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https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
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face detection algo, configurability, reusability
Try to move the crop in the direction of a face if it is present More internal configuration options for choosing weights of each of the algorithm's findings Move logic into its module
This commit is contained in:
captin411
@@ -1,7 +1,5 @@
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import os
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import cv2
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import numpy as np
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from PIL import Image, ImageOps, ImageDraw
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from PIL import Image, ImageOps
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import platform
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import sys
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import tqdm
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@@ -9,6 +7,7 @@ import time
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from modules import shared, images
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from modules.shared import opts, cmd_opts
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from modules.textual_inversion import autocrop
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if cmd_opts.deepdanbooru:
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import modules.deepbooru as deepbooru
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@@ -80,6 +79,7 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro
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if process_flip:
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save_pic_with_caption(ImageOps.mirror(image), index)
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for index, imagefile in enumerate(tqdm.tqdm(files)):
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subindex = [0]
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filename = os.path.join(src, imagefile)
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@@ -118,37 +118,16 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro
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processing_option_ran = True
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if process_entropy_focus and (is_tall or is_wide):
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if is_tall:
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img = img.resize((width, height * img.height // img.width))
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else:
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img = img.resize((width * img.width // img.height, height))
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x_focal_center, y_focal_center = image_central_focal_point(img, width, height)
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# take the focal point and turn it into crop coordinates that try to center over the focal
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# point but then get adjusted back into the frame
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y_half = int(height / 2)
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x_half = int(width / 2)
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x1 = x_focal_center - x_half
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if x1 < 0:
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x1 = 0
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elif x1 + width > img.width:
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x1 = img.width - width
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y1 = y_focal_center - y_half
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if y1 < 0:
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y1 = 0
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elif y1 + height > img.height:
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y1 = img.height - height
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x2 = x1 + width
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y2 = y1 + height
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crop = [x1, y1, x2, y2]
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focal = img.crop(tuple(crop))
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if process_entropy_focus and img.height != img.width:
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autocrop_settings = autocrop.Settings(
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crop_width = width,
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crop_height = height,
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face_points_weight = 0.9,
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entropy_points_weight = 0.7,
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corner_points_weight = 0.5,
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annotate_image = False
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)
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focal = autocrop.crop_image(img, autocrop_settings)
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save_pic(focal, index)
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processing_option_ran = True
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@@ -157,105 +136,4 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro
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img = images.resize_image(1, img, width, height)
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save_pic(img, index)
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shared.state.nextjob()
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def image_central_focal_point(im, target_width, target_height):
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focal_points = []
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focal_points.extend(
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image_focal_points(im)
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)
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fp_entropy = image_entropy_point(im, target_width, target_height)
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fp_entropy['weight'] = len(focal_points) + 1 # about half of the weight to entropy
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focal_points.append(fp_entropy)
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weight = 0.0
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x = 0.0
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y = 0.0
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for focal_point in focal_points:
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weight += focal_point['weight']
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x += focal_point['x'] * focal_point['weight']
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y += focal_point['y'] * focal_point['weight']
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avg_x = round(x // weight)
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avg_y = round(y // weight)
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return avg_x, avg_y
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def image_focal_points(im):
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grayscale = im.convert("L")
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# naive attempt at preventing focal points from collecting at watermarks near the bottom
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gd = ImageDraw.Draw(grayscale)
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gd.rectangle([0, im.height*.9, im.width, im.height], fill="#999")
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np_im = np.array(grayscale)
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points = cv2.goodFeaturesToTrack(
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np_im,
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maxCorners=100,
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qualityLevel=0.04,
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minDistance=min(grayscale.width, grayscale.height)*0.07,
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useHarrisDetector=False,
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)
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if points is None:
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return []
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focal_points = []
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for point in points:
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x, y = point.ravel()
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focal_points.append({
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'x': x,
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'y': y,
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'weight': 1.0
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})
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return focal_points
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def image_entropy_point(im, crop_width, crop_height):
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landscape = im.height < im.width
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portrait = im.height > im.width
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if landscape:
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move_idx = [0, 2]
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move_max = im.size[0]
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elif portrait:
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move_idx = [1, 3]
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move_max = im.size[1]
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e_max = 0
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crop_current = [0, 0, crop_width, crop_height]
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crop_best = crop_current
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while crop_current[move_idx[1]] < move_max:
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crop = im.crop(tuple(crop_current))
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e = image_entropy(crop)
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if (e > e_max):
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e_max = e
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crop_best = list(crop_current)
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crop_current[move_idx[0]] += 4
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crop_current[move_idx[1]] += 4
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x_mid = int(crop_best[0] + crop_width/2)
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y_mid = int(crop_best[1] + crop_height/2)
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return {
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'x': x_mid,
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'y': y_mid,
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'weight': 1.0
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}
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def image_entropy(im):
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# greyscale image entropy
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band = np.asarray(im.convert("1"))
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hist, _ = np.histogram(band, bins=range(0, 256))
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hist = hist[hist > 0]
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return -np.log2(hist / hist.sum()).sum()
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shared.state.nextjob()
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