import cv2 import numpy as np from insightface.utils import face_align from face_parsing.swap import swap_regions from utils import add_logo_to_image swap_options_list = [ "All face", "Age less than", "Age greater than", "All Male", "All Female", "Specific Face", ] def swap_face(whole_img, target_face, source_face, models): inswapper = models.get("swap") face_enhancer = models.get("enhance", None) face_parser = models.get("face_parser", None) fe_enable = models.get("enhance_sett", False) bgr_fake, M = inswapper.get(whole_img, target_face, source_face, paste_back=False) image_size = 128 if not fe_enable else 512 aimg, _ = face_align.norm_crop2(whole_img, target_face.kps, image_size=image_size) if face_parser is not None: fp_enable, includes, smooth_mask, blur_amount = models.get("face_parser_sett") if fp_enable: bgr_fake = swap_regions( bgr_fake, aimg, face_parser, smooth_mask, includes=includes, blur=blur_amount ) if fe_enable: _, bgr_fake, _ = face_enhancer.enhance( bgr_fake, paste_back=True, has_aligned=True ) bgr_fake = bgr_fake[0] M /= 0.25 IM = cv2.invertAffineTransform(M) img_white = np.full((aimg.shape[0], aimg.shape[1]), 255, dtype=np.float32) bgr_fake = cv2.warpAffine( bgr_fake, IM, (whole_img.shape[1], whole_img.shape[0]), borderValue=0.0 ) img_white = cv2.warpAffine( img_white, IM, (whole_img.shape[1], whole_img.shape[0]), borderValue=0.0 ) img_white[img_white > 20] = 255 img_mask = img_white mask_h_inds, mask_w_inds = np.where(img_mask == 255) mask_h = np.max(mask_h_inds) - np.min(mask_h_inds) mask_w = np.max(mask_w_inds) - np.min(mask_w_inds) mask_size = int(np.sqrt(mask_h * mask_w)) k = max(mask_size // 10, 10) img_mask = cv2.erode(img_mask, np.ones((k, k), np.uint8), iterations=1) k = max(mask_size // 20, 5) kernel_size = (k, k) blur_size = tuple(2 * i + 1 for i in kernel_size) img_mask = cv2.GaussianBlur(img_mask, blur_size, 0) / 255 img_mask = np.reshape(img_mask, [img_mask.shape[0], img_mask.shape[1], 1]) fake_merged = img_mask * bgr_fake + (1 - img_mask) * whole_img.astype(np.float32) fake_merged = add_logo_to_image(fake_merged.astype("uint8")) return fake_merged def swap_face_with_condition( whole_img, target_faces, source_face, condition, age, models ): swapped = whole_img.copy() for target_face in target_faces: if condition == "All face": swapped = swap_face(swapped, target_face, source_face, models) elif condition == "Age less than" and target_face["age"] < age: swapped = swap_face(swapped, target_face, source_face, models) elif condition == "Age greater than" and target_face["age"] > age: swapped = swap_face(swapped, target_face, source_face, models) elif condition == "All Male" and target_face["gender"] == 1: swapped = swap_face(swapped, target_face, source_face, models) elif condition == "All Female" and target_face["gender"] == 0: swapped = swap_face(swapped, target_face, source_face, models) return swapped def swap_specific(source_specifics, target_faces, whole_img, models, threshold=0.6): swapped = whole_img.copy() for source_face, specific_face in source_specifics: specific_embed = specific_face["embedding"] specific_embed /= np.linalg.norm(specific_embed) for target_face in target_faces: target_embed = target_face["embedding"] target_embed /= np.linalg.norm(target_embed) cosine_distance = 1 - np.dot(specific_embed, target_embed) if cosine_distance > threshold: continue swapped = swap_face(swapped, target_face, source_face, models) return swapped