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 []
@@ -183,13 +185,15 @@ def image_face_points(im, settings):
             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 []
 
 
@@ -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)