1 files changed, 14 insertions, 136 deletions
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index 7c1a594e..0c79f012 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -1,7 +1,5 @@
 import os
-import cv2
-import numpy as np
-from PIL import Image, ImageOps, ImageDraw
+from PIL import Image, ImageOps
 import platform
 import sys
 import tqdm
@@ -9,6 +7,7 @@ import time
 
 from modules import shared, images
 from modules.shared import opts, cmd_opts
+from modules.textual_inversion import autocrop
 if cmd_opts.deepdanbooru:
     import modules.deepbooru as deepbooru
 
@@ -80,6 +79,7 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro
         if process_flip:
             save_pic_with_caption(ImageOps.mirror(image), index)
 
+
     for index, imagefile in enumerate(tqdm.tqdm(files)):
         subindex = [0]
         filename = os.path.join(src, imagefile)
@@ -118,37 +118,16 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro
             
             processing_option_ran = True
 
-        if process_entropy_focus and (is_tall or is_wide):
-            if is_tall:
-                img = img.resize((width, height * img.height // img.width))
-            else:
-                img = img.resize((width * img.width // img.height, height))
-
-            x_focal_center, y_focal_center = image_central_focal_point(img, width, height)
-
-            # take the focal point and turn it into crop coordinates that try to center over the focal
-            # point but then get adjusted back into the frame
-            y_half = int(height / 2)
-            x_half = int(width / 2)
-
-            x1 = x_focal_center - x_half
-            if x1 < 0:
-                x1 = 0
-            elif x1 + width > img.width:
-                x1 = img.width - width
-
-            y1 = y_focal_center - y_half
-            if y1 < 0:
-                y1 = 0
-            elif y1 + height > img.height:
-                y1 = img.height - height
-
-            x2 = x1 + width
-            y2 = y1 + height
-
-            crop = [x1, y1, x2, y2]
-
-            focal = img.crop(tuple(crop))
+        if process_entropy_focus and img.height != img.width:
+            autocrop_settings = autocrop.Settings(
+                crop_width = width,
+                crop_height = height,
+                face_points_weight = 0.9,
+                entropy_points_weight = 0.7,
+                corner_points_weight = 0.5,
+                annotate_image = False
+            )
+            focal = autocrop.crop_image(img, autocrop_settings)
             save_pic(focal, index)
 
             processing_option_ran = True
@@ -157,105 +136,4 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro
             img = images.resize_image(1, img, width, height)
             save_pic(img, index)
 
-        shared.state.nextjob()
-
-
-def image_central_focal_point(im, target_width, target_height):
-    focal_points = []
-
-    focal_points.extend(
-        image_focal_points(im)
-    )
-
-    fp_entropy = image_entropy_point(im, target_width, target_height)
-    fp_entropy['weight'] = len(focal_points) + 1 # about half of the weight to entropy
-
-    focal_points.append(fp_entropy)
-
-    weight = 0.0
-    x = 0.0
-    y = 0.0
-    for focal_point in focal_points:
-        weight += focal_point['weight']
-        x += focal_point['x'] * focal_point['weight']
-        y += focal_point['y'] * focal_point['weight']
-    avg_x = round(x // weight)
-    avg_y = round(y // weight)
-
-    return avg_x, avg_y
-
-
-def image_focal_points(im):
-    grayscale = im.convert("L")
-
-    # naive attempt at preventing focal points from collecting at watermarks near the bottom
-    gd = ImageDraw.Draw(grayscale)
-    gd.rectangle([0, im.height*.9, im.width, im.height], fill="#999")
-
-    np_im = np.array(grayscale)
-
-    points = cv2.goodFeaturesToTrack(
-        np_im,
-        maxCorners=100,
-        qualityLevel=0.04,
-        minDistance=min(grayscale.width, grayscale.height)*0.07,
-        useHarrisDetector=False,
-    )
-
-    if points is None:
-        return []
-
-    focal_points = []
-    for point in points:
-        x, y = point.ravel()
-        focal_points.append({
-            'x': x,
-            'y': y,
-            'weight': 1.0
-        })
-
-    return focal_points
-
-
-def image_entropy_point(im, crop_width, crop_height):
-    landscape = im.height < im.width
-    portrait = im.height > im.width
-    if landscape:
-      move_idx = [0, 2]
-      move_max = im.size[0]
-    elif portrait:
-      move_idx = [1, 3]
-      move_max = im.size[1]
-
-    e_max = 0
-    crop_current = [0, 0, crop_width, crop_height]
-    crop_best = crop_current
-    while crop_current[move_idx[1]] < move_max:
-        crop = im.crop(tuple(crop_current))
-        e = image_entropy(crop)
-
-        if (e > e_max):
-          e_max = e
-          crop_best = list(crop_current)
-
-        crop_current[move_idx[0]] += 4
-        crop_current[move_idx[1]] += 4
-
-    x_mid = int(crop_best[0] + crop_width/2)
-    y_mid = int(crop_best[1] + crop_height/2)
-
-
-    return {
-        'x': x_mid,
-        'y': y_mid,
-        'weight': 1.0
-    }
-
-
-def image_entropy(im):
-    # greyscale image entropy
-    band = np.asarray(im.convert("1"))
-    hist, _ = np.histogram(band, bins=range(0, 256))
-    hist = hist[hist > 0]
-    return -np.log2(hist / hist.sum()).sum()
-
+        shared.state.nextjob()
+\ No newline at end of file