app.py

import os
os.environ["TRANSFORMERS_NO_TF"] = "1"  # force PyTorch-only pipelines

import numpy as np
from PIL import Image, ImageFilter
import gradio as gr
import torch
from transformers import pipeline

# ---- Config ----
DEVICE = 0 if torch.cuda.is_available() else -1
SEG_MODEL = "nvidia/segformer-b0-finetuned-ade-512-512"
DEPTH_MODEL = "Intel/dpt-hybrid-midas"
SIZE = (512, 512)

# ---- Pipelines (loaded once) ----
seg_pipe = pipeline("image-segmentation", model=SEG_MODEL, device=DEVICE, framework="pt")
depth_pipe = pipeline("depth-estimation", model=DEPTH_MODEL, device=DEVICE, framework="pt")

# ---- Helpers ----
def resize_center_crop(img: Image.Image, size=SIZE) -> Image.Image:
    img = img.convert("RGB")
    w, h = img.size
    tw, th = size
    s = max(tw / w, th / h)
    nw, nh = int(round(w * s)), int(round(h * s))
    img = img.resize((nw, nh), Image.BICUBIC)
    left, top = (nw - tw) // 2, (nh - th) // 2
    return img.crop((left, top, left + tw, top + th))

def person_mask(img_512: Image.Image) -> Image.Image:
    results = seg_pipe(img_512)
    person = next((r for r in results if r.get("label", "").lower() == "person"), None)
    if person is None:
        person = next((r for r in results if "person" in r.get("label", "").lower()), None)
    if person is None:
        return Image.new("L", img_512.size, 0)  # no person detected
    m = person["mask"].convert("L")
    m = (np.array(m) > 127).astype(np.uint8) * 255
    return Image.fromarray(m, mode="L")

def gaussian_bg_blur(img_512: Image.Image, sigma: int = 15) -> Image.Image:
    m = person_mask(img_512)
    blurred = img_512.filter(ImageFilter.GaussianBlur(radius=int(sigma)))
    return Image.composite(img_512, blurred, m)  # white=person -> keep sharp

def depth_lens_blur(img_512: Image.Image, max_radius: int = 15, keep_subject: bool = True) -> Image.Image:
    out = depth_pipe(img_512)
    d = out["depth"].resize(SIZE, Image.BICUBIC)
    dnp = np.array(d).astype(np.float32)
    d01 = (dnp - dnp.min()) / (dnp.max() - dnp.min() + 1e-8)  # 0..1
    far = 1.0 - d01  # larger=farther -> more blur
    if keep_subject:
        m = person_mask(img_512)
        m01 = (np.array(m) > 127).astype(np.float32)
        far = far * (1.0 - 0.85 * m01)  # suppress blur on detected subject
    max_radius = int(max(0, min(30, max_radius)))
    idx = np.clip(np.rint(far * max_radius).astype(np.int32), 0, max_radius)
    stack = [img_512 if r == 0 else img_512.filter(ImageFilter.GaussianBlur(radius=r))
             for r in range(max_radius + 1)]
    stack_np = np.stack([np.array(im) for im in stack], axis=0)  # [R+1,H,W,3]
    H, W = idx.shape
    h = np.arange(H)[:, None]; w = np.arange(W)[None, :]
    out_np = stack_np[idx, h, w]
    return Image.fromarray(out_np.astype(np.uint8))

def run(image, effect, sigma, max_radius, keep_subject):
    if image is None:
        return None, None
    img_512 = resize_center_crop(image, SIZE)
    if effect == "Gaussian Background Blur (subject sharp)":
        out = gaussian_bg_blur(img_512, sigma=int(sigma))
    else:
        out = depth_lens_blur(img_512, max_radius=int(max_radius), keep_subject=bool(keep_subject))
    return img_512, out

# ---- UI ----
with gr.Blocks(title="Gaussian & Lens Blur Lab") as demo:
    gr.Markdown("# Gaussian & Lens Blur Lab\nUpload an image and compare effects.")
    with gr.Row():
        with gr.Column():
            in_img = gr.Image(type="pil", label="Upload image")
            effect = gr.Radio(
                ["Gaussian Background Blur (subject sharp)", "Depth-based Lens Blur"],
                value="Gaussian Background Blur (subject sharp)", label="Effect"
            )
            sigma = gr.Slider(1, 40, value=15, step=1, label="Gaussian sigma")
            max_r = gr.Slider(4, 30, value=15, step=1, label="Max blur radius (lens blur)")
            keep = gr.Checkbox(True, label="Keep detected subject sharper (lens blur)")
            btn = gr.Button("Run")
        with gr.Column():
            out_a = gr.Image(label="Preprocessed 512×512")
            out_b = gr.Image(label="Result")
    btn.click(run, inputs=[in_img, effect, sigma, max_r, keep], outputs=[out_a, out_b])

if __name__ == "__main__":
    demo.launch()