File size: 4,676 Bytes
9c15e66 8a589e9 9c15e66 b0f9d98 9c15e66 b0f9d98 3ae3a9f b0f9d98 9c15e66 70cbfc1 9c15e66 52b4297 9c15e66 70cbfc1 9c15e66 8a589e9 9c15e66 |
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 |
import matplotlib.pyplot as plt
import torch
from PIL import Image
from torchvision import transforms
import torch.nn.functional as F
from typing import Literal, Any
import gradio as gr
import spaces
from io import BytesIO
class Classifier:
LABELS = [
"Panoramic",
"Feature",
"Detail",
"Enclosed",
"Focal",
"Ephemeral",
"Canopied",
]
@spaces.GPU(duration=60)
def __init__(
self, model_path="Litton-7type-visual-landscape-model.pth", device=None
):
if device is None:
self.device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu")
else:
self.device = device
self.device = device
self.model = torch.load(
model_path, map_location=self.device, weights_only=False
)
if hasattr(self.model, "module"):
self.model = self.model.module
self.model.eval()
self.preprocess = transforms.Compose(
[
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
),
]
)
@spaces.GPU(duration=60)
def predict(self, image: Image.Image) -> tuple[Literal["Failed", "Success"], Any]:
image = image.convert("RGB")
input_tensor = self.preprocess(image).unsqueeze(0).to(self.device)
with torch.no_grad():
logits = self.model(input_tensor)
probs = F.softmax(logits[:, :7], dim=1).cpu()
return draw_bar_chart(
{
"class": self.LABELS,
"probs": probs[0] * 100,
}
)
def draw_bar_chart(data: dict[str, list[str | float]]):
classes = data["class"]
probabilities = data["probs"]
plt.figure(figsize=(8, 6))
plt.bar(classes, probabilities, color="skyblue")
plt.xlabel("Class")
plt.ylabel("Probability (%)")
plt.title("Class Probabilities")
for i, prob in enumerate(probabilities):
plt.text(i, prob + 0.01, f"{prob:.2f}", ha="center", va="bottom")
plt.tight_layout()
return plt
def get_layout():
css = """
.main-title {
font-size: 24px;
font-weight: bold;
text-align: center;
margin-bottom: 20px;
}
.reference {
text-align: center;
font-size: 1.2em;
color: #d1d5db;
margin-bottom: 20px;
}
.reference a {
color: #FB923C;
text-decoration: none;
}
.reference a:hover {
text-decoration: underline;
color: #FB923C;
}
.title {
border-bottom: 1px solid;
}
.footer {
text-align: center;
margin-top: 30px;
padding-top: 20px;
border-top: 1px solid #ddd;
color: #d1d5db;
font-size: 14px;
}
"""
theme = gr.themes.Base(
primary_hue="orange",
secondary_hue="orange",
neutral_hue="gray",
font=gr.themes.GoogleFont("Source Sans Pro"),
).set(
background_fill_primary="*neutral_950", # 主背景色（深黑）
button_primary_background_fill="*primary_500", # 按鈕顏色（橘色）
body_text_color="*neutral_200", # 文字顏色（淺色）
)
# with gr.Blocks(css=css, theme=theme) as demo:
with gr.Blocks() as demo:
with gr.Column():
gr.HTML(
value=(
'<div class="main-title">Litton7景觀分類模型</div>'
'<div class="reference">引用資料：'
'<a href="https://www.airitilibrary.com/Article/Detail/10125434-N202406210003-00003" target="_blank">'
"何立智、李沁築、邱浩修(2024)。Litton7：Litton視覺景觀分類深度學習模型。戶外遊憩研究，37(2)"
"</a>"
"</div>"
),
)
with gr.Row(equal_height=True):
image_input = gr.Image(label="上傳影像", type="pil")
chart = gr.Image(label="分類結果")
start_button = gr.Button("開始分類", variant="primary")
gr.HTML(
'<div class="footer">© 2024 LCL 版權所有<br>開發者：何立智、楊哲睿</div>',
)
start_button.click(
fn=Classifier().predict,
inputs=image_input,
outputs=chart,
)
return demo
if __name__ == "__main__":
get_layout().launch()
|