import os import cv2 import glob import time import torch import shutil import gfpgan import argparse import platform import datetime import subprocess import insightface import onnxruntime import numpy as np import gradio as gr from moviepy.editor import VideoFileClip, ImageSequenceClip from face_analyser import detect_conditions, analyse_face from utils import trim_video, StreamerThread, ProcessBar, open_directory from face_parsing import init_parser, swap_regions, mask_regions, mask_regions_to_list, SoftErosion from swapper import ( swap_face, swap_face_with_condition, swap_specific, swap_options_list, ) ## ------------------------------ 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("--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 WORKSPACE = None OUTPUT_FILE = None CURRENT_FRAME = None STREAMER = None DETECT_CONDITION = "left most" 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 ## ------------------------------ 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(name="./assets/pretrained_models/inswapper_128.onnx"): global FACE_SWAPPER path = os.path.join(os.path.abspath(os.path.dirname(__file__)), name) if FACE_SWAPPER is None: FACE_SWAPPER = insightface.model_zoo.get_model(path, providers=PROVIDER) def load_face_enhancer_model(name="./assets/pretrained_models/GFPGANv1.4.pth"): global FACE_ENHANCER path = os.path.join(os.path.abspath(os.path.dirname(__file__)), name) if FACE_ENHANCER is None: FACE_ENHANCER = gfpgan.GFPGANer(model_path=path, upscale=1) def load_face_parser_model(name="./assets/pretrained_models/79999_iter.pth"): global FACE_PARSER path = os.path.join(os.path.abspath(os.path.dirname(__file__)), name) if FACE_PARSER is None: FACE_PARSER = init_parser(name, mode=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_enhance, enable_face_parser, mask_includes, mask_soft_kernel, mask_soft_iterations, blur_amount, *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), ) ## ------------------------------ LOAD PENDING MODELS ------------------------------ start_time = time.time() specifics = list(specifics) half = len(specifics) // 2 sources = specifics[:half] specifics = specifics[half:] 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_enhance: yield "### \n ⌛ Loading face enhancer model...", *ui_before() load_face_enhancer_model() if enable_face_parser: yield "### \n ⌛ Loading face parsing model...", *ui_before() load_face_parser_model() yield "### \n ⌛ Analysing Face...", *ui_before() includes = mask_regions_to_list(mask_includes) if mask_soft_iterations > 0: smooth_mask = SoftErosion(kernel_size=17, threshold=0.9, iterations=int(mask_soft_iterations)).to(device) else: smooth_mask = None models = { "swap": FACE_SWAPPER, "enhance": FACE_ENHANCER, "enhance_sett": face_enhance, "face_parser": FACE_PARSER, "face_parser_sett": (enable_face_parser, includes, smooth_mask, int(blur_amount)) } ## ------------------------------ ANALYSE SOURCE & SPECIFIC ------------------------------ analysed_source_specific = [] if condition == "Specific Face": for source, specific in zip(sources, specifics): if source is None or specific is None: continue analysed_source = analyse_face( source, FACE_ANALYSER, return_single_face=True, detect_condition=DETECT_CONDITION, ) analysed_specific = analyse_face( specific, FACE_ANALYSER, return_single_face=True, detect_condition=DETECT_CONDITION, ) analysed_source_specific.append([analysed_source, analysed_specific]) else: source = cv2.imread(source_path) analysed_source = analyse_face( source, FACE_ANALYSER, return_single_face=True, detect_condition=DETECT_CONDITION, ) ## ------------------------------ IMAGE ------------------------------ if input_type == "Image": target = cv2.imread(image_path) analysed_target = analyse_face(target, FACE_ANALYSER, return_single_face=False) if condition == "Specific Face": swapped = swap_specific( analysed_source_specific, analysed_target, target, models, threshold=distance, ) else: swapped = swap_face_with_condition( target, analysed_target, analysed_source, condition, age, models ) filename = os.path.join(output_path, output_name + ".png") cv2.imwrite(filename, swapped) OUTPUT_FILE = filename WORKSPACE = output_path PREVIEW = swapped[:, :, ::-1] tot_exec_time = time.time() - start_time _min, _sec = divmod(tot_exec_time, 60) yield f"Completed in {int(_min)} min {int(_sec)} sec.", *ui_after() ## ------------------------------ VIDEO ------------------------------ elif input_type == "Video": temp_path = os.path.join(output_path, output_name, "sequence") os.makedirs(temp_path, exist_ok=True) video_clip = VideoFileClip(video_path) duration = video_clip.duration fps = video_clip.fps total_frames = video_clip.reader.nframes analysed_targets = [] process_bar = ProcessBar(30, total_frames) yield "### \n ⌛ Analysing...", *ui_before() for i, frame in enumerate(video_clip.iter_frames()): analysed_targets.append( analyse_face(frame, FACE_ANALYSER, return_single_face=False) ) info_text = "Analysing Faces || " info_text += process_bar.get(i) print("\033[1A\033[K", end="", flush=True) print(info_text) if i % 10 == 0: yield "### \n" + info_text, *ui_before() video_clip.close() image_sequence = [] video_clip = VideoFileClip(video_path) audio_clip = video_clip.audio if video_clip.audio is not None else None process_bar = ProcessBar(30, total_frames) yield "### \n ⌛ Swapping...", *ui_before() for i, frame in enumerate(video_clip.iter_frames()): swapped = frame analysed_target = analysed_targets[i] if condition == "Specific Face": swapped = swap_specific( analysed_source_specific, analysed_target, frame, models, threshold=distance, ) else: swapped = swap_face_with_condition( frame, analysed_target, analysed_source, condition, age, models ) image_path = os.path.join(temp_path, f"frame_{i}.png") cv2.imwrite(image_path, swapped[:, :, ::-1]) image_sequence.append(image_path) info_text = "Swapping Faces || " info_text += process_bar.get(i) print("\033[1A\033[K", end="", flush=True) print(info_text) if i % 6 == 0: PREVIEW = swapped yield "### \n" + info_text, *ui_before() yield "### \n ⌛ Merging...", *ui_before() edited_video_clip = ImageSequenceClip(image_sequence, fps=fps) if audio_clip is not None: edited_video_clip = edited_video_clip.set_audio(audio_clip) output_video_path = os.path.join(output_path, output_name + ".mp4") edited_video_clip.set_duration(duration).write_videofile( output_video_path, codec="libx264" ) edited_video_clip.close() video_clip.close() 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 tot_exec_time = time.time() - start_time _min, _sec = divmod(tot_exec_time, 60) yield f"✔️ Completed in {int(_min)} min {int(_sec)} sec.", *ui_after_vid() ## ------------------------------ DIRECTORY ------------------------------ elif input_type == "Directory": source = cv2.imread(source_path) source = analyse_face( source, FACE_ANALYSER, return_single_face=True, detect_condition=DETECT_CONDITION, ) 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) swapped = None files = [] for file_path in glob.glob(os.path.join(directory_path, "*")): if any(file_path.lower().endswith(ext) for ext in extensions): files.append(file_path) files_length = len(files) filename = None for i, file_path in enumerate(files): target = cv2.imread(file_path) analysed_target = analyse_face( target, FACE_ANALYSER, return_single_face=False ) if condition == "Specific Face": swapped = swap_specific( analysed_source_specific, analysed_target, target, models, threshold=distance, ) else: swapped = swap_face_with_condition( target, analysed_target, analysed_source, condition, age, models ) filename = os.path.join(temp_path, os.path.basename(file_path)) cv2.imwrite(filename, swapped) info_text = f"### \n ⌛ Processing file {i+1} of {files_length}" PREVIEW = swapped[:, :, ::-1] yield info_text, *ui_before() WORKSPACE = temp_path OUTPUT_FILE = filename tot_exec_time = time.time() - start_time _min, _sec = divmod(tot_exec_time, 60) yield f"✔️ Completed in {int(_min)} min {int(_sec)} sec.", *ui_after() ## ------------------------------ STREAM ------------------------------ elif input_type == "Stream": yield "### \n ⌛ Starting...", *ui_before() global STREAMER STREAMER = StreamerThread(src=directory_path) STREAMER.start() while True: try: target = STREAMER.frame analysed_target = analyse_face( target, FACE_ANALYSER, return_single_face=False ) if condition == "Specific Face": swapped = swap_specific( target, analysed_target, analysed_source_specific, models, threshold=distance, ) else: swapped = swap_face_with_condition( target, analysed_target, analysed_source, condition, age, models ) PREVIEW = swapped[:, :, ::-1] yield f"Streaming...", *ui_before() except AttributeError: yield "Streaming...", *ui_before() STREAMER.stop() ## ------------------------------ 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("Enhance Face", open=True): enable_face_enhance = gr.Checkbox( label="Enable GFPGAN", value=False, interactive=True ) 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, ) 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", "Stream"], 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 #gr.Video if USE_COLAB else gr.Text video_input = vid_widget( 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=False, ) 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=False, ) 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, enable_face_enhance, enable_face_parser_mask, mask_include, mask_soft_kernel, mask_soft_iterations, blur_amount, *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=False ) 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=False, ) 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)