feat: Continuidad Facial - versión limpia con Xet

.gitattributes

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README.md

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+---
+title: Continuidad Facial
+emoji: 🏢
+colorFrom: indigo
+colorTo: blue
+sdk: gradio
+sdk_version: 5.49.1
+app_file: app.py
+pinned: false
+license: mit
+short_description: Continuidad facial para proyectos audiovisuales
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import os
+import gradio as gr
+from gradio_client import Client, handle_file
+import tempfile
+import cv2
+import numpy as np
+import torch
+from moviepy.editor import VideoFileClip
+
+# ===== Crear enlaces simbólicos para pesos de facexlib (están en raíz) =====
+GFPGAN_WEIGHTS_DIR = "gfpgan/weights"
+os.makedirs(GFPGAN_WEIGHTS_DIR, exist_ok=True)
+for model in ["detection_Resnet50_Final.pth", "parsing_parsenet.pth"]:
+    src = model
+    dst = os.path.join(GFPGAN_WEIGHTS_DIR, model)
+    if os.path.exists(src) and not os.path.exists(dst):
+        os.symlink(os.path.abspath(src), os.path.abspath(dst))
+        print(f"🔗 Enlace simbólico creado: {dst} → {src}")
+
+# ===== Configuración =====
+HF_FS_TOKEN = os.environ.get("FS")
+MOTOR_PRIVADO = "Malaji71/motor_cambiarcaras"
+GFPGAN_ENABLED = os.path.exists("GFPGANv1.4.pth")
+
+# ===== Cargar FaceAnalysis (opcional) =====
+try:
+    from insightface.app import FaceAnalysis
+    face_analyser = FaceAnalysis(name='buffalo_l', providers=['CPUExecutionProvider'])
+    face_analyser.prepare(ctx_id=0, det_size=(640, 640))
+    FACE_ANALYSIS_LOADED = True
+    print("✓ FaceAnalysis cargado")
+except Exception as e:
+    print("⚠️ FaceAnalysis no disponible:", e)
+    FACE_ANALYSIS_LOADED = False
+
+# ===== Cargar Real-ESRGAN =====
+try:
+    from basicsr.archs.rrdbnet_arch import RRDBNet
+    from realesrgan import RealESRGANer
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    realesrgan_model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
+    realesrgan_upsampler = RealESRGANer(
+        scale=4,
+        model_path="RealESRGAN_x4plus.pth",
+        model=realesrgan_model,
+        tile=0,
+        tile_pad=10,
+        pre_pad=0,
+        half=False,
+        device=device
+    )
+    ESRGAN_ENABLED = True
+    print("✓ Real-ESRGAN x4+ cargado")
+except Exception as e:
+    print("⚠️ Real-ESRGAN no disponible:", e)
+    ESRGAN_ENABLED = False
+
+def ensure_3_channels(img):
+    if len(img.shape) == 2:
+        return cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+    elif img.shape[2] == 1:
+        return cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+    elif img.shape[2] == 4:
+        return cv2.cvtColor(img, cv2.COLOR_BGRA2BGR)
+    return img
+
+def enhance_after_esrgan(image_np, device):
+    image_np = ensure_3_channels(image_np)
+    if device.type == 'cuda':
+        img_rgb = cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB)
+        img_tensor = torch.from_numpy(img_rgb).float().to(device) / 255.0
+        img_tensor = img_tensor.permute(2, 0, 1).unsqueeze(0)
+        with torch.no_grad():
+            kernel = torch.tensor([[0, -1, 0], [-1, 5, -1], [0, -1, 0]], dtype=torch.float32, device=device)
+            kernel = kernel.view(1, 1, 3, 3)
+            padded = torch.nn.functional.pad(img_tensor, (1,1,1,1), mode='reflect')
+            sharpened = torch.nn.functional.conv2d(padded, kernel.repeat(3,1,1,1), groups=3)
+            result = 0.8 * sharpened + 0.2 * img_tensor
+            result = torch.clamp(result, 0, 1)
+        result_np = result.squeeze(0).permute(1, 2, 0).cpu().numpy()
+        return cv2.cvtColor((result_np * 255).astype(np.uint8), cv2.COLOR_RGB2BGR)
+    else:
+        kernel = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]])
+        sharpened = cv2.filter2D(image_np, -1, kernel)
+        blended = cv2.addWeighted(image_np, 0.2, sharpened, 0.8, 0)
+        return np.clip(blended, 0, 255).astype(np.uint8)
+
+# ===== Mejorar SOLO el rostro =====
+def mejorar_rostro_en_imagen(image_path):
+    if not FACE_ANALYSIS_LOADED or not ESRGAN_ENABLED:
+        return image_path
+    img = cv2.imread(image_path, cv2.IMREAD_COLOR)
+    if img is None:
+        return image_path
+    faces = face_analyser.get(img)
+    if not faces:
+        return image_path
+    face = faces[0]
+    x1, y1, x2, y2 = map(int, face.bbox)
+    h, w = img.shape[:2]
+    x1, y1 = max(0, x1), max(0, y1)
+    x2, y2 = min(w, x2), min(h, y2)
+    if x2 <= x1 or y2 <= y1:
+        return image_path
+    face_lowres = img[y1:y2, x1:x2]
+    if face_lowres.size == 0:
+        return image_path
+    try:
+        face_highres, _ = realesrgan_upsampler.enhance(face_lowres, outscale=4)
+        face_highres = enhance_after_esrgan(face_highres, device)
+    except Exception as e:
+        print("Real-ESRGAN (rostro) falló:", e)
+        face_highres = cv2.resize(face_lowres, None, fx=4, fy=4, interpolation=cv2.INTER_LANCZOS4)
+    mask_low = np.zeros((y2 - y1, x2 - x1), dtype=np.float32)
+    center = ((x2 - x1) // 2, (y2 - y1) // 2)
+    radius = int(min(center[0], center[1]) * 0.9)
+    cv2.circle(mask_low, center, radius, 1.0, -1)
+    mask_low = cv2.GaussianBlur(mask_low, (21, 21), 0)
+    mask_high = cv2.resize(mask_low, (face_highres.shape[1], face_highres.shape[0]), interpolation=cv2.INTER_LINEAR)
+    original_region_high = cv2.resize(img[y1:y2, x1:x2], (face_highres.shape[1], face_highres.shape[0]), interpolation=cv2.INTER_LINEAR)
+    blended_high = (mask_high[..., None] * face_highres + (1 - mask_high[..., None]) * original_region_high).astype(np.uint8)
+    blended_original_size = cv2.resize(blended_high, (x2 - x1, y2 - y1), interpolation=cv2.INTER_AREA)
+    result = img.copy()
+    result[y1:y2, x1:x2] = blended_original_size
+    out_path = os.path.join(tempfile.gettempdir(), f"rostro_mejorado_{os.urandom(4).hex()}.png")
+    cv2.imwrite(out_path, result)
+    return out_path
+
+# ===== GFPGAN: 100% local, sin descargas, con pesos en raíz =====
+def aplicar_gfpgan_local(image_path, weight=0.7, has_aligned=False, only_center_face=True):
+    if not GFPGAN_ENABLED:
+        return image_path
+    try:
+        from gfpgan import GFPGANer
+        gfpgan = GFPGANer(
+            model_path="GFPGANv1.4.pth",
+            upscale=1,
+            arch='clean',
+            channel_multiplier=2,
+            bg_upsampler=None
+        )
+        img = cv2.imread(image_path, cv2.IMREAD_UNCHANGED)
+        if img is None:
+            return image_path
+        if len(img.shape) == 2:
+            img = np.stack([img] * 3, axis=-1)
+        elif img.shape[2] == 1:
+            img = np.stack([img.squeeze()] * 3, axis=-1)
+        elif img.shape[2] == 4:
+            img = cv2.cvtColor(img, cv2.COLOR_BGRA2BGR)
+        if img.dtype != np.uint8:
+            img = np.clip(img * 255, 0, 255).astype(np.uint8)
+        _, _, output = gfpgan.enhance(
+            img[:, :, ::-1],
+            has_aligned=has_aligned,
+            only_center_face=only_center_face,
+            paste_back=True
+        )
+        if output is not None and len(output) > 0:
+            restored = output[0][:, :, ::-1]
+            if restored.shape != img.shape:
+                restored = cv2.resize(restored, (img.shape[1], img.shape[0]))
+            blended = cv2.addWeighted(img, 1 - weight, restored, weight, 0)
+            out_path = os.path.join(tempfile.gettempdir(), f"gfpgan_{os.urandom(4).hex()}.png")
+            cv2.imwrite(out_path, blended)
+            return out_path
+        return image_path
+    except Exception as e:
+        print("GFPGAN falló:", e)
+        return image_path
+
+# ===== CodeFormer (remoto, sin escalado previo) =====
+def aplicar_codeformer_remoto(image_path, face_align=True, background_enhance=False, face_upsample=True, upscale=2, fidelity=0.5):
+    try:
+        client = Client("sczhou/CodeFormer")
+        result = client.predict(
+            image=handle_file(image_path),
+            face_align=face_align,
+            background_enhance=background_enhance,
+            face_upsample=face_upsample,
+            upscale=upscale,
+            codeformer_fidelity=fidelity,
+            api_name="/predict"
+        )
+        return result
+    except Exception as e:
+        print("CodeFormer falló:", e)
+        return image_path
+
+# ===== Procesamiento: Imagen (usa motor + post-procesado) =====
+def procesar_imagen(img_fuente, img_objetivo, modo_mejora,
+                    gfpgan_weight, gfpgan_has_aligned, gfpgan_only_center_face,
+                    cf_face_align, cf_background_enhance, cf_face_upsample, cf_upscale, cf_fidelity):
+    cliente = Client(MOTOR_PRIVADO, hf_token=HF_FS_TOKEN)
+    resultado_bruto = cliente.predict(
+        source_img_path=handle_file(img_fuente),
+        target_img_path=handle_file(img_objetivo),
+        api_name="////swap_image"
+    )
+    resultado = resultado_bruto
+    if modo_mejora != "Ninguno":
+        if ESRGAN_ENABLED and FACE_ANALYSIS_LOADED:
+            resultado = mejorar_rostro_en_imagen(resultado)
+        if modo_mejora == "GFPGAN (realismo textural)":
+            return aplicar_gfpgan_local(resultado, gfpgan_weight, gfpgan_has_aligned, gfpgan_only_center_face)
+        elif modo_mejora == "CodeFormer (calidad + identidad)":
+            return aplicar_codeformer_remoto(
+                resultado, cf_face_align, cf_background_enhance, cf_face_upsample, cf_upscale, cf_fidelity
+            )
+    return resultado
+
+# ===== Procesamiento: Video =====
+def procesar_video(img_fuente, vid_objetivo, modo_mejora,
+                   gfpgan_weight, gfpgan_has_aligned, gfpgan_only_center_face,
+                   cf_face_align, cf_background_enhance, cf_face_upsample, cf_upscale, cf_fidelity,
+                   duracion_max):
+    cliente = Client(MOTOR_PRIVADO, hf_token=HF_FS_TOKEN)
+    resultado_bruto = cliente.predict(
+        source_img_path=handle_file(img_fuente),
+        target_video_path={"video": handle_file(vid_objetivo)},
+        api_name="////swap_video"
+    )
+    video_path = resultado_bruto["video"] if isinstance(resultado_bruto, dict) else resultado_bruto
+    if modo_mejora == "Ninguno":
+        return video_path
+    clip = VideoFileClip(video_path)
+    if clip.duration > duracion_max:
+        clip = clip.subclip(0, duracion_max)
+    fps, size = clip.fps, (int(clip.size[0]), int(clip.size[1]))
+    output_path = os.path.join(tempfile.gettempdir(), f"vid_proc_{os.urandom(8).hex()}.mp4")
+    writer = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, size)
+    for frame_rgb in clip.iter_frames():
+        frame_bgr = cv2.cvtColor(frame_rgb, cv2.COLOR_RGB2BGR)
+        temp_frame = os.path.join(tempfile.gettempdir(), f"tmp_{os.urandom(4).hex()}.png")
+        cv2.imwrite(temp_frame, frame_bgr)
+        if ESRGAN_ENABLED and FACE_ANALYSIS_LOADED:
+            temp_frame = mejorar_rostro_en_imagen(temp_frame)
+        if modo_mejora == "GFPGAN (realismo textural)":
+            temp_frame = aplicar_gfpgan_local(temp_frame, gfpgan_weight, gfpgan_has_aligned, gfpgan_only_center_face)
+        elif modo_mejora == "CodeFormer (calidad + identidad)":
+            temp_frame = aplicar_codeformer_remoto(
+                temp_frame, cf_face_align, cf_background_enhance, cf_face_upsample, cf_upscale, cf_fidelity
+            )
+        proc = cv2.imread(temp_frame, cv2.IMREAD_COLOR)
+        if proc is not None and proc.shape[:2] == (size[1], size[0]):
+            writer.write(proc)
+        else:
+            writer.write(frame_bgr)
+        if os.path.exists(temp_frame):
+            os.remove(temp_frame)
+    writer.release()
+    clip.close()
+    return output_path
+
+# ===== Restauración: SOLO mejora, SIN motor =====
+def restaurar_imagen(imagen, metodo, 
+                    cf_face_align, cf_background_enhance, cf_face_upsample, cf_upscale, cf_fidelity,
+                    gf_weight, gf_has_aligned, gf_only_center_face):
+    if metodo == "CodeFormer (calidad + identidad)":
+        return aplicar_codeformer_remoto(
+            imagen, cf_face_align, cf_background_enhance, cf_face_upsample, cf_upscale, cf_fidelity
+        )
+    elif metodo == "GFPGAN (restauración facial local)":
+        return aplicar_gfpgan_local(imagen, gf_weight, gf_has_aligned, gf_only_center_face)
+    else:
+        return imagen
+
+# ===== Interfaz en castellano =====
+with gr.Blocks(title="Estudio de Personajes") as demo:
+    gr.Markdown("# 🎭 Estudio de Personajes")
+    gr.Markdown(
+        "Herramienta abierta de inteligencia artificial para garantizar **continuidad visual de personajes** "
+        "en proyectos audiovisuales, documentales y narrativas históricas."
+    )
+
+    with gr.Tab("Imagen"):
+        with gr.Row():
+            with gr.Column():
+                img_src = gr.Image(type="filepath", label="Rostro a mantener")
+                img_tgt = gr.Image(type="filepath", label="Imagen objetivo")
+                modo_img = gr.Radio(
+                    choices=["Ninguno", "CodeFormer (calidad + identidad)", "GFPGAN (realismo textural)"],
+                    value="Ninguno",
+                    label="Post-procesamiento"
+                )
+                with gr.Row(visible=False) as cf_panel_img:
+                    gr.Markdown("### 🧠 CodeFormer: restauración avanzada con control de fidelidad")
+                    with gr.Column():
+                        cf_fa_img = gr.Checkbox(True, label="Alinear rostro")
+                        gr.Markdown("_Corrige inclinación para mejor procesamiento._")
+                        cf_be_img = gr.Checkbox(False, label="Mejorar fondo")
+                        gr.Markdown("_Mejora todo el fondo (puede alterar texturas no faciales)._")
+                        cf_fu_img = gr.Checkbox(True, label="Aumentar resolución facial")
+                        gr.Markdown("_Escalado previo al refinamiento para más detalle._")
+                    with gr.Column():
+                        cf_up_img = gr.Slider(1, 4, value=2, step=1, label="Factor de escalado")
+                        gr.Markdown("_1: original, 4: máxima resolución._")
+                        cf_fid_img = gr.Slider(0.0, 1.0, value=0.5, label="Fidelidad al rostro original")
+                        gr.Markdown("_**0**: calidad óptima, **1**: identidad fiel._")
+                with gr.Row(visible=False) as gf_panel_img:
+                    gr.Markdown("### 👁️ GFPGAN: restauración facial realista (local)")
+                    with gr.Column():
+                        gf_w_img = gr.Slider(0.0, 1.0, value=0.7, label="Intensidad de restauración")
+                        gr.Markdown("_0: sin efecto, 1: restauración total._")
+                        gf_ha_img = gr.Checkbox(False, label="Rostro pre-alineado")
+                        gr.Markdown("_Solo marcar si el rostro ya está centrado y recto._")
+                        gf_oc_img = gr.Checkbox(True, label="Procesar solo rostro principal")
+                        gr.Markdown("_Ideal para retratos con una sola persona._")
+                def update_vis(m):
+                    return (
+                        gr.Row(visible=(m == "CodeFormer (calidad + identidad)")),
+                        gr.Row(visible=(m == "GFPGAN (realismo textural)"))
+                    )
+                modo_img.change(update_vis, inputs=modo_img, outputs=[cf_panel_img, gf_panel_img])
+                btn_img = gr.Button("Generar personaje", variant="primary")
+            with gr.Column():
+                out_img = gr.Image(label="Resultado")
+        btn_img.click(
+            procesar_imagen,
+            [img_src, img_tgt, modo_img, gf_w_img, gf_ha_img, gf_oc_img, cf_fa_img, cf_be_img, cf_fu_img, cf_up_img, cf_fid_img],
+            out_img
+        )
+
+    with gr.Tab("Video"):
+        gr.Markdown("⚠️ **El procesamiento de video con post-procesado puede tardar varios minutos.**")
+        with gr.Row():
+            with gr.Column():
+                vid_src = gr.Image(type="filepath", label="Rostro a mantener")
+                vid_tgt = gr.Video(label="Video objetivo")
+                modo_vid = gr.Radio(
+                    choices=["Ninguno", "CodeFormer (calidad + identidad)", "GFPGAN (realismo textural)"],
+                    value="Ninguno",
+                    label="Post-procesamiento"
+                )
+                with gr.Row(visible=False) as cf_panel_vid:
+                    gr.Markdown("### 🧠 CodeFormer: restauración avanzada con control de fidelidad")
+                    with gr.Column():
+                        cf_fa_vid = gr.Checkbox(True, label="Alinear rostro")
+                        gr.Markdown("_Corrige inclinación para mejor procesamiento._")
+                        cf_be_vid = gr.Checkbox(False, label="Mejorar fondo")
+                        gr.Markdown("_Mejora todo el fondo (puede alterar texturas no faciales)._")
+                        cf_fu_vid = gr.Checkbox(True, label="Aumentar resolución facial")
+                        gr.Markdown("_Escalado previo al refinamiento para más detalle._")
+                    with gr.Column():
+                        cf_up_vid = gr.Slider(1, 4, value=2, step=1, label="Factor de escalado")
+                        gr.Markdown("_1: original, 4: máxima resolución._")
+                        cf_fid_vid = gr.Slider(0.0, 1.0, value=0.5, label="Fidelidad al rostro original")
+                        gr.Markdown("_**0**: calidad óptima, **1**: identidad fiel._")
+                with gr.Row(visible=False) as gf_panel_vid:
+                    gr.Markdown("### 👁️ GFPGAN: restauración facial realista (local)")
+                    with gr.Column():
+                        gf_w_vid = gr.Slider(0.0, 1.0, value=0.7, label="Intensidad de restauración")
+                        gr.Markdown("_0: sin efecto, 1: restauración total._")
+                        gf_ha_vid = gr.Checkbox(False, label="Rostro pre-alineado")
+                        gr.Markdown("_Solo marcar si el rostro ya está centrado y recto._")
+                        gf_oc_vid = gr.Checkbox(True, label="Procesar solo rostro principal")
+                        gr.Markdown("_Ideal para retratos con una sola persona._")
+                modo_vid.change(update_vis, inputs=modo_vid, outputs=[cf_panel_vid, gf_panel_vid])
+                dur_vid = gr.Slider(5, 60, value=10, label="Duración máxima (segundos)")
+                btn_vid = gr.Button("Generar video con personaje", variant="primary")
+            with gr.Column():
+                out_vid = gr.Video(label="Resultado")
+        btn_vid.click(
+            procesar_video,
+            [vid_src, vid_tgt, modo_vid, gf_w_vid, gf_ha_vid, gf_oc_vid, cf_fa_vid, cf_be_vid, cf_fu_vid, cf_up_vid, cf_fid_vid, dur_vid],
+            out_vid
+        )
+
+    with gr.Tab("Restauración de imágenes"):
+        gr.Markdown("Mejora imágenes antiguas o dañadas antes de usarlas en intercambio facial.")
+        with gr.Row():
+            with gr.Column():
+                img_r = gr.Image(type="filepath", label="Imagen a restaurar")
+                modo_r = gr.Radio(
+                    choices=["Ninguno", "CodeFormer (calidad + identidad)", "GFPGAN (restauración facial local)"],
+                    value="Ninguno",
+                    label="Método"
+                )
+                with gr.Row(visible=False) as cf_r:
+                    gr.Markdown("### 🧠 CodeFormer: restauración avanzada con control de fidelidad")
+                    with gr.Column():
+                        cf_fa_r = gr.Checkbox(True, label="Alinear rostro")
+                        gr.Markdown("_Corrige inclinación para mejor procesamiento._")
+                        cf_be_r = gr.Checkbox(False, label="Mejorar fondo")
+                        gr.Markdown("_Mejora todo el fondo (puede alterar texturas no faciales)._")
+                        cf_fu_r = gr.Checkbox(True, label="Aumentar resolución facial")
+                        gr.Markdown("_Escalado previo al refinamiento para más detalle._")
+                    with gr.Column():
+                        cf_up_r = gr.Slider(1, 4, value=2, step=1, label="Factor de escalado")
+                        gr.Markdown("_1: original, 4: máxima resolución._")
+                        cf_fid_r = gr.Slider(0.0, 1.0, value=0.5, label="Fidelidad al rostro original")
+                        gr.Markdown("_**0**: calidad óptima, **1**: identidad fiel._")
+                with gr.Row(visible=False) as gf_r:
+                    gr.Markdown("### 👁️ GFPGAN: restauración facial realista (local)")
+                    with gr.Column():
+                        gf_w_r = gr.Slider(0.0, 1.0, value=0.7, label="Intensidad de restauración")
+                        gr.Markdown("_0: sin efecto, 1: restauración total._")
+                        gf_ha_r = gr.Checkbox(False, label="Rostro pre-alineado")
+                        gr.Markdown("_Solo marcar si el rostro ya está centrado y recto._")
+                        gf_oc_r = gr.Checkbox(True, label="Procesar solo rostro principal")
+                        gr.Markdown("_Ideal para retratos con una sola persona._")
+                modo_r.change(update_vis, inputs=modo_r, outputs=[cf_r, gf_r])
+                btn_r = gr.Button("Restaurar", variant="secondary")
+            with gr.Column():
+                out_r = gr.Image(label="Resultado")
+        btn_r.click(
+            restaurar_imagen,
+            [img_r, modo_r, cf_fa_r, cf_be_r, cf_fu_r, cf_up_r, cf_fid_r, gf_w_r, gf_ha_r, gf_oc_r],
+            out_r
+        )
+
+    # ✅ Dedicatoria final
+    gr.Markdown(
+        "Creado por Angel E. Pariente 🇬🇹<br>"
+        "Dedicado a David Esteban Cubero y a la <a href='https://cursosdeguion.com/' target='_blank'>Academia Guiones y guionistas</a> 🇺🇾"
+    )
+
+demo.launch()

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