6aed2bb402
- max_train_epochs - max_data_loader_n_workers Move some of the codeto common gui library.
240 lines
10 KiB
Python
240 lines
10 KiB
Python
# このスクリプトのライセンスは、train_dreambooth.pyと同じくApache License 2.0とします
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# (c) 2022 Kohya S. @kohya_ss
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# 横長の画像から顔検出して正立するように回転し、そこを中心に正方形に切り出す
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# v2: extract max face if multiple faces are found
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# v3: add crop_ratio option
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# v4: add multiple faces extraction and min/max size
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import argparse
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import math
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import cv2
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import glob
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import os
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from anime_face_detector import create_detector
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from tqdm import tqdm
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import numpy as np
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KP_REYE = 11
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KP_LEYE = 19
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SCORE_THRES = 0.90
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def detect_faces(detector, image, min_size):
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preds = detector(image) # bgr
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# print(len(preds))
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faces = []
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for pred in preds:
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bb = pred['bbox']
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score = bb[-1]
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if score < SCORE_THRES:
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continue
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left, top, right, bottom = bb[:4]
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cx = int((left + right) / 2)
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cy = int((top + bottom) / 2)
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fw = int(right - left)
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fh = int(bottom - top)
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lex, ley = pred['keypoints'][KP_LEYE, 0:2]
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rex, rey = pred['keypoints'][KP_REYE, 0:2]
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angle = math.atan2(ley - rey, lex - rex)
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angle = angle / math.pi * 180
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faces.append((cx, cy, fw, fh, angle))
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faces.sort(key=lambda x: max(x[2], x[3]), reverse=True) # 大きい順
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return faces
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def rotate_image(image, angle, cx, cy):
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h, w = image.shape[0:2]
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rot_mat = cv2.getRotationMatrix2D((cx, cy), angle, 1.0)
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# # 回転する分、すこし画像サイズを大きくする→とりあえず無効化
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# nh = max(h, int(w * math.sin(angle)))
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# nw = max(w, int(h * math.sin(angle)))
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# if nh > h or nw > w:
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# pad_y = nh - h
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# pad_t = pad_y // 2
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# pad_x = nw - w
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# pad_l = pad_x // 2
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# m = np.array([[0, 0, pad_l],
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# [0, 0, pad_t]])
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# rot_mat = rot_mat + m
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# h, w = nh, nw
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# cx += pad_l
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# cy += pad_t
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result = cv2.warpAffine(image, rot_mat, (w, h), flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT)
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return result, cx, cy
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def process(args):
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assert (not args.resize_fit) or args.resize_face_size is None, f"resize_fit and resize_face_size can't be specified both / resize_fitとresize_face_sizeはどちらか片方しか指定できません"
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assert args.crop_ratio is None or args.resize_face_size is None, f"crop_ratio指定時はresize_face_sizeは指定できません"
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# アニメ顔検出モデルを読み込む
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print("loading face detector.")
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detector = create_detector('yolov3')
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# cropの引数を解析する
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if args.crop_size is None:
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crop_width = crop_height = None
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else:
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tokens = args.crop_size.split(',')
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assert len(tokens) == 2, f"crop_size must be 'width,height' / crop_sizeは'幅,高さ'で指定してください"
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crop_width, crop_height = [int(t) for t in tokens]
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if args.crop_ratio is None:
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crop_h_ratio = crop_v_ratio = None
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else:
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tokens = args.crop_ratio.split(',')
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assert len(tokens) == 2, f"crop_ratio must be 'horizontal,vertical' / crop_ratioは'幅,高さ'の倍率で指定してください"
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crop_h_ratio, crop_v_ratio = [float(t) for t in tokens]
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# 画像を処理する
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print("processing.")
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output_extension = ".png"
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os.makedirs(args.dst_dir, exist_ok=True)
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paths = glob.glob(os.path.join(args.src_dir, "*.png")) + glob.glob(os.path.join(args.src_dir, "*.jpg")) + \
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glob.glob(os.path.join(args.src_dir, "*.webp"))
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for path in tqdm(paths):
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basename = os.path.splitext(os.path.basename(path))[0]
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# image = cv2.imread(path) # 日本語ファイル名でエラーになる
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image = cv2.imdecode(np.fromfile(path, np.uint8), cv2.IMREAD_UNCHANGED)
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if len(image.shape) == 2:
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image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
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if image.shape[2] == 4:
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print(f"image has alpha. ignore / 画像の透明度が設定されているため無視します: {path}")
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image = image[:, :, :3].copy() # copyをしないと内部的に透明度情報が付いたままになるらしい
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h, w = image.shape[:2]
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faces = detect_faces(detector, image, args.multiple_faces)
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for i, face in enumerate(faces):
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cx, cy, fw, fh, angle = face
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face_size = max(fw, fh)
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if args.min_size is not None and face_size < args.min_size:
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continue
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if args.max_size is not None and face_size >= args.max_size:
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continue
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face_suffix = f"_{i+1:02d}" if args.multiple_faces else ""
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# オプション指定があれば回転する
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face_img = image
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if args.rotate:
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face_img, cx, cy = rotate_image(face_img, angle, cx, cy)
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# オプション指定があれば顔を中心に切り出す
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if crop_width is not None or crop_h_ratio is not None:
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cur_crop_width, cur_crop_height = crop_width, crop_height
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if crop_h_ratio is not None:
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cur_crop_width = int(face_size * crop_h_ratio + .5)
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cur_crop_height = int(face_size * crop_v_ratio + .5)
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# リサイズを必要なら行う
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scale = 1.0
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if args.resize_face_size is not None:
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# 顔サイズを基準にリサイズする
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scale = args.resize_face_size / face_size
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if scale < cur_crop_width / w:
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print(
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f"image width too small in face size based resizing / 顔を基準にリサイズすると画像の幅がcrop sizeより小さい（顔が相対的に大きすぎる）ので顔サイズが変わります: {path}")
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scale = cur_crop_width / w
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if scale < cur_crop_height / h:
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print(
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f"image height too small in face size based resizing / 顔を基準にリサイズすると画像の高さがcrop sizeより小さい（顔が相対的に大きすぎる）ので顔サイズが変わります: {path}")
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scale = cur_crop_height / h
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elif crop_h_ratio is not None:
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# 倍率指定の時にはリサイズしない
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pass
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else:
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# 切り出しサイズ指定あり
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if w < cur_crop_width:
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print(f"image width too small/ 画像の幅がcrop sizeより小さいので画質が劣化します: {path}")
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scale = cur_crop_width / w
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if h < cur_crop_height:
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print(f"image height too small/ 画像の高さがcrop sizeより小さいので画質が劣化します: {path}")
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scale = cur_crop_height / h
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if args.resize_fit:
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scale = max(cur_crop_width / w, cur_crop_height / h)
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if scale != 1.0:
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w = int(w * scale + .5)
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h = int(h * scale + .5)
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face_img = cv2.resize(face_img, (w, h), interpolation=cv2.INTER_AREA if scale < 1.0 else cv2.INTER_LANCZOS4)
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cx = int(cx * scale + .5)
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cy = int(cy * scale + .5)
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fw = int(fw * scale + .5)
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fh = int(fh * scale + .5)
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cur_crop_width = min(cur_crop_width, face_img.shape[1])
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cur_crop_height = min(cur_crop_height, face_img.shape[0])
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x = cx - cur_crop_width // 2
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cx = cur_crop_width // 2
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if x < 0:
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cx = cx + x
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x = 0
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elif x + cur_crop_width > w:
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cx = cx + (x + cur_crop_width - w)
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x = w - cur_crop_width
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face_img = face_img[:, x:x+cur_crop_width]
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y = cy - cur_crop_height // 2
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cy = cur_crop_height // 2
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if y < 0:
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cy = cy + y
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y = 0
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elif y + cur_crop_height > h:
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cy = cy + (y + cur_crop_height - h)
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y = h - cur_crop_height
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face_img = face_img[y:y + cur_crop_height]
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# # debug
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# print(path, cx, cy, angle)
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# crp = cv2.resize(image, (image.shape[1]//8, image.shape[0]//8))
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# cv2.imshow("image", crp)
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# if cv2.waitKey() == 27:
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# break
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# cv2.destroyAllWindows()
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# debug
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if args.debug:
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cv2.rectangle(face_img, (cx-fw//2, cy-fh//2), (cx+fw//2, cy+fh//2), (255, 0, 255), fw//20)
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_, buf = cv2.imencode(output_extension, face_img)
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with open(os.path.join(args.dst_dir, f"{basename}{face_suffix}_{cx:04d}_{cy:04d}_{fw:04d}_{fh:04d}{output_extension}"), "wb") as f:
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buf.tofile(f)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument("--src_dir", type=str, help="directory to load images / 画像を読み込むディレクトリ")
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parser.add_argument("--dst_dir", type=str, help="directory to save images / 画像を保存するディレクトリ")
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parser.add_argument("--rotate", action="store_true", help="rotate images to align faces / 顔が正立するように画像を回転する")
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parser.add_argument("--resize_fit", action="store_true",
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help="resize to fit smaller side after cropping / 切り出し後の画像の短辺がcrop_sizeにあうようにリサイズする")
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parser.add_argument("--resize_face_size", type=int, default=None,
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help="resize image before cropping by face size / 切り出し前に顔がこのサイズになるようにリサイズする")
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parser.add_argument("--crop_size", type=str, default=None,
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help="crop images with 'width,height' pixels, face centered / 顔を中心として'幅,高さ'のサイズで切り出す")
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parser.add_argument("--crop_ratio", type=str, default=None,
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help="crop images with 'horizontal,vertical' ratio to face, face centered / 顔を中心として顔サイズの'幅倍率,高さ倍率'のサイズで切り出す")
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parser.add_argument("--min_size", type=int, default=None,
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help="minimum face size to output (included) / 処理対象とする顔の最小サイズ（この値以上）")
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parser.add_argument("--max_size", type=int, default=None,
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help="maximum face size to output (excluded) / 処理対象とする顔の最大サイズ（この値未満）")
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parser.add_argument("--multiple_faces", action="store_true",
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help="output each faces / 複数の顔が見つかった場合、それぞれを切り出す")
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parser.add_argument("--debug", action="store_true", help="render rect for face / 処理後画像の顔位置に矩形を描画します")
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args = parser.parse_args()
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process(args)
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