82 lines
3.8 KiB
Markdown
82 lines
3.8 KiB
Markdown
# Documentation
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- Class name: CropFace
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- Category: facerestore_cf
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- Output node: False
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- Repo Ref: https://github.com/mav-rik/facerestore_cf
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CropFace节点旨在从输入的图像中提取和处理面部区域。它利用检测模型来识别面部，然后根据预定义的模板裁剪和对齐它们。该节点的主要功能是通过确保面部被隔离并按大小和方向标准化，为进一步的处理（如恢复或识别）准备面部图像。
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# Input types
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## Required
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- image
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- 输入图像对于节点执行面部检测和裁剪至关重要。它是提取和处理面部区域的来源。没有有效的图像，节点无法执行其预期功能。
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- Comfy dtype: IMAGE
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- Python dtype: numpy.ndarray
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## Optional
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- facedetection
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- facedetection参数指定用于在输入图像中识别面部的检测模型。它影响面部检测过程的准确性和效率，进而影响裁剪面部图像的质量。
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- Comfy dtype: COMBO[retinaface_resnet50, retinaface_mobile0.25, YOLOv5l, YOLOv5n]
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- Python dtype: str
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# Output types
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- IMAGE
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- 输出包括裁剪和对齐的面部图像，这些图像准备好进行进一步处理。这些输出是节点面部检测和图像处理能力的成果。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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# Usage tips
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- Infra type: CPU
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# Source code
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```
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class CropFace:
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@classmethod
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def INPUT_TYPES(s):
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return {'required': {'image': ('IMAGE',), 'facedetection': (['retinaface_resnet50', 'retinaface_mobile0.25', 'YOLOv5l', 'YOLOv5n'],)}}
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RETURN_TYPES = ('IMAGE',)
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FUNCTION = 'crop_face'
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CATEGORY = 'facerestore_cf'
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def __init__(self):
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self.face_helper = None
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def crop_face(self, image, facedetection):
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device = model_management.get_torch_device()
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if self.face_helper is None:
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self.face_helper = FaceRestoreHelper(1, face_size=512, crop_ratio=(1, 1), det_model=facedetection, save_ext='png', use_parse=True, device=device)
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image_np = 255.0 * image.cpu().numpy()
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total_images = image_np.shape[0]
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out_images = np.ndarray(shape=(total_images, 512, 512, 3))
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next_idx = 0
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for i in range(total_images):
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cur_image_np = image_np[i, :, :, ::-1]
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original_resolution = cur_image_np.shape[0:2]
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if self.face_helper is None:
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return image
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self.face_helper.clean_all()
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self.face_helper.read_image(cur_image_np)
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self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5)
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self.face_helper.align_warp_face()
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faces_found = len(self.face_helper.cropped_faces)
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if faces_found == 0:
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next_idx += 1
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if out_images.shape[0] < next_idx + faces_found:
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print(out_images.shape)
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print((next_idx + faces_found, 512, 512, 3))
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print('aaaaa')
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out_images = np.resize(out_images, (next_idx + faces_found, 512, 512, 3))
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print(out_images.shape)
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for j in range(faces_found):
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cropped_face_1 = self.face_helper.cropped_faces[j]
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cropped_face_2 = img2tensor(cropped_face_1 / 255.0, bgr2rgb=True, float32=True)
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normalize(cropped_face_2, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
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cropped_face_3 = cropped_face_2.unsqueeze(0).to(device)
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cropped_face_4 = tensor2img(cropped_face_3, rgb2bgr=True, min_max=(-1, 1)).astype('uint8')
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cropped_face_5 = cv2.cvtColor(cropped_face_4, cv2.COLOR_BGR2RGB)
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out_images[next_idx] = cropped_face_5
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next_idx += 1
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cropped_face_6 = np.array(out_images).astype(np.float32) / 255.0
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cropped_face_7 = torch.from_numpy(cropped_face_6)
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return (cropped_face_7,)
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``` |