utils.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Mar 12 10:44:33 2023

@author: lorenzoquerol
"""

import matplotlib.pyplot as plt
import numpy as np
import os
from alive_progress import alive_bar
import pandas as pd
import glob
import cv2
import smartcrop
from matplotlib import gridspec

from PIL import Image
import seaborn as sns
sns.set_theme(style="ticks")


plt.rcParams['figure.dpi'] = 600


def load_images(path):
    images = []
    filenames = os.listdir(path)

    with alive_bar(len(filenames), title="Loading Images", bar='smooth', spinner=None) as bar:
        for filename in filenames:
            image = cv2.imread(os.path.join(path, filename))
            image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
            images.append(image)
            bar()

    return np.array(images, dtype=object)


def show_raw_images(images, classname, start_index=0):
    fig, axes = plt.subplots(ncols=4, nrows=1, figsize=(15, 3))
    plt.suptitle(classname)
    axes = axes.ravel()
    index = start_index
    for i in range(4):
        axes[i].imshow(images[index])
        # axes[i].set_title(images[index].shape)
        axes[i].get_xaxis().set_visible(False)
        axes[i].get_yaxis().set_visible(False)
        index += 1
    plt.show()


def show_images_with_labels(images, labels, classnames, start_index=0):
    fig, ax = plt.subplots(ncols=4, nrows=2)

    index = start_index
    for i in range(8):
        ax[i].imshow(images[index], cmap='gray')
        ax[i].set_title(classnames[labels[index]])
        ax[i].get_xaxis().set_visible(False)
        ax[i].get_yaxis().set_visible(False)
        index += 1
    plt.show()


def crop_images(images, size):
    sc = smartcrop.SmartCrop()
    cropped_images = []

    with alive_bar(len(images), bar='smooth', spinner=None) as bar:
        for image in images:
            image = np.array(image)
            image = Image.fromarray(image)
            localized = sc.crop(image, size, size)

            bbox = (
                localized['top_crop']['x'],
                localized['top_crop']['y'],
                localized['top_crop']['width'] + localized['top_crop']['x'],
                localized['top_crop']['height'] + localized['top_crop']['y']
            )

            cropped = image.crop(bbox)
            resized = cropped.resize((size, size))
            cropped_images.append(np.array(resized))
            bar()

    return np.array(cropped_images)


def preprocess_images(images):
    preprocessed_images = []

    with alive_bar(len(images), title="Preprocessing Images", bar='smooth', spinner=None) as bar:
        for image in images:
            image = cv2.cvtColor(np.array(image).astype(
                'uint8'), cv2.COLOR_RGB2GRAY)
            # image = image / 255.
            preprocessed_images.append(image)
            bar()

    return np.array(preprocessed_images)


def find_misclassifications(labels, preds):
    indices = []
    for i, (label, pred) in enumerate(zip(preds, labels)):
        if pred != label:
            indices.append(i)

    return np.array(indices)


def arrange_subplots(xs, ys, n_plots):
      """
      ---- Parameters ----
      xs (n_plots, d): list with n_plot different lists of x values that we wish to plot
      ys (n_plots, d): list with n_plot different lists of y values that we wish to plot
      n_plots (int): the number of desired subplots
      """
    
      # compute the number of rows and columns
      n_cols = int(np.sqrt(n_plots))
      n_rows = int(np.ceil(n_plots / n_cols))
    
      # setup the plot
      gs = gridspec.GridSpec(n_rows, n_cols)
      scale = max(n_cols, n_rows)
      fig = plt.figure(figsize=(5 * scale, 5 * scale))
    
      # loop through each subplot and plot values there
      for i in range(n_plots):
        ax = fig.add_subplot(gs[i])
        ax.plot(xs[i], ys[i])
    
def show_misclassifications(images, misclassified, labels, preds, start_index=0):
    # fig, axes = plt.subplots(ncols=5, nrows=2, figsize=(10, 6))

    classnames = ['healthy', 'wssv']
    index = start_index
    n_plots = 5
    # compute the number of rows and columns
    n_rows = int(np.sqrt(n_plots))
    n_cols = int(np.ceil(n_plots / n_rows))
  
    # setup the plot
    gs = gridspec.GridSpec(n_rows, n_cols)
    scale = max(n_cols, n_rows)
    fig = plt.figure(figsize=(3*scale, 3*scale))
  
    # loop through each subplot and plot values there
    for i in range(n_plots):
        try:
            ax = fig.add_subplot(gs[i])
            ax.imshow(images[misclassified[i]], cmap='gray')
            ax.set_title(f'actual: {classnames[labels[misclassified[index]]]} \n'
                         f'pred: {classnames[preds[misclassified[index]]]}')
            ax.set_axis_off()
        except IndexError:
            break
        
    # for i in range(2):
    #     for j in range(5):
    #         try:
    #             axes[i, j].imshow(images[misclassified[index]], cmap='gray')
    #             axes[i, j].set_title(f'actual: {classnames[labels[misclassified[index]]]} \n'
    #                                  f'pred: {classnames[preds[misclassified[index]]]}')
    #             axes[i, j].axis('off')
    #             index += 1
    #         except IndexError:
    #             break