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Delete assets/core.py
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assets/core.py
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import os
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import cv2
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import glob
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import time
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import torch
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import shutil
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import argparse
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import platform
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import datetime
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import subprocess
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import insightface
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import onnxruntime
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import numpy as np
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import gradio as gr
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import threading
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import queue
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from tqdm import tqdm
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import concurrent.futures
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from moviepy.editor import VideoFileClip
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from face_swapper import Inswapper, paste_to_whole
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from face_analyser import detect_conditions, get_analysed_data, swap_options_list
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from face_parsing import init_parsing_model, get_parsed_mask, mask_regions, mask_regions_to_list
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from face_enhancer import get_available_enhancer_names, load_face_enhancer_model, cv2_interpolations
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from utils import trim_video, StreamerThread, ProcessBar, open_directory, split_list_by_lengths, merge_img_sequence_from_ref, create_image_grid
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## ------------------------------ USER ARGS ------------------------------
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parser = argparse.ArgumentParser(description="Swap-Mukham Face Swapper")
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parser.add_argument("--out_dir", help="Default Output directory", default=os.getcwd())
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parser.add_argument("--batch_size", help="Gpu batch size", default=32)
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parser.add_argument("--cuda", action="store_true", help="Enable cuda", default=False)
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parser.add_argument(
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"--colab", action="store_true", help="Enable colab mode", default=False
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)
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user_args = parser.parse_args()
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## ------------------------------ DEFAULTS ------------------------------
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USE_COLAB = user_args.colab
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USE_CUDA = user_args.cuda
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DEF_OUTPUT_PATH = user_args.out_dir
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BATCH_SIZE = int(user_args.batch_size)
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WORKSPACE = None
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OUTPUT_FILE = None
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CURRENT_FRAME = None
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STREAMER = None
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DETECT_CONDITION = "best detection"
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DETECT_SIZE = 640
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DETECT_THRESH = 0.6
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NUM_OF_SRC_SPECIFIC = 10
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MASK_INCLUDE = [
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"Skin",
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"R-Eyebrow",
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"L-Eyebrow",
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"L-Eye",
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"R-Eye",
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"Nose",
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"Mouth",
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"L-Lip",
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"U-Lip"
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]
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MASK_SOFT_KERNEL = 17
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MASK_SOFT_ITERATIONS = 10
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MASK_BLUR_AMOUNT = 0.1
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MASK_ERODE_AMOUNT = 0.15
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FACE_SWAPPER = None
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FACE_ANALYSER = None
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FACE_ENHANCER = None
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FACE_PARSER = None
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FACE_ENHANCER_LIST = ["NONE"]
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FACE_ENHANCER_LIST.extend(get_available_enhancer_names())
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FACE_ENHANCER_LIST.extend(cv2_interpolations)
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## ------------------------------ SET EXECUTION PROVIDER ------------------------------
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# Note: Non CUDA users may change settings here
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PROVIDER = ["CPUExecutionProvider"]
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if USE_CUDA:
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available_providers = onnxruntime.get_available_providers()
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if "CUDAExecutionProvider" in available_providers:
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print("\n********** Running on CUDA **********\n")
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PROVIDER = ["CUDAExecutionProvider", "CPUExecutionProvider"]
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else:
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USE_CUDA = False
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print("\n********** CUDA unavailable running on CPU **********\n")
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else:
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USE_CUDA = False
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print("\n********** Running on CPU **********\n")
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device = "cuda" if USE_CUDA else "cpu"
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EMPTY_CACHE = lambda: torch.cuda.empty_cache() if device == "cuda" else None
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## ------------------------------ LOAD MODELS ------------------------------
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def load_face_analyser_model(name="buffalo_l"):
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global FACE_ANALYSER
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if FACE_ANALYSER is None:
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FACE_ANALYSER = insightface.app.FaceAnalysis(name=name, providers=PROVIDER)
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FACE_ANALYSER.prepare(
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ctx_id=0, det_size=(DETECT_SIZE, DETECT_SIZE), det_thresh=DETECT_THRESH
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)
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def load_face_swapper_model(path="./assets/pretrained_models/inswapper_128.onnx"):
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global FACE_SWAPPER
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if FACE_SWAPPER is None:
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batch = int(BATCH_SIZE) if device == "cuda" else 1
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FACE_SWAPPER = Inswapper(model_file=path, batch_size=batch, providers=PROVIDER)
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def load_face_parser_model(path="./assets/pretrained_models/79999_iter.pth"):
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global FACE_PARSER
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if FACE_PARSER is None:
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FACE_PARSER = init_parsing_model(path, device=device)
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load_face_analyser_model()
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load_face_swapper_model()
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## ------------------------------ MAIN PROCESS ------------------------------
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def process(
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input_type,
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image_path,
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video_path,
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directory_path,
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source_path,
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output_path,
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output_name,
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keep_output_sequence,
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condition,
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age,
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distance,
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face_enhancer_name,
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enable_face_parser,
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mask_includes,
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mask_soft_kernel,
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mask_soft_iterations,
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blur_amount,
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erode_amount,
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face_scale,
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enable_laplacian_blend,
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crop_top,
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crop_bott,
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crop_left,
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crop_right,
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*specifics,
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):
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global WORKSPACE
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global OUTPUT_FILE
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global PREVIEW
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WORKSPACE, OUTPUT_FILE, PREVIEW = None, None, None
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## ------------------------------ GUI UPDATE FUNC ------------------------------
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def ui_before():
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return (
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gr.update(visible=True, value=PREVIEW),
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gr.update(interactive=False),
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gr.update(interactive=False),
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gr.update(visible=False),
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)
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def ui_after():
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return (
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gr.update(visible=True, value=PREVIEW),
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gr.update(interactive=True),
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gr.update(interactive=True),
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gr.update(visible=False),
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)
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def ui_after_vid():
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return (
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gr.update(visible=False),
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gr.update(interactive=True),
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gr.update(interactive=True),
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gr.update(value=OUTPUT_FILE, visible=True),
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)
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start_time = time.time()
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total_exec_time = lambda start_time: divmod(time.time() - start_time, 60)
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get_finsh_text = lambda start_time: f"✔️ Completed in {int(total_exec_time(start_time)[0])} min {int(total_exec_time(start_time)[1])} sec."
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## ------------------------------ PREPARE INPUTS & LOAD MODELS ------------------------------
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yield "### \n 💊 Loading face analyser model...", *ui_before()
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load_face_analyser_model()
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yield "### \n 👑 Loading face swapper model...", *ui_before()
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load_face_swapper_model()
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if face_enhancer_name != "NONE":
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if face_enhancer_name not in cv2_interpolations:
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yield f"### \n 🔮 Loading {face_enhancer_name} model...", *ui_before()
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FACE_ENHANCER = load_face_enhancer_model(name=face_enhancer_name, device=device)
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else:
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FACE_ENHANCER = None
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if enable_face_parser:
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yield "### \n 🧲 Loading face parsing model...", *ui_before()
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load_face_parser_model()
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includes = mask_regions_to_list(mask_includes)
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specifics = list(specifics)
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half = len(specifics) // 2
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sources = specifics[:half]
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specifics = specifics[half:]
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if crop_top > crop_bott:
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crop_top, crop_bott = crop_bott, crop_top
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if crop_left > crop_right:
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crop_left, crop_right = crop_right, crop_left
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crop_mask = (crop_top, 511-crop_bott, crop_left, 511-crop_right)
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def swap_process(image_sequence):
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## ------------------------------ CONTENT CHECK ------------------------------
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yield "### \n 📡 Analysing face data...", *ui_before()
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if condition != "Specific Face":
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source_data = source_path, age
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else:
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source_data = ((sources, specifics), distance)
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analysed_targets, analysed_sources, whole_frame_list, num_faces_per_frame = get_analysed_data(
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FACE_ANALYSER,
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image_sequence,
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source_data,
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swap_condition=condition,
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detect_condition=DETECT_CONDITION,
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scale=face_scale
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)
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## ------------------------------ SWAP FUNC ------------------------------
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yield "### \n ⚙️ Generating faces...", *ui_before()
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preds = []
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matrs = []
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count = 0
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global PREVIEW
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for batch_pred, batch_matr in FACE_SWAPPER.batch_forward(whole_frame_list, analysed_targets, analysed_sources):
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preds.extend(batch_pred)
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matrs.extend(batch_matr)
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EMPTY_CACHE()
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count += 1
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if USE_CUDA:
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image_grid = create_image_grid(batch_pred, size=128)
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PREVIEW = image_grid[:, :, ::-1]
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yield f"### \n ⚙️ Generating face Batch {count}", *ui_before()
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## ------------------------------ FACE ENHANCEMENT ------------------------------
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generated_len = len(preds)
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if face_enhancer_name != "NONE":
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yield f"### \n 📐 Upscaling faces with {face_enhancer_name}...", *ui_before()
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for idx, pred in tqdm(enumerate(preds), total=generated_len, desc=f"Upscaling with {face_enhancer_name}"):
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enhancer_model, enhancer_model_runner = FACE_ENHANCER
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pred = enhancer_model_runner(pred, enhancer_model)
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preds[idx] = cv2.resize(pred, (512,512))
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EMPTY_CACHE()
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## ------------------------------ FACE PARSING ------------------------------
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if enable_face_parser:
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yield "### \n 🖇️ Face-parsing mask...", *ui_before()
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masks = []
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count = 0
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for batch_mask in get_parsed_mask(FACE_PARSER, preds, classes=includes, device=device, batch_size=BATCH_SIZE, softness=int(mask_soft_iterations)):
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masks.append(batch_mask)
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EMPTY_CACHE()
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count += 1
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if len(batch_mask) > 1:
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image_grid = create_image_grid(batch_mask, size=128)
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PREVIEW = image_grid[:, :, ::-1]
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yield f"### \n ✏️ Face parsing Batch {count}", *ui_before()
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masks = np.concatenate(masks, axis=0) if len(masks) >= 1 else masks
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else:
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masks = [None] * generated_len
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## ------------------------------ SPLIT LIST ------------------------------
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split_preds = split_list_by_lengths(preds, num_faces_per_frame)
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del preds
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split_matrs = split_list_by_lengths(matrs, num_faces_per_frame)
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del matrs
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split_masks = split_list_by_lengths(masks, num_faces_per_frame)
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del masks
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## ------------------------------ PASTE-BACK ------------------------------
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yield "### \n 🛠️ Pasting back...", *ui_before()
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def post_process(frame_idx, frame_img, split_preds, split_matrs, split_masks, enable_laplacian_blend, crop_mask, blur_amount, erode_amount):
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whole_img_path = frame_img
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whole_img = cv2.imread(whole_img_path)
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blend_method = 'laplacian' if enable_laplacian_blend else 'linear'
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for p, m, mask in zip(split_preds[frame_idx], split_matrs[frame_idx], split_masks[frame_idx]):
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p = cv2.resize(p, (512,512))
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mask = cv2.resize(mask, (512,512)) if mask is not None else None
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m /= 0.25
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whole_img = paste_to_whole(p, whole_img, m, mask=mask, crop_mask=crop_mask, blend_method=blend_method, blur_amount=blur_amount, erode_amount=erode_amount)
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cv2.imwrite(whole_img_path, whole_img)
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def concurrent_post_process(image_sequence, *args):
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with concurrent.futures.ThreadPoolExecutor() as executor:
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futures = []
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for idx, frame_img in enumerate(image_sequence):
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future = executor.submit(post_process, idx, frame_img, *args)
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futures.append(future)
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for future in tqdm(concurrent.futures.as_completed(futures), total=len(futures), desc="Pasting back"):
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result = future.result()
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concurrent_post_process(
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image_sequence,
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split_preds,
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split_matrs,
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split_masks,
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enable_laplacian_blend,
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crop_mask,
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blur_amount,
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erode_amount
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)
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## ------------------------------ IMAGE ------------------------------
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if input_type == "Image":
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target = cv2.imread(image_path)
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output_file = os.path.join(output_path, output_name + ".png")
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cv2.imwrite(output_file, target)
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for info_update in swap_process([output_file]):
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yield info_update
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OUTPUT_FILE = output_file
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WORKSPACE = output_path
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PREVIEW = cv2.imread(output_file)[:, :, ::-1]
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yield get_finsh_text(start_time), *ui_after()
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## ------------------------------ VIDEO ------------------------------
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elif input_type == "Video":
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temp_path = os.path.join(output_path, output_name, "sequence")
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os.makedirs(temp_path, exist_ok=True)
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yield "### \n 💽 Extracting video frames...", *ui_before()
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image_sequence = []
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cap = cv2.VideoCapture(video_path)
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curr_idx = 0
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while True:
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ret, frame = cap.read()
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if not ret:break
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frame_path = os.path.join(temp_path, f"frame_{curr_idx}.jpg")
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cv2.imwrite(frame_path, frame)
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image_sequence.append(frame_path)
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curr_idx += 1
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cap.release()
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cv2.destroyAllWindows()
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for info_update in swap_process(image_sequence):
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yield info_update
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yield "### \n 🔗 Merging sequence...", *ui_before()
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output_video_path = os.path.join(output_path, output_name + ".mp4")
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merge_img_sequence_from_ref(video_path, image_sequence, output_video_path)
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if os.path.exists(temp_path) and not keep_output_sequence:
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yield "### \n 🚽 Removing temporary files...", *ui_before()
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shutil.rmtree(temp_path)
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WORKSPACE = output_path
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OUTPUT_FILE = output_video_path
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yield get_finsh_text(start_time), *ui_after_vid()
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382 |
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## ------------------------------ DIRECTORY ------------------------------
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384 |
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elif input_type == "Directory":
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extensions = ["jpg", "jpeg", "png", "bmp", "tiff", "ico", "webp"]
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temp_path = os.path.join(output_path, output_name)
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388 |
-
if os.path.exists(temp_path):
|
389 |
-
shutil.rmtree(temp_path)
|
390 |
-
os.mkdir(temp_path)
|
391 |
-
|
392 |
-
file_paths =[]
|
393 |
-
for file_path in glob.glob(os.path.join(directory_path, "*")):
|
394 |
-
if any(file_path.lower().endswith(ext) for ext in extensions):
|
395 |
-
img = cv2.imread(file_path)
|
396 |
-
new_file_path = os.path.join(temp_path, os.path.basename(file_path))
|
397 |
-
cv2.imwrite(new_file_path, img)
|
398 |
-
file_paths.append(new_file_path)
|
399 |
-
|
400 |
-
for info_update in swap_process(file_paths):
|
401 |
-
yield info_update
|
402 |
-
|
403 |
-
PREVIEW = cv2.imread(file_paths[-1])[:, :, ::-1]
|
404 |
-
WORKSPACE = temp_path
|
405 |
-
OUTPUT_FILE = file_paths[-1]
|
406 |
-
|
407 |
-
yield get_finsh_text(start_time), *ui_after()
|
408 |
-
|
409 |
-
## ------------------------------ STREAM ------------------------------
|
410 |
-
|
411 |
-
elif input_type == "Stream":
|
412 |
-
pass
|
413 |
-
|
414 |
-
|
415 |
-
## ------------------------------ GRADIO FUNC ------------------------------
|
416 |
-
|
417 |
-
|
418 |
-
def update_radio(value):
|
419 |
-
if value == "Image":
|
420 |
-
return (
|
421 |
-
gr.update(visible=True),
|
422 |
-
gr.update(visible=False),
|
423 |
-
gr.update(visible=False),
|
424 |
-
)
|
425 |
-
elif value == "Video":
|
426 |
-
return (
|
427 |
-
gr.update(visible=False),
|
428 |
-
gr.update(visible=True),
|
429 |
-
gr.update(visible=False),
|
430 |
-
)
|
431 |
-
elif value == "Directory":
|
432 |
-
return (
|
433 |
-
gr.update(visible=False),
|
434 |
-
gr.update(visible=False),
|
435 |
-
gr.update(visible=True),
|
436 |
-
)
|
437 |
-
elif value == "Stream":
|
438 |
-
return (
|
439 |
-
gr.update(visible=False),
|
440 |
-
gr.update(visible=False),
|
441 |
-
gr.update(visible=True),
|
442 |
-
)
|
443 |
-
|
444 |
-
|
445 |
-
def swap_option_changed(value):
|
446 |
-
if value.startswith("Age"):
|
447 |
-
return (
|
448 |
-
gr.update(visible=True),
|
449 |
-
gr.update(visible=False),
|
450 |
-
gr.update(visible=True),
|
451 |
-
)
|
452 |
-
elif value == "Specific Face":
|
453 |
-
return (
|
454 |
-
gr.update(visible=False),
|
455 |
-
gr.update(visible=True),
|
456 |
-
gr.update(visible=False),
|
457 |
-
)
|
458 |
-
return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True)
|
459 |
-
|
460 |
-
|
461 |
-
def video_changed(video_path):
|
462 |
-
sliders_update = gr.Slider.update
|
463 |
-
button_update = gr.Button.update
|
464 |
-
number_update = gr.Number.update
|
465 |
-
|
466 |
-
if video_path is None:
|
467 |
-
return (
|
468 |
-
sliders_update(minimum=0, maximum=0, value=0),
|
469 |
-
sliders_update(minimum=1, maximum=1, value=1),
|
470 |
-
number_update(value=1),
|
471 |
-
)
|
472 |
-
try:
|
473 |
-
clip = VideoFileClip(video_path)
|
474 |
-
fps = clip.fps
|
475 |
-
total_frames = clip.reader.nframes
|
476 |
-
clip.close()
|
477 |
-
return (
|
478 |
-
sliders_update(minimum=0, maximum=total_frames, value=0, interactive=True),
|
479 |
-
sliders_update(
|
480 |
-
minimum=0, maximum=total_frames, value=total_frames, interactive=True
|
481 |
-
),
|
482 |
-
number_update(value=fps),
|
483 |
-
)
|
484 |
-
except:
|
485 |
-
return (
|
486 |
-
sliders_update(value=0),
|
487 |
-
sliders_update(value=0),
|
488 |
-
number_update(value=1),
|
489 |
-
)
|
490 |
-
|
491 |
-
|
492 |
-
def analyse_settings_changed(detect_condition, detection_size, detection_threshold):
|
493 |
-
yield "### \n 💡 Applying new values..."
|
494 |
-
global FACE_ANALYSER
|
495 |
-
global DETECT_CONDITION
|
496 |
-
DETECT_CONDITION = detect_condition
|
497 |
-
FACE_ANALYSER = insightface.app.FaceAnalysis(name="buffalo_l", providers=PROVIDER)
|
498 |
-
FACE_ANALYSER.prepare(
|
499 |
-
ctx_id=0,
|
500 |
-
det_size=(int(detection_size), int(detection_size)),
|
501 |
-
det_thresh=float(detection_threshold),
|
502 |
-
)
|
503 |
-
yield f"### \n ✔️ Applied detect condition:{detect_condition}, detection size: {detection_size}, detection threshold: {detection_threshold}"
|
504 |
-
|
505 |
-
|
506 |
-
def stop_running():
|
507 |
-
global STREAMER
|
508 |
-
if hasattr(STREAMER, "stop"):
|
509 |
-
STREAMER.stop()
|
510 |
-
STREAMER = None
|
511 |
-
return "Cancelled"
|
512 |
-
|
513 |
-
|
514 |
-
def slider_changed(show_frame, video_path, frame_index):
|
515 |
-
if not show_frame:
|
516 |
-
return None, None
|
517 |
-
if video_path is None:
|
518 |
-
return None, None
|
519 |
-
clip = VideoFileClip(video_path)
|
520 |
-
frame = clip.get_frame(frame_index / clip.fps)
|
521 |
-
frame_array = np.array(frame)
|
522 |
-
clip.close()
|
523 |
-
return gr.Image.update(value=frame_array, visible=True), gr.Video.update(
|
524 |
-
visible=False
|
525 |
-
)
|
526 |
-
|
527 |
-
|
528 |
-
def trim_and_reload(video_path, output_path, output_name, start_frame, stop_frame):
|
529 |
-
yield video_path, f"### \n 🛠️ Trimming video frame {start_frame} to {stop_frame}..."
|
530 |
-
try:
|
531 |
-
output_path = os.path.join(output_path, output_name)
|
532 |
-
trimmed_video = trim_video(video_path, output_path, start_frame, stop_frame)
|
533 |
-
yield trimmed_video, "### \n ✔️ Video trimmed and reloaded."
|
534 |
-
except Exception as e:
|
535 |
-
print(e)
|
536 |
-
yield video_path, "### \n ❌ Video trimming failed. See console for more info."
|
537 |
-
|
538 |
-
|
539 |
-
## ------------------------------ GRADIO GUI ------------------------------
|
540 |
-
|
541 |
-
css = """
|
542 |
-
footer{display:none !important}
|
543 |
-
"""
|
544 |
-
|
545 |
-
with gr.Blocks(css=css) as interface:
|
546 |
-
gr.Markdown("# 🧸 Deepfake Faceswap")
|
547 |
-
gr.Markdown("### 📥 insightface inswapper bypass NSFW.")
|
548 |
-
with gr.Row():
|
549 |
-
with gr.Row():
|
550 |
-
with gr.Column(scale=0.4):
|
551 |
-
with gr.Tab("⚖️ Swap Condition"):
|
552 |
-
swap_option = gr.Dropdown(
|
553 |
-
swap_options_list,
|
554 |
-
info="Choose which face or faces in the target image to swap.",
|
555 |
-
multiselect=False,
|
556 |
-
show_label=False,
|
557 |
-
value=swap_options_list[0],
|
558 |
-
interactive=True,
|
559 |
-
)
|
560 |
-
age = gr.Number(
|
561 |
-
value=25, label="Value", interactive=True, visible=False
|
562 |
-
)
|
563 |
-
|
564 |
-
with gr.Tab("🎛️ Detection Settings"):
|
565 |
-
detect_condition_dropdown = gr.Dropdown(
|
566 |
-
detect_conditions,
|
567 |
-
label="Condition",
|
568 |
-
value=DETECT_CONDITION,
|
569 |
-
interactive=True,
|
570 |
-
info="This condition is only used when multiple faces are detected on source or specific image.",
|
571 |
-
)
|
572 |
-
detection_size = gr.Number(
|
573 |
-
label="Detection Size", value=DETECT_SIZE, interactive=True
|
574 |
-
)
|
575 |
-
detection_threshold = gr.Number(
|
576 |
-
label="Detection Threshold",
|
577 |
-
value=DETECT_THRESH,
|
578 |
-
interactive=True,
|
579 |
-
)
|
580 |
-
apply_detection_settings = gr.Button("Apply settings")
|
581 |
-
|
582 |
-
with gr.Tab("♻️ Output Settings"):
|
583 |
-
output_directory = gr.Text(
|
584 |
-
label="Output Directory",
|
585 |
-
value=DEF_OUTPUT_PATH,
|
586 |
-
interactive=True,
|
587 |
-
)
|
588 |
-
output_name = gr.Text(
|
589 |
-
label="Output Name", value="Result", interactive=True
|
590 |
-
)
|
591 |
-
keep_output_sequence = gr.Checkbox(
|
592 |
-
label="Keep output sequence", value=False, interactive=True
|
593 |
-
)
|
594 |
-
|
595 |
-
with gr.Tab("💎 Other Settings"):
|
596 |
-
face_scale = gr.Slider(
|
597 |
-
label="Face Scale",
|
598 |
-
minimum=0,
|
599 |
-
maximum=2,
|
600 |
-
value=1,
|
601 |
-
interactive=True,
|
602 |
-
)
|
603 |
-
|
604 |
-
face_enhancer_name = gr.Dropdown(
|
605 |
-
FACE_ENHANCER_LIST, label="Face Enhancer", value="NONE", multiselect=False, interactive=True
|
606 |
-
)
|
607 |
-
|
608 |
-
with gr.Accordion("Advanced Mask", open=False):
|
609 |
-
enable_face_parser_mask = gr.Checkbox(
|
610 |
-
label="Enable Face Parsing",
|
611 |
-
value=False,
|
612 |
-
interactive=True,
|
613 |
-
)
|
614 |
-
|
615 |
-
mask_include = gr.Dropdown(
|
616 |
-
mask_regions.keys(),
|
617 |
-
value=MASK_INCLUDE,
|
618 |
-
multiselect=True,
|
619 |
-
label="Include",
|
620 |
-
interactive=True,
|
621 |
-
)
|
622 |
-
mask_soft_kernel = gr.Number(
|
623 |
-
label="Soft Erode Kernel",
|
624 |
-
value=MASK_SOFT_KERNEL,
|
625 |
-
minimum=3,
|
626 |
-
interactive=True,
|
627 |
-
visible = False
|
628 |
-
)
|
629 |
-
mask_soft_iterations = gr.Number(
|
630 |
-
label="Soft Erode Iterations",
|
631 |
-
value=MASK_SOFT_ITERATIONS,
|
632 |
-
minimum=0,
|
633 |
-
interactive=True,
|
634 |
-
|
635 |
-
)
|
636 |
-
|
637 |
-
|
638 |
-
with gr.Accordion("Crop Mask", open=False):
|
639 |
-
crop_top = gr.Slider(label="Top", minimum=0, maximum=511, value=0, step=1, interactive=True)
|
640 |
-
crop_bott = gr.Slider(label="Bottom", minimum=0, maximum=511, value=511, step=1, interactive=True)
|
641 |
-
crop_left = gr.Slider(label="Left", minimum=0, maximum=511, value=0, step=1, interactive=True)
|
642 |
-
crop_right = gr.Slider(label="Right", minimum=0, maximum=511, value=511, step=1, interactive=True)
|
643 |
-
|
644 |
-
|
645 |
-
erode_amount = gr.Slider(
|
646 |
-
label="Mask Erode",
|
647 |
-
minimum=0,
|
648 |
-
maximum=1,
|
649 |
-
value=MASK_ERODE_AMOUNT,
|
650 |
-
step=0.05,
|
651 |
-
interactive=True,
|
652 |
-
)
|
653 |
-
|
654 |
-
blur_amount = gr.Slider(
|
655 |
-
label="Mask Blur",
|
656 |
-
minimum=0,
|
657 |
-
maximum=1,
|
658 |
-
value=MASK_BLUR_AMOUNT,
|
659 |
-
step=0.05,
|
660 |
-
interactive=True,
|
661 |
-
)
|
662 |
-
|
663 |
-
enable_laplacian_blend = gr.Checkbox(
|
664 |
-
label="Laplacian Blending",
|
665 |
-
value=True,
|
666 |
-
interactive=True,
|
667 |
-
)
|
668 |
-
|
669 |
-
|
670 |
-
source_image_input = gr.Image(
|
671 |
-
label="Source face", type="filepath", interactive=True
|
672 |
-
)
|
673 |
-
|
674 |
-
with gr.Box(visible=False) as specific_face:
|
675 |
-
for i in range(NUM_OF_SRC_SPECIFIC):
|
676 |
-
idx = i + 1
|
677 |
-
code = "\n"
|
678 |
-
code += f"with gr.Tab(label='({idx})'):"
|
679 |
-
code += "\n\twith gr.Row():"
|
680 |
-
code += f"\n\t\tsrc{idx} = gr.Image(interactive=True, type='numpy', label='Source Face {idx}')"
|
681 |
-
code += f"\n\t\ttrg{idx} = gr.Image(interactive=True, type='numpy', label='Specific Face {idx}')"
|
682 |
-
exec(code)
|
683 |
-
|
684 |
-
distance_slider = gr.Slider(
|
685 |
-
minimum=0,
|
686 |
-
maximum=2,
|
687 |
-
value=0.6,
|
688 |
-
interactive=True,
|
689 |
-
label="Distance",
|
690 |
-
info="Lower distance is more similar and higher distance is less similar to the target face.",
|
691 |
-
)
|
692 |
-
|
693 |
-
with gr.Group():
|
694 |
-
input_type = gr.Radio(
|
695 |
-
["Image", "Video"],
|
696 |
-
label="Target Type",
|
697 |
-
value="Image",
|
698 |
-
)
|
699 |
-
|
700 |
-
with gr.Box(visible=True) as input_image_group:
|
701 |
-
image_input = gr.Image(
|
702 |
-
label="Target Image", interactive=True, type="filepath"
|
703 |
-
)
|
704 |
-
|
705 |
-
with gr.Box(visible=False) as input_video_group:
|
706 |
-
vid_widget = gr.Video if USE_COLAB else gr.Text
|
707 |
-
video_input = gr.Video(
|
708 |
-
label="Target Video", interactive=True
|
709 |
-
)
|
710 |
-
with gr.Accordion("🎨 Trim video", open=False):
|
711 |
-
with gr.Column():
|
712 |
-
with gr.Row():
|
713 |
-
set_slider_range_btn = gr.Button(
|
714 |
-
"Set frame range", interactive=True
|
715 |
-
)
|
716 |
-
show_trim_preview_btn = gr.Checkbox(
|
717 |
-
label="Show frame when slider change",
|
718 |
-
value=True,
|
719 |
-
interactive=True,
|
720 |
-
)
|
721 |
-
|
722 |
-
video_fps = gr.Number(
|
723 |
-
value=30,
|
724 |
-
interactive=False,
|
725 |
-
label="Fps",
|
726 |
-
visible=False,
|
727 |
-
)
|
728 |
-
start_frame = gr.Slider(
|
729 |
-
minimum=0,
|
730 |
-
maximum=1,
|
731 |
-
value=0,
|
732 |
-
step=1,
|
733 |
-
interactive=True,
|
734 |
-
label="Start Frame",
|
735 |
-
info="",
|
736 |
-
)
|
737 |
-
end_frame = gr.Slider(
|
738 |
-
minimum=0,
|
739 |
-
maximum=1,
|
740 |
-
value=1,
|
741 |
-
step=1,
|
742 |
-
interactive=True,
|
743 |
-
label="End Frame",
|
744 |
-
info="",
|
745 |
-
)
|
746 |
-
trim_and_reload_btn = gr.Button(
|
747 |
-
"Trim and Reload", interactive=True
|
748 |
-
)
|
749 |
-
|
750 |
-
with gr.Box(visible=False) as input_directory_group:
|
751 |
-
direc_input = gr.Text(label="Path", interactive=True)
|
752 |
-
|
753 |
-
with gr.Column(scale=0.6):
|
754 |
-
info = gr.Markdown(value="...")
|
755 |
-
|
756 |
-
with gr.Row():
|
757 |
-
swap_button = gr.Button("🎯 Swap", variant="primary")
|
758 |
-
cancel_button = gr.Button("❌ Cancel")
|
759 |
-
|
760 |
-
preview_image = gr.Image(label="Output", interactive=False)
|
761 |
-
preview_video = gr.Video(
|
762 |
-
label="Output", interactive=False, visible=False
|
763 |
-
)
|
764 |
-
|
765 |
-
with gr.Row():
|
766 |
-
output_directory_button = gr.Button(
|
767 |
-
"💌", interactive=False, visible=False
|
768 |
-
)
|
769 |
-
output_video_button = gr.Button(
|
770 |
-
"📽️", interactive=False, visible=False
|
771 |
-
)
|
772 |
-
|
773 |
-
with gr.Box():
|
774 |
-
with gr.Row():
|
775 |
-
gr.Markdown(
|
776 |
-
"### [🎭 Sponsor]"
|
777 |
-
)
|
778 |
-
gr.Markdown(
|
779 |
-
"### [🖥️ Source code](https://huggingface.co/spaces/victorisgeek/SwapFace2Pon)"
|
780 |
-
)
|
781 |
-
gr.Markdown(
|
782 |
-
"### [ 🧩 Playground](https://huggingface.co/spaces/victorisgeek/SwapFace2Pon)"
|
783 |
-
)
|
784 |
-
gr.Markdown(
|
785 |
-
"### [📸 Run in Colab](https://colab.research.google.com/github/victorgeel/FaceSwapNoNfsw/blob/main/SwapFace.ipynb)"
|
786 |
-
)
|
787 |
-
gr.Markdown(
|
788 |
-
"### [🤗 Modified Version](https://github.com/victorgeel/FaceSwapNoNfsw)"
|
789 |
-
)
|
790 |
-
|
791 |
-
## ------------------------------ GRADIO EVENTS ------------------------------
|
792 |
-
|
793 |
-
set_slider_range_event = set_slider_range_btn.click(
|
794 |
-
video_changed,
|
795 |
-
inputs=[video_input],
|
796 |
-
outputs=[start_frame, end_frame, video_fps],
|
797 |
-
)
|
798 |
-
|
799 |
-
trim_and_reload_event = trim_and_reload_btn.click(
|
800 |
-
fn=trim_and_reload,
|
801 |
-
inputs=[video_input, output_directory, output_name, start_frame, end_frame],
|
802 |
-
outputs=[video_input, info],
|
803 |
-
)
|
804 |
-
|
805 |
-
start_frame_event = start_frame.release(
|
806 |
-
fn=slider_changed,
|
807 |
-
inputs=[show_trim_preview_btn, video_input, start_frame],
|
808 |
-
outputs=[preview_image, preview_video],
|
809 |
-
show_progress=True,
|
810 |
-
)
|
811 |
-
|
812 |
-
end_frame_event = end_frame.release(
|
813 |
-
fn=slider_changed,
|
814 |
-
inputs=[show_trim_preview_btn, video_input, end_frame],
|
815 |
-
outputs=[preview_image, preview_video],
|
816 |
-
show_progress=True,
|
817 |
-
)
|
818 |
-
|
819 |
-
input_type.change(
|
820 |
-
update_radio,
|
821 |
-
inputs=[input_type],
|
822 |
-
outputs=[input_image_group, input_video_group, input_directory_group],
|
823 |
-
)
|
824 |
-
swap_option.change(
|
825 |
-
swap_option_changed,
|
826 |
-
inputs=[swap_option],
|
827 |
-
outputs=[age, specific_face, source_image_input],
|
828 |
-
)
|
829 |
-
|
830 |
-
apply_detection_settings.click(
|
831 |
-
analyse_settings_changed,
|
832 |
-
inputs=[detect_condition_dropdown, detection_size, detection_threshold],
|
833 |
-
outputs=[info],
|
834 |
-
)
|
835 |
-
|
836 |
-
src_specific_inputs = []
|
837 |
-
gen_variable_txt = ",".join(
|
838 |
-
[f"src{i+1}" for i in range(NUM_OF_SRC_SPECIFIC)]
|
839 |
-
+ [f"trg{i+1}" for i in range(NUM_OF_SRC_SPECIFIC)]
|
840 |
-
)
|
841 |
-
exec(f"src_specific_inputs = ({gen_variable_txt})")
|
842 |
-
swap_inputs = [
|
843 |
-
input_type,
|
844 |
-
image_input,
|
845 |
-
video_input,
|
846 |
-
direc_input,
|
847 |
-
source_image_input,
|
848 |
-
output_directory,
|
849 |
-
output_name,
|
850 |
-
keep_output_sequence,
|
851 |
-
swap_option,
|
852 |
-
age,
|
853 |
-
distance_slider,
|
854 |
-
face_enhancer_name,
|
855 |
-
enable_face_parser_mask,
|
856 |
-
mask_include,
|
857 |
-
mask_soft_kernel,
|
858 |
-
mask_soft_iterations,
|
859 |
-
blur_amount,
|
860 |
-
erode_amount,
|
861 |
-
face_scale,
|
862 |
-
enable_laplacian_blend,
|
863 |
-
crop_top,
|
864 |
-
crop_bott,
|
865 |
-
crop_left,
|
866 |
-
crop_right,
|
867 |
-
*src_specific_inputs,
|
868 |
-
]
|
869 |
-
|
870 |
-
swap_outputs = [
|
871 |
-
info,
|
872 |
-
preview_image,
|
873 |
-
output_directory_button,
|
874 |
-
output_video_button,
|
875 |
-
preview_video,
|
876 |
-
]
|
877 |
-
|
878 |
-
swap_event = swap_button.click(
|
879 |
-
fn=process, inputs=swap_inputs, outputs=swap_outputs, show_progress=True
|
880 |
-
)
|
881 |
-
|
882 |
-
cancel_button.click(
|
883 |
-
fn=stop_running,
|
884 |
-
inputs=None,
|
885 |
-
outputs=[info],
|
886 |
-
cancels=[
|
887 |
-
swap_event,
|
888 |
-
trim_and_reload_event,
|
889 |
-
set_slider_range_event,
|
890 |
-
start_frame_event,
|
891 |
-
end_frame_event,
|
892 |
-
],
|
893 |
-
show_progress=True,
|
894 |
-
)
|
895 |
-
output_directory_button.click(
|
896 |
-
lambda: open_directory(path=WORKSPACE), inputs=None, outputs=None
|
897 |
-
)
|
898 |
-
output_video_button.click(
|
899 |
-
lambda: open_directory(path=OUTPUT_FILE), inputs=None, outputs=None
|
900 |
-
)
|
901 |
-
|
902 |
-
if __name__ == "__main__":
|
903 |
-
if USE_COLAB:
|
904 |
-
print("Running in colab mode")
|
905 |
-
|
906 |
-
interface.queue(concurrency_count=2, max_size=20).launch(share=USE_COLAB)
|
|
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