v1

.gitattributes

@@ -32,3 +32,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+examples/*.* filter=lfs diff=lfs merge=lfs -text
+saved_model/*.* filter=lfs diff=lfs merge=lfs -text
+saved_model/*.* filter=lfs diff=lfs merge=lfs -text
+examples/*.* filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,152 @@
+import gradio as gr
+import numpy as np
+import os
+os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
+
+from glob import glob
+import tensorflow as tf
+import matplotlib.pyplot as plt
+from skimage.transform import resize
+from skimage.io import imsave
+from skimage.filters import threshold_otsu
+
+from doodleverse_utils.prediction_imports import *
+from doodleverse_utils.imports import *
+
+
+#load model
+filepath = './saved_model'
+model = tf.keras.models.load_model(filepath, compile = True)
+model.compile
+
+#segmentation
+def segment(input_img, use_tta, use_otsu, dims=(512, 512)):
+
+    N = 2
+
+    if use_otsu:
+        print("Use Otsu threshold")
+    else:
+        print("No Otsu threshold")
+
+    if use_tta:
+        print("Use TTA")
+    else:
+        print("Do not use TTA")
+
+
+    worig, horig, channels = input_img.shape
+
+    w, h = dims[0], dims[1]
+
+    print("Original dimensions {}x{}".format(worig,horig))
+    print("New dimensions {}x{}".format(w,h))
+
+    img = standardize(input_img)
+    
+    img = resize(img, dims, preserve_range=True, clip=True) 
+    
+    img = np.expand_dims(img,axis=0)
+    
+    est_label = model.predict(img)
+
+    if use_tta:
+        #Test Time Augmentation
+        est_label2 = np.flipud(model.predict((np.flipud(img)), batch_size=1))
+        est_label3 = np.fliplr(model.predict((np.fliplr(img)), batch_size=1))
+        est_label4 = np.flipud(np.fliplr(model.predict((np.flipud(np.fliplr(img))))))
+
+        #soft voting - sum the softmax scores to return the new TTA estimated softmax scores
+        est_label = est_label + est_label2 + est_label3 + est_label4
+        est_label /= 4
+    
+    pred = np.squeeze(est_label, axis=0)
+    pred = resize(pred, (worig, horig), preserve_range=True, clip=True)
+    
+    mask = np.argmax(pred,-1)
+
+    imsave("greyscale_download_me.png", mask.astype('uint8'))
+    
+    class_label_colormap = [
+        "#3366CC",
+        "#DC3912",
+        "#FF9900",
+        "#109618",
+        "#990099",
+        "#0099C6",
+        "#DD4477",
+        "#66AA00",
+        "#B82E2E",
+        "#316395",
+    ]
+    
+    # add classes
+    class_label_colormap = class_label_colormap[:N]
+
+    color_label = label_to_colors(
+        mask,
+        input_img[:, :, 0] == 0,
+        alpha=128,
+        colormap=class_label_colormap,
+        color_class_offset=0,
+        do_alpha=False,
+    )
+    
+    imsave("color_download_me.png", color_label)
+
+
+    if use_otsu:
+        thres = threshold_otsu(mask)
+        print("Otsu threshold is {}".format(thres))
+        water_nowater = (mask>thres).astype('uint8')
+    else:
+        water_nowater = (mask>=1).astype('uint8')
+
+
+    #overlay plot
+    plt.clf()
+    plt.subplot(121)
+    plt.imshow(input_img[:,:,-1],cmap='gray')
+    plt.imshow(color_label, alpha=0.4)
+    plt.axis("off")
+    plt.margins(x=0, y=0)
+
+    plt.subplot(122)
+    plt.imshow(input_img[:,:,-1],cmap='gray')
+    plt.contour(water_nowater, levels=[0], colors='r')
+    plt.axis("off")
+    plt.margins(x=0, y=0)
+
+    plt.savefig("overlay_download_me.png", dpi=300, bbox_inches="tight")    
+
+    return color_label, plt , "greyscale_download_me.png", "color_download_me.png", "overlay_download_me.png"
+
+
+
+with open("article.html", "r", encoding='utf-8') as f:
+    article= f.read()
+
+title = "Segment Satellite imagery" 
+description = "This simple model demonstration segments 15-m Landsat-7/8 or 10-m Sentinel-2 RGB (visible spectrum) imagery into the following classes: 1. water and 2. other. Please note that, ordinarily, ensemble models are used in predictive mode. Here, we are using just one model, i.e. without ensembling. Allows upload of 3-band imagery in jpg format and download of label imagery only one at a time. " 
+
+
+examples= [[l] for l in glob('examples/*.jpg')]
+
+inp = gr.Image()
+out1 = gr.Image(type='numpy')
+out2 = gr.Plot(type='matplotlib')
+out3 = gr.File()
+out4 = gr.File()
+out5 = gr.File()
+
+inp2 = gr.inputs.Checkbox(default=False, label="Use TTA")
+inp3 = gr.inputs.Checkbox(default=False, label="Use Otsu")
+
+Segapp = gr.Interface(segment, [inp, inp2, inp3], 
+                    [out1, out2, out3, out4, out5], 
+                    title = title, description = description, examples=examples,  article=article,
+                    theme="grass")
+                    
+Segapp.launch(enable_queue=True)
+
+

article.html

@@ -0,0 +1,3 @@
+<p style='text-align: center'>
+    <a href='https://github.com/Doodleverse' target='_blank'>Part of the Doodleverse</a> | <a href='https://github.com/MARDAScience' target='_blank'>MARDAScience Github Repo</a> | <a href='https://zenodo.org/record/7384255' target='_blank'>Zenodo model repository</a> 
+</p>

requirements.txt

@@ -0,0 +1,5 @@
+tensorflow
+numpy
+matplotlib
+scikit-image
+doodleverse_utils

saved_model/keras_metadata.pb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:55bc51ea5be7fae6332e9aa0c4a28cacf8d70b71046d4dfcae2c62a350473dcc
+size 223371

saved_model/saved_model.pb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7bfd3ea0da120d8d8e8f92338955e44c584bc187c108640ff13556c68a1d38f2
+size 1859619

saved_model/variables/variables.data-00000-of-00001

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:44428075abf569220b6484cd0de2a05774788f27a11467447f19941fe4b13cff
+size 23047137

saved_model/variables/variables.index

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2393079c3b5b548d55173010ff1867fb0a9eacd9169cfdb39d5d00f5712be92a
+size 10734