first commit

2023-07-26 17:42:02 +02:00
commit 056f7a72a2
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+from PIL import Image
+import numpy as np
+from fastapi import FastAPI, Body
+from fastapi.exceptions import HTTPException
+from modules.api.models import *
+from modules.api import api
+from scripts.faceswaplab_api.faceswaplab_api_types import FaceSwapUnit, FaceSwapRequest, FaceSwapResponse
+from scripts.faceswaplab_globals import VERSION_FLAG
+import gradio as gr
+from typing import List, Optional
+from scripts.faceswaplab_swapping import swapper
+from scripts.faceswaplab_utils.faceswaplab_logging import save_img_debug
+from scripts.faceswaplab_ui.faceswaplab_unit_settings import FaceSwapUnitSettings
+from scripts.faceswaplab_utils.imgutils import (pil_to_cv2,check_against_nsfw, base64_to_pil)
+from scripts.faceswaplab_utils.models_utils import get_current_model
+from modules.shared import opts
+
+
+def encode_to_base64(image):
+    if type(image) is str:
+        return image
+    elif type(image) is Image.Image:
+        return api.encode_pil_to_base64(image)
+    elif type(image) is np.ndarray:
+        return encode_np_to_base64(image)
+    else:
+        return ""
+
+def encode_np_to_base64(image):
+    pil = Image.fromarray(image)
+    return api.encode_pil_to_base64(pil)
+
+
+def faceswaplab_api(_: gr.Blocks, app: FastAPI):
+    @app.get("/faceswaplab/version", tags=["faceswaplab"], description="Get faceswaplab version")
+    async def version():
+        return {"version": VERSION_FLAG}
+    
+    # use post as we consider the method non idempotent (which is debatable)
+    @app.post("/faceswaplab/swap_face", tags=["faceswaplab"], description="Swap a face in an image using units")
+    async def swap_face(request : FaceSwapRequest) -> FaceSwapResponse:
+        units : List[FaceSwapUnitSettings]= []
+        src_image : Optional[Image.Image] = base64_to_pil(request.image)
+        response = FaceSwapResponse(images = [], infos=[])
+        if src_image is not None :
+            for u in request.units:
+                units.append(
+                    FaceSwapUnitSettings(source_img=base64_to_pil(u.source_img),
+                                    source_face = u.source_face,
+                                    _batch_files = u.get_batch_images(),
+                                    blend_faces= u.blend_faces,
+                                    enable = True,
+                                    same_gender = u.same_gender,
+                                    check_similarity=u.check_similarity,
+                                    _compute_similarity=u.compute_similarity,
+                                    min_ref_sim= u.min_ref_sim,
+                                    min_sim= u.min_sim,
+                                    _faces_index = ",".join([str(i) for i in (u.faces_index)]),
+                                    swap_in_generated=True,
+                                    swap_in_source=False
+                                ) 
+                )
+
+            swapped_images = swapper.process_images_units(get_current_model(), images=[(src_image,None)], units=units, upscaled_swapper=opts.data.get("faceswaplab_upscaled_swapper", False))
+            for img, info in swapped_images:
+                response.images.append(encode_to_base64(img))
+                response.infos.append(info)
+
+        return response
+
@@ -0,0 +1,73 @@
+from scripts.faceswaplab_swapping import swapper
+import numpy as np
+from typing import List, Tuple
+import dill as pickle
+import gradio as gr
+from insightface.app.common import Face
+from PIL import Image
+from scripts.faceswaplab_utils.imgutils import (pil_to_cv2,check_against_nsfw, base64_to_pil)
+from scripts.faceswaplab_utils.faceswaplab_logging import logger
+from pydantic import BaseModel, Field
+from scripts.faceswaplab_postprocessing.postprocessing_options import InpaintingWhen
+
+
+class FaceSwapUnit(BaseModel) :
+    
+    # The image given in reference
+    source_img: str = Field(description='base64 reference image', examples=["data:image/jpeg;base64,/9j/4AAQSkZJRgABAQECWAJYAAD...."], default=None)
+    # The checkpoint file
+    source_face : str = Field(description='face checkpoint (from models/faceswaplab/faces)',examples=["my_face.pkl"], default=None)
+    # base64 batch source images
+    batch_images: Tuple[str] = Field(description='list of base64 batch source images',examples=["data:image/jpeg;base64,/9j/4AAQSkZJRgABAQECWAJYAAD....", "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQECWAJYAAD...."], default=None)
+
+    # Will blend faces if True
+    blend_faces: bool = Field(description='Will blend faces if True', default=True)
+    
+    # Use same gender filtering
+    same_gender: bool = Field(description='Use same gender filtering', default=True)
+
+    # If True, discard images with low similarity
+    check_similarity : bool = Field(description='If True, discard images with low similarity', default=False) 
+    # if True will compute similarity and add it to the image info
+    compute_similarity : bool = Field(description='If True will compute similarity and add it to the image info', default=False) 
+
+    # Minimum similarity against the used face (reference, batch or checkpoint)
+    min_sim: float = Field(description='Minimum similarity against the used face (reference, batch or checkpoint)', default=0.0) 
+    # Minimum similarity against the reference (reference or checkpoint if checkpoint is given)
+    min_ref_sim: float = Field(description='Minimum similarity against the reference (reference or checkpoint if checkpoint is given)', default=0.0)
+
+    # The face index to use for swapping
+    faces_index: Tuple[int] = Field(description='The face index to use for swapping, list of face numbers starting from 0', default=(0,))
+
+    def get_batch_images(self) -> List[Image.Image] :
+        images = []
+        if self.batch_images :
+            for img in self.batch_images :
+                images.append(base64_to_pil(img))
+        return images
+
+class PostProcessingOptions (BaseModel):
+    face_restorer_name: str = Field(description='face restorer name', default=None)
+    restorer_visibility: float = Field(description='face restorer visibility', default=1, le=1, ge=0)
+    codeformer_weight: float = Field(description='face restorer codeformer weight', default=1, le=1, ge=0)
+
+    upscaler_name: str = Field(description='upscaler name', default=None)
+    scale: float = Field(description='upscaling scale', default=1, le=10, ge=0)
+    upscale_visibility: float = Field(description='upscaler visibility', default=1, le=1, ge=0)
+    
+    inpainting_denoising_strengh : float = Field(description='Inpainting denoising strenght', default=0, lt=1, ge=0)
+    inpainting_prompt : str = Field(description='Inpainting denoising strenght',examples=["Portrait of a [gender]"], default="Portrait of a [gender]")
+    inpainting_negative_prompt : str =  Field(description='Inpainting denoising strenght',examples=["Deformed, blurry, bad anatomy, disfigured, poorly drawn face, mutation"], default="")
+    inpainting_steps : int = Field(description='Inpainting steps',examples=["Portrait of a [gender]"], ge=1, le=150, default=20)
+    inpainting_sampler : str = Field(description='Inpainting sampler',examples=["Euler"], default="Euler")
+    inpainting_when : InpaintingWhen = Field(description='When inpainting happens', examples=[e.value for e in InpaintingWhen.__members__.values()], default=InpaintingWhen.NEVER)
+
+
+class FaceSwapRequest(BaseModel) : 
+    image : str = Field(description='base64 reference image', examples=["data:image/jpeg;base64,/9j/4AAQSkZJRgABAQECWAJYAAD...."], default=None)
+    units : List[FaceSwapUnit]
+    postprocessing : PostProcessingOptions
+
+class FaceSwapResponse(BaseModel) :
+    images : List[str] = Field(description='base64 swapped image',default=None)
+    infos : List[str]