v1

.gitattributes

@@ -32,3 +32,6 @@ 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
+weights/*.* filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,299 @@
+## Daniel Buscombe, Marda Science LLC 2023
+# This file contains many functions originally from Doodleverse https://github.com/Doodleverse programs
+
+import gradio as gr
+import numpy as np
+import tensorflow as tf
+import matplotlib.pyplot as plt
+from skimage.transform import resize
+from skimage.io import imsave, imread
+
+from skimage.filters import threshold_otsu
+# from skimage.measure import EllipseModel, CircleModel, ransac
+from glob import glob 
+import json 
+from transformers import TFSegformerForSemanticSegmentation
+
+##========================================================
+def segformer(
+    id2label,
+    num_classes=2,
+):
+    """
+    https://keras.io/examples/vision/segformer/
+    https://huggingface.co/nvidia/mit-b0
+    """
+
+    label2id = {label: id for id, label in id2label.items()}
+    model_checkpoint = "nvidia/mit-b0"
+
+    model = TFSegformerForSemanticSegmentation.from_pretrained(
+        model_checkpoint,
+        num_labels=num_classes,
+        id2label=id2label,
+        label2id=label2id,
+        ignore_mismatched_sizes=True,
+    )
+    return model
+
+##========================================================
+def fromhex(n):
+    """hexadecimal to integer"""
+    return int(n, base=16)
+
+##========================================================
+def label_to_colors(
+    img,
+    mask,
+    alpha,  # =128,
+    colormap,  # =class_label_colormap, #px.colors.qualitative.G10,
+    color_class_offset,  # =0,
+    do_alpha,  # =True
+):
+    """
+    Take MxN matrix containing integers representing labels and return an MxNx4
+    matrix where each label has been replaced by a color looked up in colormap.
+    colormap entries must be strings like plotly.express style colormaps.
+    alpha is the value of the 4th channel
+    color_class_offset allows adding a value to the color class index to force
+    use of a particular range of colors in the colormap. This is useful for
+    example if 0 means 'no class' but we want the color of class 1 to be
+    colormap[0].
+    """
+
+    colormap = [
+        tuple([fromhex(h[s : s + 2]) for s in range(0, len(h), 2)])
+        for h in [c.replace("#", "") for c in colormap]
+    ]
+
+    cimg = np.zeros(img.shape[:2] + (3,), dtype="uint8")
+    minc = np.min(img)
+    maxc = np.max(img)
+
+    for c in range(minc, maxc + 1):
+        cimg[img == c] = colormap[(c + color_class_offset) % len(colormap)]
+
+    cimg[mask == 1] = (0, 0, 0)
+
+    if do_alpha is True:
+        return np.concatenate(
+            (cimg, alpha * np.ones(img.shape[:2] + (1,), dtype="uint8")), axis=2
+        )
+    else:
+        return cimg
+
+##====================================
+def standardize(img):
+    # standardization using adjusted standard deviation
+
+    N = np.shape(img)[0] * np.shape(img)[1]
+    s = np.maximum(np.std(img), 1.0 / np.sqrt(N))
+    m = np.mean(img)
+    img = (img - m) / s
+    del m, s, N
+    #
+    if np.ndim(img) == 2:
+        img = np.dstack((img, img, img))
+
+    return img
+
+############################################################
+############################################################
+
+#load model
+filepath = './weights/ct_NAIP_8class_768_segformer_v3_fullmodel.h5'
+
+configfile = filepath.replace('_fullmodel.h5','.json')
+with open(configfile) as f:
+    config = json.load(f)
+
+# This is how the program is able to use variables that have never been explicitly defined
+for k in config.keys():
+    exec(k+'=config["'+k+'"]')
+
+id2label = {}
+for k in range(NCLASSES):
+    id2label[k]=str(k)
+model = segformer(id2label,num_classes=NCLASSES)
+# model.compile(optimizer='adam')
+
+model.load_weights(filepath)
+
+
+############################################################
+############################################################
+
+# #-----------------------------------
+def est_label_multiclass(image,Mc,MODEL,TESTTIMEAUG,NCLASSES,TARGET_SIZE):
+
+    est_label = np.zeros((TARGET_SIZE[0], TARGET_SIZE[1], NCLASSES))
+    
+    for counter, model in enumerate(Mc):
+        # heatmap = make_gradcam_heatmap(tf.expand_dims(image, 0) , model)
+        try:
+            if MODEL=='segformer':
+                est_label = model(tf.expand_dims(image, 0)).logits
+            else:
+                est_label = tf.squeeze(model(tf.expand_dims(image, 0)))
+        except:
+            if MODEL=='segformer':
+                est_label = model(tf.expand_dims(image[:,:,0], 0)).logits
+            else:
+                est_label = tf.squeeze(model(tf.expand_dims(image[:,:,0], 0)))
+
+        if TESTTIMEAUG == True:
+            # return the flipped prediction
+            if MODEL=='segformer':
+                est_label2 = np.flipud(
+                    model(tf.expand_dims(np.flipud(image), 0)).logits
+                    )                
+            else:
+                est_label2 = np.flipud(
+                    tf.squeeze(model(tf.expand_dims(np.flipud(image), 0)))
+                    )
+            if MODEL=='segformer':
+
+                est_label3 = np.fliplr(
+                    model(
+                        tf.expand_dims(np.fliplr(image), 0)).logits
+                        )                
+            else:
+                est_label3 = np.fliplr(
+                    tf.squeeze(model(tf.expand_dims(np.fliplr(image), 0)))
+                )                
+            if MODEL=='segformer':
+                est_label4 = np.flipud(
+                    np.fliplr(
+                        tf.squeeze(model(tf.expand_dims(np.flipud(np.fliplr(image)), 0)).logits))
+                )                
+            else:
+                est_label4 = np.flipud(
+                    np.fliplr(
+                        tf.squeeze(model(
+                            tf.expand_dims(np.flipud(np.fliplr(image)), 0)))
+                            ))
+                
+            # soft voting - sum the softmax scores to return the new TTA estimated softmax scores
+            est_label = est_label + est_label2 + est_label3 + est_label4
+
+    return est_label, counter
+
+# #-----------------------------------
+def seg_file2tensor_3band(bigimage, TARGET_SIZE):  
+    """
+    "seg_file2tensor(f)"
+    This function reads a jpeg image from file into a cropped and resized tensor,
+    for use in prediction with a trained segmentation model
+    INPUTS:
+        * f [string] file name of jpeg
+    OPTIONAL INPUTS: None
+    OUTPUTS:
+        * image [tensor array]: unstandardized image
+    GLOBAL INPUTS: TARGET_SIZE
+    """
+
+    smallimage = resize(
+        bigimage, (TARGET_SIZE[0], TARGET_SIZE[1]), preserve_range=True, clip=True
+    )
+    smallimage = np.array(smallimage)
+    smallimage = tf.cast(smallimage, tf.uint8)
+
+    w = tf.shape(bigimage)[0]
+    h = tf.shape(bigimage)[1]
+
+    return smallimage, w, h, bigimage
+
+# #-----------------------------------
+def get_image(f,N_DATA_BANDS,TARGET_SIZE,MODEL):
+    image, w, h, bigimage = seg_file2tensor_3band(f, TARGET_SIZE)
+    image = standardize(image.numpy()).squeeze()
+
+    if MODEL=='segformer':
+        if np.ndim(image)==2:
+            image = np.dstack((image, image, image))
+        image = tf.transpose(image, (2, 0, 1))
+
+    return image, w, h, bigimage 
+
+# #-----------------------------------
+
+#segmentation
+def segment(input_img, use_tta, use_otsu, dims=(768, 768)):
+
+    if use_otsu:
+        print("Use Otsu threshold")
+    else:
+        print("No Otsu threshold")
+
+    if use_tta:
+        print("Use TTA")
+    else:
+        print("Do not use TTA")
+
+    image, w, h, bigimage = get_image(input_img,N_DATA_BANDS,TARGET_SIZE,MODEL)
+
+    est_label, counter = est_label_multiclass(image,[model],'segformer',TESTTIMEAUG,NCLASSES,TARGET_SIZE)
+    print(est_label.shape)
+
+    est_label /= counter + 1
+    # est_label cannot be float16 so convert to float32
+    est_label = est_label.numpy().astype('float32')
+
+    est_label = resize(est_label, (1, NCLASSES, TARGET_SIZE[0],TARGET_SIZE[1]), preserve_range=True, clip=True).squeeze()
+    est_label = np.transpose(est_label, (1,2,0))
+    est_label = resize(est_label, (w, h))
+    est_label = np.argmax(est_label,-1)
+    print(est_label.shape)
+
+    imsave("greyscale_download_me.png", est_label.astype('uint8'))
+    
+    class_label_colormap = [
+        "#3366CC",
+        "#DC3912",
+        "#FF9900",
+        "#109618",
+        "#990099",
+        "#0099C6",
+        "#DD4477",
+        "#66AA00",
+        "#B82E2E",
+        "#316395",
+    ]
+    
+    # add classes
+    class_label_colormap = class_label_colormap[:NCLASSES]
+
+    color_label = label_to_colors(
+        est_label,
+        input_img[:, :, 0] == 0,
+        alpha=128,
+        colormap=class_label_colormap,
+        color_class_offset=0,
+        do_alpha=False,
+    )
+    
+    imsave("color_download_me.png", color_label)
+
+    return color_label,"greyscale_download_me.png", "color_download_me.png"
+
+title = "Mapping sand in high-res. imagery" 
+description = "This simple model demonstration segments NAIP RGB (visible spectrum) imagery into the following classes:1. water (unbroken water); 2. whitewater (surf, active wave breaking); 3. sediment (natural deposits of sand. gravel, mud, etc), 4. other_bare_natural_terrain, 5. marsh_vegetation, 6. terrestrial_vegetation, 7. agricultural, 8. development. 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()
+
+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, out3, out4], #out2
+                    title = title, description = description, examples=examples, 
+                    theme="grass")
+                    
+Segapp.launch(enable_queue=True)

requirements.txt

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+tensorflow
+numpy
+matplotlib
+scikit-image

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