v1

.gitattributes

@@ -32,3 +32,5 @@ 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

app.py

@@ -0,0 +1,186 @@
+## Daniel Buscombe, Marda Science LLC 2023
+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
+from skimage.filters import threshold_otsu
+from skimage.measure import EllipseModel, CircleModel, ransac
+
+##========================================================
+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+    matrix where each label has been replaced by a color looked up in colormap.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 = './saved_model'
+model = tf.keras.models.load_model(filepath, compile = True)
+model.compile
+
+#segmentation
+def segment(input_img, dims=(1024, 1024)):
+
+    w = input_img.shape[0]
+    h = input_img.shape[1]       
+    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) 
+
+    #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, (w, h), preserve_range=True, clip=True)
+    
+    bias=.1
+    thres_land = threshold_otsu(pred[:,:,1])-bias
+    print("Coin threshold: %f" % (thres_land))
+    mask = (pred[:,:,1]<=thres_land).astype('uint8')
+    
+    imsave("greyscale.png", mask*255)
+
+    class_label_colormap = [
+        "#3366CC",
+        "#DC3912",
+        "#FF9900",
+    ]
+    # add classes
+    class_label_colormap = class_label_colormap[:2]
+
+    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.png", color_label)
+   
+    #overlay plot
+    plt.clf()
+    plt.imshow(input_img,cmap='gray')
+    plt.imshow(color_label, alpha=0.4)
+    plt.axis("off")
+    plt.margins(x=0, y=0)
+
+    ############################################################
+    dst = 1-mask.squeeze()
+    points = np.array(np.nonzero(dst)).T
+    points = np.column_stack((points[:,1], points[:,0]))
+
+    # print("Fitting ellipse to coin to compute diameter ....")
+    # model_robust, inliers = ransac(points, EllipseModel, min_samples=100,residual_threshold=2, max_trials=3)
+    # r=np.max([model_robust.params[2] , model_robust.params[3]])
+    # x=model_robust.params[0]
+    # y=model_robust.params[1]
+    # a_over_b = model_robust.params[2] / model_robust.params[3] ##a/b
+
+    print("Fitting circle to coin to compute diameter ....")
+    model_robust, inliers = ransac(points, CircleModel, min_samples=100,residual_threshold=2, max_trials=100)
+    r=model_robust.params[2]
+    x=model_robust.params[0]
+    y=model_robust.params[1]
+
+    print('diameter of coin = %f pixels' % (r*2))
+    print('image scaling (assuming quarter dollar) = %f mm/pixel' % (24.26 / r*2))
+
+    plt.plot(x, y, 'ko')
+    plt.plot(np.arange(x-r, x+r, int(r*2)), np.arange(y-r, y+r, int(r*2)),'m')
+
+    plt.savefig("overlay.png", dpi=300, bbox_inches="tight")
+
+    return 'diameter of coin = %f pixels' % (r*2), 'image scaling (assuming quarter dollar) = %f mm/pixel' % (24.26 / r*2), color_label, plt , "greyscale.png", "color.png", "overlay.png"
+
+title = "Find and measure coins in images of sand!" 
+description = "This model demonstration segments beach sediment imagery into two classes: a) background, and b) coin, then measuring the coin. Allows upload of imagery and download of label imagery only one at a time. This model is part of the Doodleverse https://github.com/Doodleverse" 
+    
+
+examples = [['examples/20191011_091052.jpg'],
+['examples/20191010_135020.jpg'],
+['examples/IMG_20210922_170908944.jpg'],
+['examples/IMG_20211121_120533257_HDR.jpg'],
+['examples/20210208_172834.jpg'],
+['examples/20220101_165359.jpg']] 
+
+inp = gr.Image()
+out1 = gr.Image(type='numpy')
+out2 = gr.Plot(type='matplotlib')
+out3 = gr.File()
+out4 = gr.File()
+out5 = gr.File()
+
+Segapp = gr.Interface(segment, inp, ["text", "text", out1, out2, out3, out4, out5], title = title, description = description, examples=examples, theme="grass")
+#, allow_flagging='manual', flagging_options=["bad", "ok", "good", "perfect"], flagging_dir="flagged")
+
+Segapp.launch(enable_queue=True)

requirements.txt

@@ -0,0 +1,4 @@
+tensorflow
+numpy
+matplotlib
+scikit-image

saved_model/fingerprint.pb

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