Merge pull request #14121 from AUTOMATIC1111/fix-Auto-focal-point-crop-for-opencv-4.8.x

Fix auto focal point crop for opencv >= 4.8

modules/textual_inversion/autocrop.py

@@ -3,6 +3,8 @@ import requests
 import os
 import numpy as np
 from PIL import ImageDraw
+from modules import paths_internal
+from pkg_resources import parse_version
 
 GREEN = "#0F0"
 BLUE = "#00F"
@@ -25,7 +27,6 @@ def crop_image(im, settings):
         elif is_portrait(settings.crop_width, settings.crop_height):
             scale_by = settings.crop_height / im.height
 
-
     im = im.resize((int(im.width * scale_by), int(im.height * scale_by)))
     im_debug = im.copy()
 
@@ -69,6 +70,7 @@ def crop_image(im, settings):
 
     return results
 
+
 def focal_point(im, settings):
     corner_points = image_corner_points(im, settings) if settings.corner_points_weight > 0 else []
     entropy_points = image_entropy_points(im, settings) if settings.entropy_points_weight > 0 else []
@@ -110,7 +112,7 @@ def focal_point(im, settings):
         if corner_centroid is not None:
             color = BLUE
             box = corner_centroid.bounding(max_size * corner_centroid.weight)
-        d.text((box[0], box[1]-15), f"Edge: {corner_centroid.weight:.02f}", fill=color)
+            d.text((box[0], box[1] - 15), f"Edge: {corner_centroid.weight:.02f}", fill=color)
             d.ellipse(box, outline=color)
             if len(corner_points) > 1:
                 for f in corner_points:
@@ -118,7 +120,7 @@ def focal_point(im, settings):
         if entropy_centroid is not None:
             color = "#ff0"
             box = entropy_centroid.bounding(max_size * entropy_centroid.weight)
-        d.text((box[0], box[1]-15), f"Entropy: {entropy_centroid.weight:.02f}", fill=color)
+            d.text((box[0], box[1] - 15), f"Entropy: {entropy_centroid.weight:.02f}", fill=color)
             d.ellipse(box, outline=color)
             if len(entropy_points) > 1:
                 for f in entropy_points:
@@ -126,7 +128,7 @@ def focal_point(im, settings):
         if face_centroid is not None:
             color = RED
             box = face_centroid.bounding(max_size * face_centroid.weight)
-        d.text((box[0], box[1]-15), f"Face: {face_centroid.weight:.02f}", fill=color)
+            d.text((box[0], box[1] - 15), f"Face: {face_centroid.weight:.02f}", fill=color)
             d.ellipse(box, outline=color)
             if len(face_points) > 1:
                 for f in face_points:
@@ -159,8 +161,8 @@ def image_face_points(im, settings):
                     PointOfInterest(
                         int(x + (w * 0.5)),  # face focus left/right is center
                         int(y + (h * 0.33)),  # face focus up/down is close to the top of the head
-              size = w,
-              weight = 1/len(faces[1])
+                        size=w,
+                        weight=1 / len(faces[1])
                     )
                 )
         return results
@@ -169,27 +171,29 @@ def image_face_points(im, settings):
         gray = cv2.cvtColor(np_im, cv2.COLOR_BGR2GRAY)
 
         tries = [
-        [ f'{cv2.data.haarcascades}haarcascade_eye.xml', 0.01 ],
-        [ f'{cv2.data.haarcascades}haarcascade_frontalface_default.xml', 0.05 ],
-        [ f'{cv2.data.haarcascades}haarcascade_profileface.xml', 0.05 ],
-        [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt.xml', 0.05 ],
-        [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt2.xml', 0.05 ],
-        [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt_tree.xml', 0.05 ],
-        [ f'{cv2.data.haarcascades}haarcascade_eye_tree_eyeglasses.xml', 0.05 ],
-        [ f'{cv2.data.haarcascades}haarcascade_upperbody.xml', 0.05 ]
+            [f'{cv2.data.haarcascades}haarcascade_eye.xml', 0.01],
+            [f'{cv2.data.haarcascades}haarcascade_frontalface_default.xml', 0.05],
+            [f'{cv2.data.haarcascades}haarcascade_profileface.xml', 0.05],
+            [f'{cv2.data.haarcascades}haarcascade_frontalface_alt.xml', 0.05],
+            [f'{cv2.data.haarcascades}haarcascade_frontalface_alt2.xml', 0.05],
+            [f'{cv2.data.haarcascades}haarcascade_frontalface_alt_tree.xml', 0.05],
+            [f'{cv2.data.haarcascades}haarcascade_eye_tree_eyeglasses.xml', 0.05],
+            [f'{cv2.data.haarcascades}haarcascade_upperbody.xml', 0.05]
         ]
         for t in tries:
             classifier = cv2.CascadeClassifier(t[0])
             minsize = int(min(im.width, im.height) * t[1])  # at least N percent of the smallest side
             try:
                 faces = classifier.detectMultiScale(gray, scaleFactor=1.1,
-            minNeighbors=7, minSize=(minsize, minsize), flags=cv2.CASCADE_SCALE_IMAGE)
+                                                    minNeighbors=7, minSize=(minsize, minsize),
+                                                    flags=cv2.CASCADE_SCALE_IMAGE)
             except Exception:
                 continue
 
             if faces:
                 rects = [[f[0], f[1], f[0] + f[2], f[1] + f[3]] for f in faces]
-          return [PointOfInterest((r[0] +r[2]) // 2, (r[1] + r[3]) // 2, size=abs(r[0]-r[2]), weight=1/len(rects)) for r in rects]
+                return [PointOfInterest((r[0] + r[2]) // 2, (r[1] + r[3]) // 2, size=abs(r[0] - r[2]),
+                                        weight=1 / len(rects)) for r in rects]
     return []
 
 
@@ -198,7 +202,7 @@ def image_corner_points(im, settings):
 
     # 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")
+    gd.rectangle([0, im.height * .9, im.width, im.height], fill="#999")
 
     np_im = np.array(grayscale)
 
@@ -206,7 +210,7 @@ def image_corner_points(im, settings):
         np_im,
         maxCorners=100,
         qualityLevel=0.04,
-        minDistance=min(grayscale.width, grayscale.height)*0.06,
+        minDistance=min(grayscale.width, grayscale.height) * 0.06,
         useHarrisDetector=False,
     )
 
@@ -216,7 +220,7 @@ def image_corner_points(im, settings):
     focal_points = []
     for point in points:
         x, y = point.ravel()
-      focal_points.append(PointOfInterest(x, y, size=4, weight=1/len(points)))
+        focal_points.append(PointOfInterest(x, y, size=4, weight=1 / len(points)))
 
     return focal_points
 
@@ -247,8 +251,8 @@ def image_entropy_points(im, settings):
         crop_current[move_idx[0]] += 4
         crop_current[move_idx[1]] += 4
 
-    x_mid = int(crop_best[0] + settings.crop_width/2)
-    y_mid = int(crop_best[1] + settings.crop_height/2)
+    x_mid = int(crop_best[0] + settings.crop_width / 2)
+    y_mid = int(crop_best[1] + settings.crop_height / 2)
 
     return [PointOfInterest(x_mid, y_mid, size=25, weight=1.0)]
 
@@ -294,22 +298,23 @@ def is_square(w, h):
     return w == h
 
 
-def download_and_cache_models(dirname):
-    download_url = 'https://github.com/opencv/opencv_zoo/blob/91fb0290f50896f38a0ab1e558b74b16bc009428/models/face_detection_yunet/face_detection_yunet_2022mar.onnx?raw=true'
-    model_file_name = 'face_detection_yunet.onnx'
+model_dir_opencv = os.path.join(paths_internal.models_path, 'opencv')
+if parse_version(cv2.__version__) >= parse_version('4.8'):
+    model_file_path = os.path.join(model_dir_opencv, 'face_detection_yunet_2023mar.onnx')
+    model_url = 'https://github.com/opencv/opencv_zoo/blob/b6e370b10f641879a87890d44e42173077154a05/models/face_detection_yunet/face_detection_yunet_2023mar.onnx?raw=true'
+else:
+    model_file_path = os.path.join(model_dir_opencv, 'face_detection_yunet.onnx')
+    model_url = 'https://github.com/opencv/opencv_zoo/blob/91fb0290f50896f38a0ab1e558b74b16bc009428/models/face_detection_yunet/face_detection_yunet_2022mar.onnx?raw=true'
 
-    os.makedirs(dirname, exist_ok=True)
 
-    cache_file = os.path.join(dirname, model_file_name)
-    if not os.path.exists(cache_file):
-        print(f"downloading face detection model from '{download_url}' to '{cache_file}'")
-        response = requests.get(download_url)
-        with open(cache_file, "wb") as f:
+def download_and_cache_models():
+    if not os.path.exists(model_file_path):
+        os.makedirs(model_dir_opencv, exist_ok=True)
+        print(f"downloading face detection model from '{model_url}' to '{model_file_path}'")
+        response = requests.get(model_url)
+        with open(model_file_path, "wb") as f:
             f.write(response.content)
-
-    if os.path.exists(cache_file):
-        return cache_file
-    return None
+    return model_file_path
 
 
 class PointOfInterest:

modules/textual_inversion/preprocess.py

@@ -3,7 +3,7 @@ from PIL import Image, ImageOps
 import math
 import tqdm
 
-from modules import paths, shared, images, deepbooru
+from modules import shared, images, deepbooru
 from modules.textual_inversion import autocrop
 
 
@@ -196,7 +196,7 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre
 
             dnn_model_path = None
             try:
-                dnn_model_path = autocrop.download_and_cache_models(os.path.join(paths.models_path, "opencv"))
+                dnn_model_path = autocrop.download_and_cache_models()
             except Exception as e:
                 print("Unable to load face detection model for auto crop selection. Falling back to lower quality haar method.", e)