Spaces:
Sleeping
Sleeping
File size: 3,138 Bytes
73b3af8 e9f8684 73b3af8 e9f8684 73b3af8 e9f8684 73b3af8 e9f8684 73b3af8 e9f8684 cb364c2 e9f8684 73b3af8 e9f8684 73b3af8 e9f8684 cb364c2 e9f8684 1b9d572 e9f8684 cb364c2 a3961aa e9f8684 a3961aa 73b3af8 a3961aa |
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 |
import streamlit as st
import tensorflow as tf
import numpy as np
from PIL import Image
import os
# === Fix font/matplotlib warnings for Hugging Face ===
os.environ["MPLCONFIGDIR"] = "/tmp/matplotlib"
os.environ["XDG_CACHE_HOME"] = "/tmp"
# === Custom loss and metrics ===
def weighted_dice_loss(y_true, y_pred):
smooth = 1e-6
y_true_f = tf.reshape(y_true, [-1])
y_pred_f = tf.reshape(y_pred, [-1])
intersection = tf.reduce_sum(y_true_f * y_pred_f)
return 1 - ((2. * intersection + smooth) /
(tf.reduce_sum(y_true_f) + tf.reduce_sum(y_pred_f) + smooth))
def iou_metric(y_true, y_pred):
y_true = tf.cast(y_true > 0.5, tf.float32)
y_pred = tf.cast(y_pred > 0.5, tf.float32)
intersection = tf.reduce_sum(y_true * y_pred)
union = tf.reduce_sum(y_true) + tf.reduce_sum(y_pred) - intersection
return intersection / (union + 1e-6)
def bce_loss(y_true, y_pred):
return tf.keras.losses.binary_crossentropy(y_true, y_pred)
# === Load model ===
model_path = "final_model_after_third_iteration_WDL0.07_0.5155/"
@st.cache_resource
def load_model():
return tf.keras.models.load_model(
model_path,
custom_objects={
"weighted_dice_loss": weighted_dice_loss,
"iou_metric": iou_metric,
"bce_loss": bce_loss
}
)
model = load_model()
# === Title ===
st.title("🕳️ Sinkhole Segmentation with EffV2-UNet")
# === File uploader ===
uploaded_image = st.file_uploader("Upload an image", type=["png", "jpg", "jpeg", "tif", "tiff"])
# === Example selector ===
example_dir = "examples"
example_files = sorted([
f for f in os.listdir(example_dir)
if f.lower().endswith((".jpg", ".jpeg", ".png", ".tif", ".tiff"))
])
if example_files:
st.subheader("🖼️ Try with an Example Image")
cols = st.columns(min(len(example_files), 4)) # up to 4 per row
for i, file in enumerate(example_files):
img_path = os.path.join(example_dir, file)
example_img = Image.open(img_path)
with cols[i % len(cols)]:
if st.button(file, key=file):
uploaded_image = img_path # simulate upload
image = example_img.convert("RGB")
st.image(image, caption=f"Example: {file}", use_column_width=True)
# === Prediction ===
if uploaded_image:
if isinstance(uploaded_image, str):
image = Image.open(uploaded_image).convert("RGB")
else:
image = Image.open(uploaded_image).convert("RGB")
st.image(image, caption="Input Image", use_column_width=True)
# Confidence threshold slider
threshold = st.slider("Confidence Threshold", 0.0, 1.0, 0.5, step=0.01)
# Preprocess and predict
resized = image.resize((512, 512))
x = np.expand_dims(np.array(resized), axis=0)
y = model.predict(x)[0, :, :, 0]
st.text(f"Prediction min/max: {y.min():.5f} / {y.max():.5f}")
# Apply thresholding if needed
mask_bin = (y > threshold).astype(np.uint8) * 255
mask_image = Image.fromarray(mask_bin)
st.image(mask_image, caption=f"Segmentation Mask (Threshold = {threshold:.2f})", use_column_width=True)
|