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410 lines
15 KiB
Python
410 lines
15 KiB
Python
import traceback
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from pprint import pformat
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from typing import *
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from scripts.faceswaplab_utils.typing import *
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import gradio as gr
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import onnx
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import pandas as pd
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from modules.shared import opts
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from PIL import Image
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import scripts.faceswaplab_swapping.swapper as swapper
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from scripts.faceswaplab_postprocessing.postprocessing_options import (
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PostProcessingOptions,
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)
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from scripts.faceswaplab_ui.faceswaplab_postprocessing_ui import postprocessing_ui
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from scripts.faceswaplab_ui.faceswaplab_unit_settings import FaceSwapUnitSettings
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from scripts.faceswaplab_ui.faceswaplab_unit_ui import faceswap_unit_ui
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from scripts.faceswaplab_utils import face_checkpoints_utils, imgutils
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from scripts.faceswaplab_utils.faceswaplab_logging import logger
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from scripts.faceswaplab_utils.models_utils import get_models
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from scripts.faceswaplab_utils.ui_utils import dataclasses_from_flat_list
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def compare(img1: PILImage, img2: PILImage) -> str:
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"""
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Compares the similarity between two faces extracted from images using cosine similarity.
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Args:
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img1: The first image containing a face.
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img2: The second image containing a face.
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Returns:
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A str of a float value representing the similarity between the two faces (0 to 1).
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Returns"You need 2 images to compare" if one or both of the images do not contain any faces.
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"""
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try:
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if img1 is not None and img2 is not None:
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return str(swapper.compare_faces(img1, img2))
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except Exception as e:
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logger.error("Fail to compare", e)
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traceback.print_exc()
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return "You need 2 images to compare"
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def extract_faces(
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files: List[gr.File],
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extract_path: Optional[str],
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*components: Tuple[gr.components.Component, ...],
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) -> Optional[List[PILImage]]:
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"""
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Extracts faces from a list of image files.
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Given a list of image file paths, this function opens each image, extracts the faces,
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and saves them in a specified directory. Post-processing is applied to each extracted face,
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and the processed faces are saved as separate PNG files.
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Parameters:
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files (Optional[List[str]]): List of file paths to the images to extract faces from.
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extract_path (Optional[str]): Path where the extracted faces will be saved.
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If no path is provided, a temporary directory will be created.
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components (List[gr.components.Component]): List of components for post-processing.
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Returns:
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Optional[List[str]]: List of file paths to the saved images of the extracted faces.
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If no faces are found, None is returned.
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"""
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if files and len(files) == 0:
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logger.error("You need at least one image file to extract")
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return []
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try:
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postprocess_options = PostProcessingOptions(*components) # type: ignore
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images = [
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Image.open(file.name) for file in files
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] # potentially greedy but Image.open is supposed to be lazy
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result_images = swapper.extract_faces(
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images, extract_path=extract_path, postprocess_options=postprocess_options
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)
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return result_images
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except Exception as e:
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logger.error("Failed to extract : %s", e)
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traceback.print_exc()
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return None
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def analyse_faces(image: PILImage, det_threshold: float = 0.5) -> Optional[str]:
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"""
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Function to analyze the faces in an image and provide a detailed report.
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Parameters
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----------
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image : PIL.PILImage
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The input image where faces will be detected. The image must be a PIL Image object.
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det_threshold : float, optional
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The detection threshold for the face detection process, by default 0.5. It determines
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the confidence level at which the function will consider a detected object as a face.
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Value should be in the range [0, 1], with higher values indicating greater certainty.
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Returns
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-------
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str or None
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Returns a formatted string providing details about each face detected in the image.
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For each face, the string will include an index and a set of facial details.
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In the event of an exception (e.g., analysis failure), the function will log the error
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and return None.
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Raises
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------
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This function handles exceptions internally and does not raise.
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Examples
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--------
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>>> image = Image.open("test.jpg")
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>>> print(analyse_faces(image, 0.7))
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"""
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try:
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faces = swapper.get_faces(imgutils.pil_to_cv2(image), det_thresh=det_threshold)
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result = ""
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for i, face in enumerate(faces):
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result += f"\nFace {i} \n" + "=" * 40 + "\n"
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result += pformat(face) + "\n"
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result += "=" * 40
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return result if result else None
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except Exception as e:
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logger.error("Analysis Failed : %s", e)
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traceback.print_exc()
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return None
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def build_face_checkpoint_and_save(
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batch_files: gr.File, name: str, overwrite: bool
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) -> PILImage:
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"""
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Builds a face checkpoint using the provided image files, performs face swapping,
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and saves the result to a file. If a blended face is successfully obtained and the face swapping
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process succeeds, the resulting image is returned. Otherwise, None is returned.
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Args:
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batch_files (list): List of image file paths used to create the face checkpoint.
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name (str): The name assigned to the face checkpoint.
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Returns:
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PIL.PILImage or None: The resulting swapped face image if the process is successful; None otherwise.
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"""
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try:
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if not batch_files:
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logger.error("No face found")
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return None
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filenames = [x.name for x in batch_files]
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preview_image = face_checkpoints_utils.build_face_checkpoint_and_save(
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filenames, name, overwrite=overwrite
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)
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except Exception as e:
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logger.error("Failed to build checkpoint %s", e)
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traceback.print_exc()
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return None
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return preview_image
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def explore_onnx_faceswap_model(model_path: str) -> pd.DataFrame:
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try:
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data: Dict[str, Any] = {
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"Node Name": [],
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"Op Type": [],
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"Inputs": [],
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"Outputs": [],
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"Attributes": [],
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}
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if model_path:
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model = onnx.load(model_path)
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for node in model.graph.node:
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data["Node Name"].append(pformat(node.name))
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data["Op Type"].append(pformat(node.op_type))
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data["Inputs"].append(pformat(node.input))
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data["Outputs"].append(pformat(node.output))
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attributes = []
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for attr in node.attribute:
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attr_name = attr.name
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attr_value = attr.t
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attributes.append(
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"{} = {}".format(pformat(attr_name), pformat(attr_value))
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)
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data["Attributes"].append(attributes)
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df = pd.DataFrame(data)
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except Exception as e:
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logger.error("Failed to explore model %s", e)
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traceback.print_exc()
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return None
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return df
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def batch_process(
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files: List[gr.File], save_path: str, *components: Tuple[Any, ...]
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) -> List[PILImage]:
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try:
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units_count = opts.data.get("faceswaplab_units_count", 3)
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classes: List[Any] = dataclasses_from_flat_list(
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[FaceSwapUnitSettings] * units_count + [PostProcessingOptions],
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components,
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)
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units: List[FaceSwapUnitSettings] = [
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u for u in classes if isinstance(u, FaceSwapUnitSettings)
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]
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postprocess_options = classes[-1]
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images = [
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Image.open(file.name) for file in files
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] # potentially greedy but Image.open is supposed to be lazy
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return swapper.batch_process(
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images,
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save_path=save_path,
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units=units,
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postprocess_options=postprocess_options,
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)
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except Exception as e:
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logger.error("Batch Process error : %s", e)
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traceback.print_exc()
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return []
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def tools_ui() -> None:
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models = get_models()
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with gr.Tab("Tools"):
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with gr.Tab("Build"):
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gr.Markdown(
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"""Build a face based on a batch list of images. Will blend the resulting face and store the checkpoint in the faceswaplab/faces directory."""
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)
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with gr.Row():
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build_batch_files = gr.components.File(
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type="file",
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file_count="multiple",
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label="Batch Sources Images",
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optional=True,
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elem_id="faceswaplab_build_batch_files",
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)
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preview = gr.components.Image(
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type="pil",
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label="Preview",
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width=512,
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height=512,
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interactive=False,
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elem_id="faceswaplab_build_preview_face",
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)
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build_name = gr.Textbox(
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value="Face",
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placeholder="Name of the character",
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label="Name of the character",
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elem_id="faceswaplab_build_character_name",
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)
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build_overwrite = gr.Checkbox(
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False,
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placeholder="overwrite",
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label="Overwrite Checkpoint if exist (else will add number)",
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elem_id="faceswaplab_build_overwrite",
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)
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generate_checkpoint_btn = gr.Button(
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"Save", elem_id="faceswaplab_build_save_btn"
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)
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with gr.Tab("Compare"):
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gr.Markdown(
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"""Give a similarity score between two images (only first face is compared)."""
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)
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with gr.Row():
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img1 = gr.components.Image(
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type="pil", label="Face 1", elem_id="faceswaplab_compare_face1"
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)
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img2 = gr.components.Image(
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type="pil", label="Face 2", elem_id="faceswaplab_compare_face2"
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)
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compare_btn = gr.Button("Compare", elem_id="faceswaplab_compare_btn")
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compare_result_text = gr.Textbox(
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interactive=False,
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label="Similarity",
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value="0",
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elem_id="faceswaplab_compare_result",
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)
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with gr.Tab("Extract"):
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gr.Markdown(
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"""Extract all faces from a batch of images. Will apply enhancement in the tools enhancement tab."""
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)
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with gr.Row():
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extracted_source_files = gr.components.File(
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type="file",
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file_count="multiple",
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label="Batch Sources Images",
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optional=True,
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elem_id="faceswaplab_extract_batch_images",
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)
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extracted_faces = gr.Gallery(
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label="Extracted faces",
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show_label=False,
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elem_id="faceswaplab_extract_results",
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).style(columns=[2], rows=[2])
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extract_save_path = gr.Textbox(
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label="Destination Directory",
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value="",
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elem_id="faceswaplab_extract_destination",
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)
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extract_btn = gr.Button("Extract", elem_id="faceswaplab_extract_btn")
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with gr.Tab("Explore Model"):
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model = gr.Dropdown(
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choices=models,
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label="Model not found, please download one and reload automatic 1111",
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elem_id="faceswaplab_explore_model",
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)
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explore_btn = gr.Button("Explore", elem_id="faceswaplab_explore_btn")
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explore_result_text = gr.Dataframe(
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interactive=False,
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label="Explored",
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elem_id="faceswaplab_explore_result",
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)
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with gr.Tab("Analyse Face"):
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img_to_analyse = gr.components.Image(
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type="pil", label="Face", elem_id="faceswaplab_analyse_face"
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)
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analyse_det_threshold = gr.Slider(
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0.1,
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1,
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0.5,
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step=0.01,
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label="Detection threshold",
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elem_id="faceswaplab_analyse_det_threshold",
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)
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analyse_btn = gr.Button("Analyse", elem_id="faceswaplab_analyse_btn")
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analyse_results = gr.Textbox(
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label="Results",
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interactive=False,
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value="",
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elem_id="faceswaplab_analyse_results",
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)
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with gr.Tab("Batch Process"):
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with gr.Tab("Source Images"):
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gr.Markdown(
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"""Batch process images. Will apply enhancement in the tools enhancement tab."""
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)
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with gr.Row():
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batch_source_files = gr.components.File(
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type="file",
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file_count="multiple",
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label="Batch Sources Images",
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optional=True,
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elem_id="faceswaplab_batch_images",
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)
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batch_results = gr.Gallery(
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label="Batch result",
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show_label=False,
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elem_id="faceswaplab_batch_results",
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).style(columns=[2], rows=[2])
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batch_save_path = gr.Textbox(
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label="Destination Directory",
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value="outputs/faceswap/",
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elem_id="faceswaplab_batch_destination",
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)
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batch_save_btn = gr.Button(
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"Process & Save", elem_id="faceswaplab_extract_btn"
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)
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unit_components = []
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for i in range(1, opts.data.get("faceswaplab_units_count", 3) + 1):
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unit_components += faceswap_unit_ui(False, i, id_prefix="faceswaplab_tab")
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upscale_options = postprocessing_ui()
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explore_btn.click(
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explore_onnx_faceswap_model, inputs=[model], outputs=[explore_result_text]
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)
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compare_btn.click(compare, inputs=[img1, img2], outputs=[compare_result_text])
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generate_checkpoint_btn.click(
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build_face_checkpoint_and_save,
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inputs=[build_batch_files, build_name, build_overwrite],
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outputs=[preview],
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)
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extract_btn.click(
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extract_faces,
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inputs=[extracted_source_files, extract_save_path] + upscale_options,
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outputs=[extracted_faces],
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)
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analyse_btn.click(
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analyse_faces,
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inputs=[img_to_analyse, analyse_det_threshold],
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outputs=[analyse_results],
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)
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batch_save_btn.click(
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batch_process,
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inputs=[batch_source_files, batch_save_path]
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+ unit_components
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+ upscale_options,
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outputs=[batch_results],
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)
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def on_ui_tabs() -> List[Any]:
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with gr.Blocks(analytics_enabled=False) as ui_faceswap:
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tools_ui()
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return [(ui_faceswap, "FaceSwapLab", "faceswaplab_tab")]
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