upload

app.py

@@ -0,0 +1,42 @@
+from skimage.util import montage as montage2d
+from utils import load_model, preprocess_image, attempt_download_from_hub
+import matplotlib.pyplot as plt
+
+import gradio as gr
+
+
+def keras_inference(img_data, model_path):
+    model_path = attempt_download_from_hub(model_path)
+    seg_model = load_model(model_path)
+    out_img = preprocess_image(img_data)
+    pred_y = seg_model.predict(out_img)
+
+    plt.imshow(montage2d(pred_y[:, :, :, 0]), cmap = 'bone_r')
+    plt.savefig('output.png')
+    return 'output.png'
+
+inputs = [
+    gr.Image(type='filepath', label='Image'),
+    gr.Dropdown(['keras_model.h5'], label='Model Path')
+]
+
+outputs = gr.Image(label='Segmentation')
+
+examples = [
+    ['data/testv1.jpg', 'kadirnar/Keras-Segmenting-Buildings-v1'],
+    ['data/testv2.jpg', 'kadirnar/Keras-Segmenting-Buildings-v1'],
+    ['data/testv3.jpg', 'kadirnar/Keras-Segmenting-Buildings-v1'],
+]
+
+title = 'Segmenting Buildings in Satellite Images with Keras'
+
+demo_app = gr.Interface(
+    keras_inference, 
+    inputs, 
+    outputs, 
+    title=title, 
+    examples=examples,
+    cache_examples=True,
+)
+
+demo_app.launch(debug=True, enable_queue=True)

requirements.txt

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+huggingface_hub==0.12.0
+keras==2.11.0
+matplotlib==3.6.2
+numpy==1.23.5
+opencv_python==4.6.0.66
+scikit_image==0.19.3

utils.py

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+#https://www.kaggle.com/code/kmader/segmenting-buildings-in-satellite-images
+
+from keras import models, layers
+from skimage.io import imread
+import numpy as np
+import cv2
+
+t_x_shape = (300, 300, 3)
+GAUSSIAN_NOISE = 0.1
+BASE_DEPTH = 16
+BLOCK_COUNT = 1
+SPATIAL_DROPOUT = 0.25
+EDGE_CROP = 16
+
+
+def conv_bn(x, filt, dl_rate=(1,1), preblock = False):
+    y = layers.Convolution2D(filt, (3, 3), 
+                             activation='linear', 
+                             padding='same', 
+                             dilation_rate=dl_rate,
+                            use_bias=False)(x)
+    if preblock: return y
+    y = layers.BatchNormalization()(y)
+    return layers.Activation('elu')(y)
+
+def keras_model():
+    in_layer = layers.Input(t_x_shape, name = 'RGB_Input')
+    pp_in_layer = layers.GaussianNoise(GAUSSIAN_NOISE)(in_layer)
+    pp_in_layer = layers.BatchNormalization()(pp_in_layer)
+
+    c = conv_bn(pp_in_layer, BASE_DEPTH//2)
+    c = conv_bn(c, BASE_DEPTH//2)
+    c = conv_bn(c, BASE_DEPTH)
+
+    skip_layers = [pp_in_layer]
+    for j in range(BLOCK_COUNT):
+        depth_steps = int(np.log2(t_x_shape[0])-2)
+        d = layers.concatenate(skip_layers+[conv_bn(c, BASE_DEPTH*2**j, (2**i, 2**i), preblock=True) 
+                                            for i in range(depth_steps)])
+        d = layers.SpatialDropout2D(SPATIAL_DROPOUT)(d)
+        d = layers.BatchNormalization()(d)
+        d = layers.Activation('elu')(d)
+        # bottleneck
+        d = conv_bn(d, BASE_DEPTH*2**(j+1))
+        skip_layers += [c]
+        c = d
+    d = layers.Convolution2D(1, (1, 1), activation='sigmoid', padding='same')(d)
+    d = layers.Cropping2D((EDGE_CROP, EDGE_CROP))(d)
+    d = layers.ZeroPadding2D((EDGE_CROP, EDGE_CROP))(d)
+    
+    seg_model = models.Model(inputs = [in_layer],outputs = [d])
+    
+    return seg_model
+
+def load_model(weight_path):
+    seg_model = keras_model()
+    seg_model.load_weights(weight_path)
+    return seg_model
+
+def preprocess_image(img_data):
+    img_data = imread(img_data)
+    
+    if img_data.shape[:2] == (300, 300):
+        img_data = img_data
+    else:
+        img_data = cv2.resize(img_data, (300, 300))
+
+    out_img = []
+    out_img += [img_data]
+
+    out_img = (np.stack(out_img, 0)/255.0).astype(np.float32)
+    return out_img
+
+
+def attempt_download_from_hub(repo_id, hf_token=None):
+    # https://github.com/fcakyon/yolov5-pip/blob/main/yolov5/utils/downloads.py
+    from huggingface_hub import hf_hub_download, list_repo_files
+    from huggingface_hub.utils._errors import RepositoryNotFoundError
+    from huggingface_hub.utils._validators import HFValidationError
+    try:
+        repo_files = list_repo_files(repo_id=repo_id, repo_type='model', token=hf_token)
+        model_file = [f for f in repo_files if f.endswith('.h5')][0]
+        file = hf_hub_download(
+            repo_id=repo_id,
+            filename=model_file,
+            repo_type='model',
+            token=hf_token,
+        )
+        return file
+    except (RepositoryNotFoundError, HFValidationError):
+        return None