app.py

import gradio as gr
import tensorflow as tf
import cv2
import numpy as np
from itertools import islice
from PIL import Image, ImageDraw, ImageColor
from line_fit import LineFit
import random
import lwbna_unet as u

MAX_SELECTIONS = 8
input_shape = (MAX_SELECTIONS,256,256,2)
def load_model():
    unet = u.LWBNAUnet(1,128,8,4)
    unet.build(input_shape)
    unet.load_weights('royal-snowflake-2400.hdf5')
    return unet

model = load_model()
colors = list(ImageColor.colormap.keys())
linefit = LineFit(30, 0.3)

def get_blob_centroids(mask):
    centers = []
    # print(mask.dtype)
    # print(mask.shape)
    contours, hierarchies = cv2.findContours(
        mask, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
    for i in contours:
        M = cv2.moments(i)
        if M['m00'] != 0:
            cx = int(M['m10']/M['m00'])
            cy = int(M['m01']/M['m00'])
            centers.append([cx,cy])
            # print(cx,cy)
    return centers
    
def predict_mask(input_img, threshold):
    # unpack and reshape input
    im, mask = input_img["image"], input_img["mask"]
    mask = mask[:,:,0].astype(np.uint8)
    im = im.astype(np.float32)/256

    # get centroids to measure the fibers
    centers = get_blob_centroids(mask)

    # create a batch of input for the model
    batch = np.zeros([MAX_SELECTIONS,256,256,2], dtype=np.float32)

    for i, (cx, cy) in enumerate(islice(centers, MAX_SELECTIONS)):
        batch[i,:,:,0] = im
        batch[i,cy,cx,1] = 1.0
    
    pred = model.predict(batch, verbose=0).squeeze()
    # create a single image with the background and the foreground
    im = Image.fromarray(im*255).convert("RGBA")
    # m = Image.fromarray(pred[0]>threshold).convert("RGBA")
    # im = Image.blend(im, m, 0.5)
    imgd = ImageDraw.Draw(im)
    ds = []
    for p in islice(pred, len(centers)):
        d, lines = linefit.predict((p>threshold).astype(np.uint8)*255)
        ds.append(d)
        m = Image.fromarray(p>threshold)
        # imgd.bitmap([0,0], m, fill=random.choice(colors))
        for line in lines:
            imgd.line(line, fill ="blue", width = 1)
        
    return im, ds

demo = gr.Blocks()

with demo:
    with gr.Column():
        gr.Markdown("""
# Measure the diameter of fibers

## How to use this app

Select fibers that you want to measure. For each fiber click once to draw a blob. Only the center of each sketched blob is considered for the selection of the fiber. You will obtain the measurements for the selected fibers. 

## Input

An image and a selection point. The recommended pixel size for uploaded images is 256x256.

## Output
The measurements for the fiber that was selected.

## Examples

The first three examples were part of the rendered hold out set, the fourth is a real micrograph also never seen by the model during training.


""")
    with gr.Row():
        with gr.Column():
            img = gr.Image(
                tool="sketch", 
                source="upload",
                label="Mask",
                image_mode='L',
                shape=[256,256],
                value='test0000.png'
            )
            threshold = gr.Slider(
                label='Segmentation threshold', minimum=0, maximum=1, value=0.5)

            with gr.Row():
                btn = gr.Button("Run")
        with gr.Column():
            img2 = gr.Image()
            text = gr.Text(label='Measurements in px')

    btn.click(fn=predict_mask, inputs=[img, threshold], outputs=[img2,text], )
    examples = gr.Examples(examples=['test0001.png',
                                     'test0002.png',
                                     'test0003.png',
                                     'real_test0000.jpg'],
                           inputs=img)

demo.launch()