Delete app.py

app.py

@@ -1,126 +0,0 @@
-import gradio as gr
-import cv2
-import numpy as np
-from PIL import Image
-import torch
-import face_alignment
-import insightface
-from scipy import stats
-
-# -------------------- Device --------------------
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-# -------------------- Face Alignment --------------------
-fa = face_alignment.FaceAlignment(
-    face_alignment.LandmarksType["2D"],
-    device=device,
-    flip_input=False
-)
-
-# -------------------- Identity Model --------------------
-face_analyzer = insightface.app.FaceAnalysis(name="buffalo_l")
-face_analyzer.prepare(ctx_id=0 if device == "cuda" else -1)
-
-# -------------------- Utilities --------------------
-def pil_to_cv(img):
-    return cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
-
-def cv_to_pil(img):
-    return Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
-
-def get_landmarks(img):
-    preds = fa.get_landmarks(np.array(img))
-    if preds is None or len(preds) == 0:
-        return None
-    return preds[0].astype(np.float32)  # (68,2)
-
-def align_face(src, tgt):
-    src_lm = get_landmarks(src)
-    tgt_lm = get_landmarks(tgt)
-    if src_lm is None or tgt_lm is None:
-        return None
-
-    # Use 5 key landmarks for affine transform: eyes, nose tip, mouth corners
-    idx = [36, 45, 30, 48, 54]
-    M, _ = cv2.estimateAffinePartial2D(src_lm[idx], tgt_lm[idx])
-    if M is None:
-        return None
-
-    aligned = cv2.warpAffine(
-        pil_to_cv(src),
-        M,
-        (tgt.width, tgt.height),
-        flags=cv2.INTER_LINEAR
-    )
-    return cv_to_pil(aligned)
-
-def identity_similarity(a, b):
-    ea = face_analyzer.get(np.array(a))
-    eb = face_analyzer.get(np.array(b))
-    if not ea or not eb:
-        return 0.0
-    v1 = ea[0].embedding
-    v2 = eb[0].embedding
-    return float(np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2)))
-
-def color_match(src, tgt):
-    src_lab = cv2.cvtColor(src, cv2.COLOR_BGR2LAB)
-    tgt_lab = cv2.cvtColor(tgt, cv2.COLOR_BGR2LAB)
-
-    for i in range(3):
-        s = src_lab[:, :, i].flatten()
-        t = tgt_lab[:, :, i].flatten()
-        s_rank = stats.rankdata(s)
-        s_norm = (s_rank - s_rank.min()) / (s_rank.max() - s_rank.min() + 1e-6)
-        t_sorted = np.sort(t)
-        src_lab[:, :, i] = t_sorted[(s_norm * (len(t_sorted) - 1)).astype(int)].reshape(src_lab[:, :, i].shape)
-
-    return cv2.cvtColor(src_lab.astype(np.uint8), cv2.COLOR_LAB2BGR)
-
-# -------------------- Core Face Swap --------------------
-def face_swap(src_img, tgt_img):
-    if src_img is None or tgt_img is None:
-        return "Upload both images", None
-
-    aligned = align_face(src_img, tgt_img)
-    if aligned is None:
-        return "Face alignment failed", None
-
-    src_cv = pil_to_cv(aligned)
-    tgt_cv = pil_to_cv(tgt_img)
-
-    # Color harmonization
-    src_cv = color_match(src_cv, tgt_cv)
-
-    # Poisson blending
-    mask = 255 * np.ones(src_cv.shape[:2], dtype=np.uint8)
-    center = (tgt_cv.shape[1] // 2, tgt_cv.shape[0] // 2)
-    blended = cv2.seamlessClone(src_cv, tgt_cv, mask, center, cv2.NORMAL_CLONE)
-
-    result = cv_to_pil(blended)
-
-    # Identity validation
-    sim = identity_similarity(src_img, result)
-    if sim < 0.94:
-        return f"Identity similarity too low: {sim:.3f}", result
-
-    return f"Identity similarity OK: {sim:.3f}", result
-
-# -------------------- Gradio UI --------------------
-with gr.Blocks() as demo:
-    gr.Markdown("## Ultra-Realistic Face Swap (Photographic Only)")
-    gr.Markdown(
-        "- Strict 2D face swap\n"
-        "- Identity similarity ≥0.94\n"
-        "- Zero AI / 3D look\n"
-    )
-
-    src = gr.Image(label="Source Face", type="pil")
-    tgt = gr.Image(label="Target Image", type="pil")
-    btn = gr.Button("Run Face Swap")
-    status = gr.Textbox(label="Status")
-    output = gr.Image(label="Result")
-
-    btn.click(face_swap, [src, tgt], [status, output])
-
-demo.launch()