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2c6bbf8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 | import gradio as gr
import numpy as np
from PIL import Image
import torch
import torchvision.transforms as T
from skimage import color as skcolor
# -----------------------------
# DEVICE
# -----------------------------
DEVICE = "cpu"
# -----------------------------
# TEST MODE (subtle tint)
# -----------------------------
def test_mode_colorize(pil_img: Image.Image) -> Image.Image:
img = pil_img.convert("RGB")
arr = np.array(img).astype(np.float32) / 255.0
tint = np.array([1.02, 1.0, 0.98], dtype=np.float32)
arr = np.clip(arr * tint, 0.0, 1.0)
arr = (arr * 255).astype(np.uint8)
return Image.fromarray(arr)
# -----------------------------
# ZHANG ECCV16 COLORIZER
# -----------------------------
try:
import colorizers
_ZHANG_AVAILABLE = True
except Exception:
_ZHANG_AVAILABLE = False
colorizers = None
_zhang_model = None
_zhang_transform = T.Compose([
T.Resize(256),
T.CenterCrop(256),
T.ToTensor(),
])
def load_zhang_model():
global _zhang_model
if not _ZHANG_AVAILABLE:
return None
if _zhang_model is None:
_zhang_model = colorizers.eccv16().eval().to(DEVICE)
return _zhang_model
def zhang_colorize(pil_img: Image.Image) -> Image.Image:
model = load_zhang_model()
if model is None:
return test_mode_colorize(pil_img)
img = pil_img.convert("RGB")
img_resized = _zhang_transform(img).unsqueeze(0).to(DEVICE)
np_img = img_resized[0].permute(1, 2, 0).cpu().numpy()
lab = skcolor.rgb2lab(np_img)
L = lab[:, :, 0]
tens_l = torch.from_numpy(L).unsqueeze(0).unsqueeze(0).float().to(DEVICE)
with torch.no_grad():
out_ab = model(tens_l).cpu()
out_ab = out_ab[0].permute(1, 2, 0).numpy()
H_orig, W_orig = img.size[1], img.size[0]
out_ab_resized = np.array(
Image.fromarray((out_ab * 255).astype(np.uint8)).resize((W_orig, H_orig), Image.BILINEAR),
dtype=np.float32
) / 255.0
img_np = np.array(img).astype(np.float32) / 255.0
lab_orig = skcolor.rgb2lab(img_np)
L_orig = lab_orig[:, :, 0]
lab_out = np.zeros((H_orig, W_orig, 3), dtype=np.float32)
lab_out[:, :, 0] = L_orig
lab_out[:, :, 1:] = out_ab_resized * 128.0
rgb_out = skcolor.lab2rgb(lab_out)
rgb_out = np.clip(rgb_out, 0.0, 1.0)
rgb_out = (rgb_out * 255).astype(np.uint8)
return Image.fromarray(rgb_out)
# -----------------------------
# DEOLDIFY‑LITE (CPU SAFE)
# -----------------------------
def deoldify_lite_colorize(pil_img: Image.Image) -> Image.Image:
img = pil_img.convert("RGB")
arr = np.array(img).astype(np.float32) / 255.0
lab = skcolor.rgb2lab(arr)
L = lab[:, :, 0]
a = lab[:, :, 1]
b = lab[:, :, 2]
a *= 1.35
b *= 1.35
a += 2.0
b += 1.0
lab_out = np.stack([L, a, b], axis=-1)
rgb_out = skcolor.lab2rgb(lab_out)
rgb_out = np.clip(rgb_out, 0.0, 1.0)
rgb_out = (rgb_out * 255).astype(np.uint8)
return Image.fromarray(rgb_out)
# -----------------------------
# MAIN PIPELINE
# -----------------------------
def colorize_image(input_image, mode):
if input_image is None:
return None
pil_img = input_image.convert("RGB")
if mode == "Test Mode (Very Subtle)":
return test_mode_colorize(pil_img)
if mode == "Zhang ECCV16 (Deep Colorizer)":
return zhang_colorize(pil_img)
if mode == "DeOldify‑Lite (Art Mode)":
return deoldify_lite_colorize(pil_img)
return test_mode_colorize(pil_img)
# -----------------------------
# GRADIO UI
# -----------------------------
with gr.Blocks(title="Biker Image Colorizer – CPU (Test + Zhang + DeOldify‑Lite)") as demo:
gr.Markdown(
"""
# Biker Image Colorizer – CPU Edition
**Three modes:**
- Test Mode (very subtle)
- Zhang ECCV16 (deep neural colorizer)
- DeOldify‑Lite (artistic strong color)
"""
)
with gr.Row():
with gr.Column():
input_image = gr.Image(type="pil", label="Input Image")
mode = gr.Radio(
choices=[
"Test Mode (Very Subtle)",
"Zhang ECCV16 (Deep Colorizer)",
"DeOldify‑Lite (Art Mode)",
],
value="Zhang ECCV16 (Deep Colorizer)",
label="Colorization Mode",
)
run_btn = gr.Button("Colorize", variant="primary")
with gr.Column():
output_image = gr.Image(type="pil", label="Output Image")
run_btn.click(
fn=colorize_image,
inputs=[input_image, mode],
outputs=[output_image],
)
if __name__ == "__main__":
demo.launch()
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