image.fromarray(cv2.cvtColor(tilt_shift(np.array( img)[:, :, ::-1].copy(), 60, round(height * ratio)), cv2.COLOR_BGR2RGB)) if glitchCheckbox: img = np.array(img)[:, :, ::-1].copy() num_glitches = 5 height, width = img.shape[:2] for _ in range(num_glitches): y = np.random.randint(height) h = np.random.randint(10, 50) y1 = np.clip(y - h // 2, 0, height) y2 = np.clip(y + h // 2, 0, height) w = np.random.randint(20, width // 4) channel = np.random.randint(0, 3) img[y1:y2, w:, channel] = img[y1:y2, :-w, channel] img[y1:y2, :w, channel] = np.random.randint(0, 256, (y2 - y1, w), dtype=np.uint8) img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) if vhsCheckbox: # Коррекция насыщености, яркости и контрастности img = ImageEnhance.Color(img).enhance(0.88) img = ImageEnhance.Brightness(img).enhance(1.06) img = ImageEnhance.Contrast(img).enhance(0.88) # Цветной шум noise = np.random.normal(loc=128, scale=128, size=img.size[::-1] + (3,)).clip(0, 255).astype(np.uint8) dust_and_scratches = Image.fromarray(noise, 'RGB').filter(ImageFilter.GaussianBlur(1)) img = Image.blend(img, dust_and_scratches, alpha=0.02) # Размытие в движении img = np.array(img)[:, :, ::-1].copy() size = 4 kernel = np.zeros((size, size)) kernel[int((size-1)/2), :] = np.ones(size) kernel = kernel / size img = cv2.filter2D(img, -1, kernel) img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) # Резкость img = ImageEnhance.Sharpness(img).enhance((1.2)) # Тиснение img = blendLayers(img, img.filter(ImageFilter.EMBOSS()), BlendType.HARDLIGHT, 1.8) # Glitch от плёнки img = np.array(img)[:, :, ::-1].copy() num_glitches = 5 height, width = img.shape[:2] for _ in range(num_glitches): y = np.random.randint(height) h = np.random.randint(1, 3) y1 = np.clip(y - h // 2, 0, height) y2 = np.clip(y + h // 2, 0, height) w = np.random.randint(20, width // 4) channel = np.random.randint(0, 3) img[y1:y2, w:, channel] = img[y1:y2, :-w, channel] img[y1:y2, :w, channel] = np.random.randint(100, 156, (y2 - y1, w), dtype=np.uint8) img = Image.fromarray(img[:, :, ::-1]) if snowfallSlider > 0: img = np.array(img)[:, :, ::-1].copy() height, width = img.shape[:2] num_snowflakes = snowfallSlider first_snow_layer = np.zeros_like(img) second_snow_layer = np.zeros_like(img) for _ in range(num_snowflakes): center_x, center_y = random.randint(0, width - 1), random.randint(0, height - 1) num_vertices = random.randint(3, 6) radius = random.randint(1, 3) polygon = np.array([[ center_x + random.randint(-radius, radius), center_y + random.randint(-radius, radius) ] for _ in range(num_vertices)], np.int32) polygon = polygon.reshape((-1, 1, 2)) blur = random.choice([True, False]) if blur: cv2.fillPoly(second_snow_layer, [polygon], (255, 255, 255)) else: cv2.fillPoly(first_snow_layer, [polygon], (255, 255, 255)) first_snow_layer = cv2.GaussianBlur(first_snow_layer, (5, 5), 0) second_snow_layer = cv2.GaussianBlur(second_snow_layer, (15, 15), 0) snowy_img = cv2.addWeighted(img, 1, first_snow_layer, 1, 0) img = cv2.addWeighted(snowy_img, 1, second_snow_layer, 1, 0) img = Image.fromarray(img[:, :, ::-1]) if asciiSlider > 0: chars = " .'`^\",:;I1!i><-+_-?][}{1)(|\/tfjrxnuvczXYUCLQ0OZmwqpbdkhao*#MW&8%B@$" small_image = img.resize((img.width // asciiSlider, img.height // asciiSlider), Image.Resampling.NEAREST) ascii_image = Image.new('RGB', img.size, 'black') font = ImageFont.truetype("arial.ttf", asciiSlider) draw = ImageDraw.Draw(ascii_image) for i in range(small_image.height): for j in range(small_image.width): pixel = small_image.getpixel((j, i)) gray = sum(pixel) // 3 char = chars[gray * len(chars) // 256] draw.text((j * asciiSlider, i * asciiSlider), char, font=font, fill=pixel) img = ascii_image if len(watermark) > 0: tempImg = Image.new('RGBA', (img.width, img.height), (0, 0, 0, 0)) draw = ImageDraw.Draw(tempImg) userText = watermark.upper() textSize = round(img.width / 5) font = ImageFont.truetype('impact.ttf', textSize) text_width, text_height = draw.textsize(userText, font) right = (img.width - text_width) - 35 bottom = (img.height - text_height) - img.height / 3 shadowcolor = (111, 0, 0) draw.text((right + (textSize / 48), bottom + (textSize / 48)), userText, font=font, fill=shadowcolor) textcolor = (20, 25, 30) draw.text((right, bottom), userText, font=font, fill=textcolor) tempImg = tempImg.transform(tempImg.size, Image.AFFINE, ( 1, 0, 0, 0.1, 1, 0), resample=Image.BICUBIC, fillcolor=(0, 0, 0, 0)) img_arr = np.array(tempImg) mask = np.random.randint( 0, 2, size=img_arr.shape[:2]).astype(bool) mask = np.repeat(mask[:, :, np.newaxis], 4, axis=2) img_arr[mask] = img_arr[np.roll(mask, 5, axis=1)] tempImg = Image.fromarray(img_arr) img = blendLayers(img, tempImg, BlendType.NORMAL, .44) if not clearEXIFCheckbox: img.info['parameters'] = proc.info if len(customEXIF) > 0: img.info['parameters'] = customEXIF result.append(img) if dontShowOriginalCheckbox: proc.images.clear() for i in result: proc.images.append(i) try: images.save_image(i, p.outpath_samples, "", info=i.info['parameters']) except: images.save_image(i, p.outpath_samples, "", info='') return Processed(p, proc.images, p.seed, '')