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## 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matrix where each label has been replaced by a color looked up in colormap.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) | |