import streamlit as st
import os
import numpy as np
import cv2
import matplotlib.pyplot as plt
import torch
import albumentations as albu
from torch.utils.data import DataLoader
from torch.utils.data import Dataset as BaseDataset
from catalyst.dl import SupervisedRunner
import segmentation_models_pytorch as smp
from io import StringIO
# streamlit run c:/Users/ronni/Downloads/polyp_seg_web_app/app.py
x_test_dir = 'test/test/images'
y_test_dir = 'test/test/masks'
ENCODER = 'mobilenet_v2'
ENCODER_WEIGHTS = 'imagenet'
CLASSES = ['polyp', 'background']
ACTIVATION = 'sigmoid'
preprocessing_fn = smp.encoders.get_preprocessing_fn(ENCODER, ENCODER_WEIGHTS)
def visualize(**images):
"""Plot images in one row."""
n = len(images)
plt.figure(figsize=(16, 5))
for i, (name, image) in enumerate(images.items()):
plt.subplot(1, n, i + 1)
plt.xticks([])
plt.yticks([])
plt.title(' '.join(name.split('_')).title())
plt.imshow(image)
plt.savefig('x',dpi=400)
st.image('x.png')
def get_training_augmentation():
train_transform = [
albu.HorizontalFlip(p=0.5),
albu.ShiftScaleRotate(scale_limit=0.5, rotate_limit=0, shift_limit=0.1, p=1, border_mode=0),
albu.Resize(576, 736, always_apply=True, p=1),
albu.IAAAdditiveGaussianNoise(p=0.2),
albu.IAAPerspective(p=0.5),
albu.OneOf(
[
albu.CLAHE(p=1),
albu.RandomBrightness(p=1),
albu.RandomGamma(p=1),
],
p=0.9,
),
albu.OneOf(
[
albu.IAASharpen(p=1),
albu.Blur(blur_limit=3, p=1),
albu.MotionBlur(blur_limit=3, p=1),
],
p=0.9,
),
albu.OneOf(
[
albu.RandomContrast(p=1),
albu.HueSaturationValue(p=1),
],
p=0.9,
),
]
return albu.Compose(train_transform)
def get_validation_augmentation():
"""Add paddings to make image shape divisible by 32"""
test_transform = [
albu.Resize(576, 736)
]
return albu.Compose(test_transform)
def to_tensor(x, **kwargs):
return x.transpose(2, 0, 1).astype('float32')
def get_preprocessing(preprocessing_fn):
"""Construct preprocessing transform
Args:
preprocessing_fn (callbale): data normalization function
(can be specific for each pretrained neural network)
Return:
transform: albumentations.Compose
"""
_transform = [
albu.Lambda(image=preprocessing_fn),
albu.Lambda(image=to_tensor, mask=to_tensor),
]
return albu.Compose(_transform)
class Dataset(BaseDataset):
"""Args:
images_dir (str): path to images folder
masks_dir (str): path to segmentation masks folder
class_values (list): values of classes to extract from segmentation mask
augmentation (albumentations.Compose): data transfromation pipeline
(e.g. flip, scale, etc.)
preprocessing (albumentations.Compose): data preprocessing
(e.g. noralization, shape manipulation, etc.)
"""
CLASSES = ['polyp', 'background']
def __init__(
self,
images_dir,
masks_dir,
classes=None,
augmentation=None,
preprocessing=None,
single_file=False
):
if single_file:
self.ids = images_dir
self.images_fps = os.path.join('test/test/images', self.ids)
self.masks_fps = os.path.join('test/test/masks', self.ids)
else:
self.ids = os.listdir(images_dir)
self.images_fps = [os.path.join(images_dir, image_id) for image_id in self.ids]
self.masks_fps = [os.path.join(masks_dir, image_id) for image_id in self.ids]
# convert str names to class values on masks
self.class_values = [self.CLASSES.index(cls.lower()) for cls in classes]
self.augmentation = augmentation
self.preprocessing = preprocessing
def __getitem__(self, i):
# read data
image = cv2.imread(self.images_fps)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
mask = cv2.imread(self.masks_fps, 0)
mask[np.where(mask < 8)] = 0
mask[np.where(mask > 8)] = 255
# extract certain classes from mask (e.g. polyp)
masks = [(mask == v) for v in self.class_values]
mask = np.stack(masks, axis=-1).astype('float')
# apply augmentations
if self.augmentation:
sample = self.augmentation(image=image, mask=mask)
image, mask = sample['image'], sample['mask']
# apply preprocessing
if self.preprocessing:
sample = self.preprocessing(image=image, mask=mask)
image, mask = sample['image'], sample['mask']
return image, mask
def __len__(self):
return len(self.ids)
def model_infer(img_name):
model = smp.UnetPlusPlus(
encoder_name=ENCODER,
encoder_weights=ENCODER_WEIGHTS,
encoder_depth=5,
decoder_channels=(256, 128, 64, 32, 16),
classes=len(CLASSES),
activation=ACTIVATION,
decoder_attention_type=None,
)
model.load_state_dict(torch.load('best.pth', map_location=torch.device('cpu'))['model_state_dict'])
model.eval()
test_dataset = Dataset(
img_name,
img_name,
augmentation=get_validation_augmentation(),
preprocessing=get_preprocessing(preprocessing_fn),
classes=CLASSES,
single_file=True
)
test_dataloader = DataLoader(test_dataset)
loaders = {"infer": test_dataloader}
runner = SupervisedRunner()
logits = []
f = 0
for prediction in runner.predict_loader(model=model, loader=loaders['infer'],cpu=True):
if f < 3:
logits.append(prediction['logits'])
f = f + 1
else:
break
threshold = 0.5
break_at = 1
for i, (input, output) in enumerate(zip(
test_dataset, logits)):
image, mask = input
image_vis = image.transpose(1, 2, 0)
gt_mask = mask[0].astype('uint8')
pr_mask = (output[0].numpy() > threshold).astype('uint8')[0]
i = i + 1
if i >= break_at:
break
return image_vis, gt_mask, pr_mask
PAGE_TITLE = "Polyp Segmentation"
SUBHEADER = "Polyps are growths in the colon which can be precursors to colon cancer and are of particular interest \
when performing colonoscopies. Improving automatic detection of polyps helps doctors analyze thousands of frames from colonoscopy videos \
and leads to more reliable and efficient prevention of colon cancer. This web app uses a CNN trained on colonoscopy images from the Kvasir dataset."
def file_selector(folder_path='.'):
filenames = os.listdir(folder_path)
selected_filename = st.selectbox('Select a file', filenames)
return os.path.join(folder_path, selected_filename)
def file_selector_ui():
folder_path = './test/test/images'
filename = file_selector(folder_path=folder_path)
printname = list(filename)
printname[filename.rfind('\\')] = '/'
st.write('You selected`%s`' % ''.join(printname))
return filename
def file_upload(folder_path='.'):
filenames = os.listdir(folder_path)
folder_path = './test/test/images'
uploaded_file = st.file_uploader("Choose a file")
filename = os.path.join(folder_path, uploaded_file.name)
printname = list(filename)
printname[filename.rfind('\\')] = '/'
st.write('You selected`%s`' % ''.join(printname))
return filename
def main():
st.set_page_config(page_title=PAGE_TITLE, layout="wide")
st.title(PAGE_TITLE)
st.markdown(SUBHEADER)
image_path = file_selector_ui()
# image_path = file_upload()
image_path = os.path.abspath(image_path)
to_infer = image_path[image_path.rfind("\\") + 1:]
if os.path.isfile(image_path) is True:
_, file_extension = os.path.splitext(image_path)
if file_extension == ".jpg":
image_vis, gt_mask, pr_mask = model_infer(to_infer)
visualize(
image=image_vis,
#ground_truth_mask=gt_mask,
predicted_mask=pr_mask
)
if __name__ == "__main__":
main()