import os import cv2 import glob import time import torch import shutil import argparse import platform import datetime import subprocess import insightface import onnxruntime import numpy as np import gradio as gr from tqdm import tqdm import concurrent.futures from moviepy.editor import VideoFileClip from nsfw_detector import get_nsfw_detector from face_swapper import Inswapper, paste_to_whole from face_analyser import detect_conditions, get_analysed_data, swap_options_list from face_enhancer import load_face_enhancer_model, face_enhancer_list, gfpgan_enhance, realesrgan_enhance from face_parsing import init_parser, swap_regions, mask_regions, mask_regions_to_list, SoftErosion from utils import trim_video, StreamerThread, ProcessBar, open_directory, split_list_by_lengths, merge_img_sequence_from_ref ## ------------------------------ USER ARGS ------------------------------ parser = argparse.ArgumentParser(description="Swap-Mukham Face Swapper") parser.add_argument("--out_dir", help="Default Output directory", default=os.getcwd()) parser.add_argument("--batch_size", help="Gpu batch size", default=32) parser.add_argument("--cuda", action="store_true", help="Enable cuda", default=False) parser.add_argument( "--colab", action="store_true", help="Enable colab mode", default=False ) user_args = parser.parse_args() ## ------------------------------ DEFAULTS ------------------------------ USE_COLAB = user_args.colab USE_CUDA = user_args.cuda DEF_OUTPUT_PATH = user_args.out_dir BATCH_SIZE = user_args.batch_size WORKSPACE = None OUTPUT_FILE = None CURRENT_FRAME = None STREAMER = None DETECT_CONDITION = "best detection" DETECT_SIZE = 640 DETECT_THRESH = 0.6 NUM_OF_SRC_SPECIFIC = 10 MASK_INCLUDE = [ "Skin", "R-Eyebrow", "L-Eyebrow", "L-Eye", "R-Eye", "Nose", "Mouth", "L-Lip", "U-Lip" ] MASK_SOFT_KERNEL = 17 MASK_SOFT_ITERATIONS = 7 MASK_BLUR_AMOUNT = 20 FACE_SWAPPER = None FACE_ANALYSER = None FACE_ENHANCER = None FACE_PARSER = None NSFW_DETECTOR = None ## ------------------------------ SET EXECUTION PROVIDER ------------------------------ # Note: For AMD,MAC or non CUDA users, change settings here PROVIDER = ["CPUExecutionProvider"] if USE_CUDA: available_providers = onnxruntime.get_available_providers() if "CUDAExecutionProvider" in available_providers: print("\n********** Running on CUDA **********\n") PROVIDER = ["CUDAExecutionProvider", "CPUExecutionProvider"] else: USE_CUDA = False print("\n********** CUDA unavailable running on CPU **********\n") else: USE_CUDA = False print("\n********** Running on CPU **********\n") device = "cuda" if USE_CUDA else "cpu" ## ------------------------------ LOAD MODELS ------------------------------ def load_face_analyser_model(name="buffalo_l"): global FACE_ANALYSER if FACE_ANALYSER is None: FACE_ANALYSER = insightface.app.FaceAnalysis(name=name, providers=PROVIDER) FACE_ANALYSER.prepare( ctx_id=0, det_size=(DETECT_SIZE, DETECT_SIZE), det_thresh=DETECT_THRESH ) def load_face_swapper_model(path="./assets/pretrained_models/inswapper_128.onnx"): global FACE_SWAPPER if FACE_SWAPPER is None: batch = int(BATCH_SIZE) if device == "cuda" else 1 FACE_SWAPPER = Inswapper(model_file=path, batch_size=batch, providers=PROVIDER) def load_face_parser_model(path="./assets/pretrained_models/79999_iter.pth"): global FACE_PARSER if FACE_PARSER is None: FACE_PARSER = init_parser(path, mode=device) def load_nsfw_detector_model(path="./assets/pretrained_models/nsfwmodel_281.pth"): global NSFW_DETECTOR if NSFW_DETECTOR is None: NSFW_DETECTOR = get_nsfw_detector(model_path=path, device=device) load_face_analyser_model() load_face_swapper_model() ## ------------------------------ MAIN PROCESS ------------------------------ def process( input_type, image_path, video_path, directory_path, source_path, output_path, output_name, keep_output_sequence, condition, age, distance, face_enhancer_name, enable_face_parser, mask_includes, mask_soft_kernel, mask_soft_iterations, blur_amount, face_scale, enable_laplacian_blend, crop_top, crop_bott, crop_left, crop_right, *specifics, ): global WORKSPACE global OUTPUT_FILE global PREVIEW WORKSPACE, OUTPUT_FILE, PREVIEW = None, None, None ## ------------------------------ GUI UPDATE FUNC ------------------------------ def ui_before(): return ( gr.update(visible=True, value=PREVIEW), gr.update(interactive=False), gr.update(interactive=False), gr.update(visible=False), ) def ui_after(): return ( gr.update(visible=True, value=PREVIEW), gr.update(interactive=True), gr.update(interactive=True), gr.update(visible=False), ) def ui_after_vid(): return ( gr.update(visible=False), gr.update(interactive=True), gr.update(interactive=True), gr.update(value=OUTPUT_FILE, visible=True), ) start_time = time.time() total_exec_time = lambda start_time: divmod(time.time() - start_time, 60) 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." ## ------------------------------ PREPARE INPUTS & LOAD MODELS ------------------------------ yield "### \n ⌛ Loading NSFW detector model...", *ui_before() load_nsfw_detector_model() yield "### \n ⌛ Loading face analyser model...", *ui_before() load_face_analyser_model() yield "### \n ⌛ Loading face swapper model...", *ui_before() load_face_swapper_model() if face_enhancer_name != "NONE": yield f"### \n ⌛ Loading {face_enhancer_name} model...", *ui_before() FACE_ENHANCER = load_face_enhancer_model(name=face_enhancer_name, device=device) else: FACE_ENHANCER = None if enable_face_parser: yield "### \n ⌛ Loading face parsing model...", *ui_before() load_face_parser_model() includes = mask_regions_to_list(mask_includes) smooth_mask = SoftErosion(kernel_size=17, threshold=0.9, iterations=int(mask_soft_iterations)).to(device) if mask_soft_iterations > 0 else None specifics = list(specifics) half = len(specifics) // 2 sources = specifics[:half] specifics = specifics[half:] ## ------------------------------ ANALYSE & SWAP FUNC ------------------------------ def swap_process(image_sequence): yield "### \n ⌛ Checking contents...", *ui_before() nsfw = NSFW_DETECTOR.is_nsfw(image_sequence) if nsfw: message = "NSFW Content detected !!!" yield f"### \n 🔞 {message}", *ui_before() assert not nsfw, message return False if device == "cuda": torch.cuda.empty_cache() yield "### \n ⌛ Analysing face data...", *ui_before() if condition != "Specific Face": source_data = source_path, age else: source_data = ((sources, specifics), distance) analysed_targets, analysed_sources, whole_frame_list, num_faces_per_frame = get_analysed_data( FACE_ANALYSER, image_sequence, source_data, swap_condition=condition, detect_condition=DETECT_CONDITION, scale=face_scale ) yield "### \n ⌛ Swapping faces...", *ui_before() preds, aimgs, matrs = FACE_SWAPPER.batch_forward(whole_frame_list, analysed_targets, analysed_sources) torch.cuda.empty_cache() if enable_face_parser: yield "### \n ⌛ Applying face-parsing mask...", *ui_before() for idx, (pred, aimg) in tqdm(enumerate(zip(preds, aimgs)), total=len(preds), desc="Face parsing"): preds[idx] = swap_regions(pred, aimg, FACE_PARSER, smooth_mask, includes=includes, blur=int(blur_amount)) torch.cuda.empty_cache() if face_enhancer_name != "NONE": yield f"### \n ⌛ Enhancing faces with {face_enhancer_name}...", *ui_before() for idx, pred in tqdm(enumerate(preds), total=len(preds), desc=f"{face_enhancer_name}"): if face_enhancer_name == 'GFPGAN': pred = gfpgan_enhance(pred, FACE_ENHANCER) elif face_enhancer_name.startswith("REAL-ESRGAN"): pred = realesrgan_enhance(pred, FACE_ENHANCER) preds[idx] = cv2.resize(pred, (512,512)) aimgs[idx] = cv2.resize(aimgs[idx], (512,512)) matrs[idx] /= 0.25 torch.cuda.empty_cache() split_preds = split_list_by_lengths(preds, num_faces_per_frame) del preds split_aimgs = split_list_by_lengths(aimgs, num_faces_per_frame) del aimgs split_matrs = split_list_by_lengths(matrs, num_faces_per_frame) del matrs yield "### \n ⌛ Post-processing...", *ui_before() def process_frame(frame_idx, frame_img, split_preds, split_aimgs, split_matrs, enable_laplacian_blend, crop_top, crop_bott, crop_left, crop_right): whole_img_path = frame_img whole_img = cv2.imread(whole_img_path) for p, a, m in zip(split_preds[frame_idx], split_aimgs[frame_idx], split_matrs[frame_idx]): whole_img = paste_to_whole(p, a, m, whole_img, laplacian_blend=enable_laplacian_blend, crop_mask=(crop_top, crop_bott, crop_left, crop_right)) cv2.imwrite(whole_img_path, whole_img) def optimize_processing(image_sequence, split_preds, split_aimgs, split_matrs, enable_laplacian_blend, crop_top, crop_bott, crop_left, crop_right): with concurrent.futures.ThreadPoolExecutor() as executor: futures = [] for idx, frame_img in enumerate(image_sequence): future = executor.submit( process_frame, idx, frame_img, split_preds, split_aimgs, split_matrs, enable_laplacian_blend, crop_top, crop_bott, crop_left, crop_right ) futures.append(future) for future in tqdm(concurrent.futures.as_completed(futures), total=len(futures), desc="Post-Processing"): try: result = future.result() except Exception as e: print(f"An error occurred: {e}") # Usage: optimize_processing( image_sequence, split_preds, split_aimgs, split_matrs, enable_laplacian_blend, crop_top, crop_bott, crop_left, crop_right ) ## ------------------------------ IMAGE ------------------------------ if input_type == "Image": target = cv2.imread(image_path) output_file = os.path.join(output_path, output_name + ".png") cv2.imwrite(output_file, target) for info_update in swap_process([output_file]): yield info_update OUTPUT_FILE = output_file WORKSPACE = output_path PREVIEW = cv2.imread(output_file)[:, :, ::-1] yield get_finsh_text(start_time), *ui_after() ## ------------------------------ VIDEO ------------------------------ elif input_type == "Video": temp_path = os.path.join(output_path, output_name, "sequence") os.makedirs(temp_path, exist_ok=True) yield "### \n ⌛ Extracting video frames...", *ui_before() image_sequence = [] cap = cv2.VideoCapture(video_path) curr_idx = 0 while True: ret, frame = cap.read() if not ret:break frame_path = os.path.join(temp_path, f"frame_{curr_idx}.jpg") cv2.imwrite(frame_path, frame) image_sequence.append(frame_path) curr_idx += 1 cap.release() cv2.destroyAllWindows() for info_update in swap_process(image_sequence): yield info_update yield "### \n ⌛ Merging sequence...", *ui_before() output_video_path = os.path.join(output_path, output_name + ".mp4") merge_img_sequence_from_ref(video_path, image_sequence, output_video_path) if os.path.exists(temp_path) and not keep_output_sequence: yield "### \n ⌛ Removing temporary files...", *ui_before() shutil.rmtree(temp_path) WORKSPACE = output_path OUTPUT_FILE = output_video_path yield get_finsh_text(start_time), *ui_after_vid() ## ------------------------------ DIRECTORY ------------------------------ elif input_type == "Directory": extensions = ["jpg", "jpeg", "png", "bmp", "tiff", "ico", "webp"] temp_path = os.path.join(output_path, output_name) if os.path.exists(temp_path): shutil.rmtree(temp_path) os.mkdir(temp_path) file_paths =[] for file_path in glob.glob(os.path.join(directory_path, "*")): if any(file_path.lower().endswith(ext) for ext in extensions): img = cv2.imread(file_path) new_file_path = os.path.join(temp_path, os.path.basename(file_path)) cv2.imwrite(new_file_path, img) file_paths.append(new_file_path) for info_update in swap_process(file_paths): yield info_update PREVIEW = cv2.imread(file_paths[-1])[:, :, ::-1] WORKSPACE = temp_path OUTPUT_FILE = file_paths[-1] yield get_finsh_text(start_time), *ui_after() ## ------------------------------ STREAM ------------------------------ elif input_type == "Stream": pass ## ------------------------------ GRADIO FUNC ------------------------------ def update_radio(value): if value == "Image": return ( gr.update(visible=True), gr.update(visible=False), gr.update(visible=False), ) elif value == "Video": return ( gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), ) elif value == "Directory": return ( gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), ) elif value == "Stream": return ( gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), ) def swap_option_changed(value): if value == swap_options_list[1] or value == swap_options_list[2]: return ( gr.update(visible=True), gr.update(visible=False), gr.update(visible=True), ) elif value == swap_options_list[5]: return ( gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), ) return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True) def video_changed(video_path): sliders_update = gr.Slider.update button_update = gr.Button.update number_update = gr.Number.update if video_path is None: return ( sliders_update(minimum=0, maximum=0, value=0), sliders_update(minimum=1, maximum=1, value=1), number_update(value=1), ) try: clip = VideoFileClip(video_path) fps = clip.fps total_frames = clip.reader.nframes clip.close() return ( sliders_update(minimum=0, maximum=total_frames, value=0, interactive=True), sliders_update( minimum=0, maximum=total_frames, value=total_frames, interactive=True ), number_update(value=fps), ) except: return ( sliders_update(value=0), sliders_update(value=0), number_update(value=1), ) def analyse_settings_changed(detect_condition, detection_size, detection_threshold): yield "### \n ⌛ Applying new values..." global FACE_ANALYSER global DETECT_CONDITION DETECT_CONDITION = detect_condition FACE_ANALYSER = insightface.app.FaceAnalysis(name="buffalo_l", providers=PROVIDER) FACE_ANALYSER.prepare( ctx_id=0, det_size=(int(detection_size), int(detection_size)), det_thresh=float(detection_threshold), ) yield f"### \n ✔️ Applied detect condition:{detect_condition}, detection size: {detection_size}, detection threshold: {detection_threshold}" def stop_running(): global STREAMER if hasattr(STREAMER, "stop"): STREAMER.stop() STREAMER = None return "Cancelled" def slider_changed(show_frame, video_path, frame_index): if not show_frame: return None, None if video_path is None: return None, None clip = VideoFileClip(video_path) frame = clip.get_frame(frame_index / clip.fps) frame_array = np.array(frame) clip.close() return gr.Image.update(value=frame_array, visible=True), gr.Video.update( visible=False ) def trim_and_reload(video_path, output_path, output_name, start_frame, stop_frame): yield video_path, f"### \n ⌛ Trimming video frame {start_frame} to {stop_frame}..." try: output_path = os.path.join(output_path, output_name) trimmed_video = trim_video(video_path, output_path, start_frame, stop_frame) yield trimmed_video, "### \n ✔️ Video trimmed and reloaded." except Exception as e: print(e) yield video_path, "### \n ❌ Video trimming failed. See console for more info." ## ------------------------------ GRADIO GUI ------------------------------ css = """ footer{display:none !important} """ with gr.Blocks(css=css) as interface: gr.Markdown("# 🗿 Swap Mukham") gr.Markdown("### Face swap app based on insightface inswapper.") with gr.Row(): with gr.Row(): with gr.Column(scale=0.4): with gr.Tab("📄 Swap Condition"): swap_option = gr.Radio( swap_options_list, show_label=False, value=swap_options_list[0], interactive=True, ) age = gr.Number( value=25, label="Value", interactive=True, visible=False ) with gr.Tab("🎚️ Detection Settings"): detect_condition_dropdown = gr.Dropdown( detect_conditions, label="Condition", value=DETECT_CONDITION, interactive=True, info="This condition is only used when multiple faces are detected on source or specific image.", ) detection_size = gr.Number( label="Detection Size", value=DETECT_SIZE, interactive=True ) detection_threshold = gr.Number( label="Detection Threshold", value=DETECT_THRESH, interactive=True, ) apply_detection_settings = gr.Button("Apply settings") with gr.Tab("📤 Output Settings"): output_directory = gr.Text( label="Output Directory", value=DEF_OUTPUT_PATH, interactive=True, ) output_name = gr.Text( label="Output Name", value="Result", interactive=True ) keep_output_sequence = gr.Checkbox( label="Keep output sequence", value=False, interactive=True ) with gr.Tab("🪄 Other Settings"): with gr.Accordion("Advanced Mask", open=False): enable_face_parser_mask = gr.Checkbox( label="Enable Face Parsing", value=False, interactive=True, ) mask_include = gr.Dropdown( mask_regions.keys(), value=MASK_INCLUDE, multiselect=True, label="Include", interactive=True, ) mask_soft_kernel = gr.Number( label="Soft Erode Kernel", value=MASK_SOFT_KERNEL, minimum=3, interactive=True, visible = False ) mask_soft_iterations = gr.Number( label="Soft Erode Iterations", value=MASK_SOFT_ITERATIONS, minimum=0, interactive=True, ) blur_amount = gr.Number( label="Mask Blur", value=MASK_BLUR_AMOUNT, minimum=0, interactive=True, ) face_scale = gr.Slider( label="Face Scale", minimum=0, maximum=2, value=1, interactive=True, ) with gr.Accordion("Crop Mask", open=False): crop_top = gr.Number(label="Top", value=0, minimum=0, interactive=True) crop_bott = gr.Number(label="Bottom", value=0, minimum=0, interactive=True) crop_left = gr.Number(label="Left", value=0, minimum=0, interactive=True) crop_right = gr.Number(label="Right", value=0, minimum=0, interactive=True) enable_laplacian_blend = gr.Checkbox( label="Laplacian Blending", value=True, interactive=True, ) face_enhancer_name = gr.Dropdown( face_enhancer_list, label="Face Enhancer", value="NONE", multiselect=False, interactive=True ) source_image_input = gr.Image( label="Source face", type="filepath", interactive=True ) with gr.Box(visible=False) as specific_face: for i in range(NUM_OF_SRC_SPECIFIC): idx = i + 1 code = "\n" code += f"with gr.Tab(label='({idx})'):" code += "\n\twith gr.Row():" code += f"\n\t\tsrc{idx} = gr.Image(interactive=True, type='numpy', label='Source Face {idx}')" code += f"\n\t\ttrg{idx} = gr.Image(interactive=True, type='numpy', label='Specific Face {idx}')" exec(code) distance_slider = gr.Slider( minimum=0, maximum=2, value=0.6, interactive=True, label="Distance", info="Lower distance is more similar and higher distance is less similar to the target face.", ) with gr.Group(): input_type = gr.Radio( ["Image", "Video", "Directory"], label="Target Type", value="Video", ) with gr.Box(visible=False) as input_image_group: image_input = gr.Image( label="Target Image", interactive=True, type="filepath" ) with gr.Box(visible=True) as input_video_group: # vid_widget = gr.Video if USE_COLAB else gr.Text video_input = gr.Video( label="Target Video Path", interactive=True ) with gr.Accordion("✂️ Trim video", open=False): with gr.Column(): with gr.Row(): set_slider_range_btn = gr.Button( "Set frame range", interactive=True ) show_trim_preview_btn = gr.Checkbox( label="Show frame when slider change", value=True, interactive=True, ) video_fps = gr.Number( value=30, interactive=False, label="Fps", visible=False, ) start_frame = gr.Slider( minimum=0, maximum=1, value=0, step=1, interactive=True, label="Start Frame", info="", ) end_frame = gr.Slider( minimum=0, maximum=1, value=1, step=1, interactive=True, label="End Frame", info="", ) trim_and_reload_btn = gr.Button( "Trim and Reload", interactive=True ) with gr.Box(visible=False) as input_directory_group: direc_input = gr.Text(label="Path", interactive=True) with gr.Column(scale=0.6): info = gr.Markdown(value="...") with gr.Row(): swap_button = gr.Button("✨ Swap", variant="primary") cancel_button = gr.Button("⛔ Cancel") preview_image = gr.Image(label="Output", interactive=False) preview_video = gr.Video( label="Output", interactive=False, visible=False ) with gr.Row(): output_directory_button = gr.Button( "📂", interactive=False, visible=False ) output_video_button = gr.Button( "🎬", interactive=False, visible=False ) with gr.Column(): gr.Markdown( '[!["Buy Me A Coffee"](https://www.buymeacoffee.com/assets/img/custom_images/orange_img.png)](https://www.buymeacoffee.com/harisreedhar)' ) gr.Markdown( "### [Source code](https://github.com/harisreedhar/Swap-Mukham) . [Disclaimer](https://github.com/harisreedhar/Swap-Mukham#disclaimer) . [Gradio](https://gradio.app/)" ) ## ------------------------------ GRADIO EVENTS ------------------------------ set_slider_range_event = set_slider_range_btn.click( video_changed, inputs=[video_input], outputs=[start_frame, end_frame, video_fps], ) trim_and_reload_event = trim_and_reload_btn.click( fn=trim_and_reload, inputs=[video_input, output_directory, output_name, start_frame, end_frame], outputs=[video_input, info], ) start_frame_event = start_frame.release( fn=slider_changed, inputs=[show_trim_preview_btn, video_input, start_frame], outputs=[preview_image, preview_video], show_progress=True, ) end_frame_event = end_frame.release( fn=slider_changed, inputs=[show_trim_preview_btn, video_input, end_frame], outputs=[preview_image, preview_video], show_progress=True, ) input_type.change( update_radio, inputs=[input_type], outputs=[input_image_group, input_video_group, input_directory_group], ) swap_option.change( swap_option_changed, inputs=[swap_option], outputs=[age, specific_face, source_image_input], ) apply_detection_settings.click( analyse_settings_changed, inputs=[detect_condition_dropdown, detection_size, detection_threshold], outputs=[info], ) src_specific_inputs = [] gen_variable_txt = ",".join( [f"src{i+1}" for i in range(NUM_OF_SRC_SPECIFIC)] + [f"trg{i+1}" for i in range(NUM_OF_SRC_SPECIFIC)] ) exec(f"src_specific_inputs = ({gen_variable_txt})") swap_inputs = [ input_type, image_input, video_input, direc_input, source_image_input, output_directory, output_name, keep_output_sequence, swap_option, age, distance_slider, face_enhancer_name, enable_face_parser_mask, mask_include, mask_soft_kernel, mask_soft_iterations, blur_amount, face_scale, enable_laplacian_blend, crop_top, crop_bott, crop_left, crop_right, *src_specific_inputs, ] swap_outputs = [ info, preview_image, output_directory_button, output_video_button, preview_video, ] swap_event = swap_button.click( fn=process, inputs=swap_inputs, outputs=swap_outputs, show_progress=True ) cancel_button.click( fn=stop_running, inputs=None, outputs=[info], cancels=[ swap_event, trim_and_reload_event, set_slider_range_event, start_frame_event, end_frame_event, ], show_progress=True, ) output_directory_button.click( lambda: open_directory(path=WORKSPACE), inputs=None, outputs=None ) output_video_button.click( lambda: open_directory(path=OUTPUT_FILE), inputs=None, outputs=None ) if __name__ == "__main__": if USE_COLAB: print("Running in colab mode") interface.queue(concurrency_count=2, max_size=20).launch(share=USE_COLAB)