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    matrix where each label has been replaced by a color looked up in colormap.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