data/utils/eval_boxplot.py

"""
This code is to create excel file for boxplot. Later boxplot.py will be 
used to generate the boxplot.
"""

import numpy as np
import cv2
from sklearn.metrics import confusion_matrix
import os
import pandas as pd
import json

import warnings
warnings.filterwarnings("ignore")

# Chronic wound dataset directory
dir_label = r'.\azh_wound_care_center_dataset_patches\test\labels' # label directory
dir_pred = r'.\predictions\OldDFU' # prediction directory

# Read json
with open("categorized_oldDfu.json", "r") as F:
    categories = json.load(F)

names = os.listdir(dir_label)

HARD_LINE = True

ep = 1e-6

with pd.ExcelWriter(os.path.join(dir_pred, 'result_boxplot' + '.xlsx')) as writer:
    
    for key, names in categories.items():
        
        # Create dataframe to store records
        df = pd.DataFrame(index=[], columns = [
            'Name', 'Accuracy', 'Specificity', 'iou', 'Precision', 'Recall', 'Dice'], dtype='object')
        
        for i, name in enumerate(names):
        
            gt_mask = cv2.imread(os.path.join(dir_label, name), 0)
        
            pr_mask = cv2.imread(os.path.join(dir_pred, name), 0)
        
            # Move to CPU and convert to numpy
            gt_mask = np.squeeze(gt_mask)
            pred = np.squeeze(pr_mask)
        
            # Calculate accuracy, specificity, iou, precision, recall, and dice
            flat_mask = np.squeeze(gt_mask).flatten()
            flat_pred = np.squeeze(pred).flatten()
            
            # Calculate tp, fp, tn, fn
            unq_mask_val = np.unique(flat_mask) # unique values in the mask. For binary image, it should be 0 and 1
            
            'Case I: If there is no GT pixels in the image'
            if len(unq_mask_val)==1 and unq_mask_val==0: # Only one unique mask value and it is zero
                
                'Case I.a: If both GT and prediction are black' 
                if np.array_equal(flat_mask, flat_pred):
                    # Calculate metrics for image-based evaluation. This time consider background as y_true. 
                    acc, sp, p, r, dice, iou = 100, 100, 100, 100, 100, 100
                    print("Img # {:1s}, Image {:1s}: acc: {:3f}, sp: {:3f}, iou: {:3f}, p: {:3f}, r: {:3f}, dice: {:3f}".format(str(i+1), name, acc, sp, iou, p, r, dice))
                                
                    # Calculate the confusion matrix for data-based evaluation
                    # Only tn will be counted. All others will be zero. Because the GT and the prediction
                    # both have 0 pixels only. So, everything is truly negative.
                    tn, fp, fn, tp = len(flat_mask), 0, 0, 0
           
                else:
                    'Case I.b: If GT is black, but prediction not'
                    if HARD_LINE:
                        # If HARD_LINE is True, then all metrics will be set to 0s.
                        
                        # Calculate metrics for image-based evaluation
                        acc, sp, p, r, dice, iou = 0, 0, 0, 0, 0, 0
                        
                        # Calculate confusion matrix for data-based evaluation
                        tp, fn = 0, 0
                        fp = np.count_nonzero(flat_pred) # no. of non-zero pixels
                        tn = len(flat_pred) - fp # no. of zero intensity pixels
                        
                    else:
                        # If HARD_LINE is False, then metrics will be calculated considering
                        # background pixels as y_true. 
                        
                        # Calculate metrics for image-based evaluation. 
                        # This time consider background as y_true. 
                        # Invert (logical NOT) GT and prediction, meaning background will be considered as foreground now.
                        invt_flat_mask = np.logical_not(flat_mask) * 1
                        invt_flat_pred = np.logical_not(flat_pred) * 1
                        
                        itn, ifp, ifn, itp = confusion_matrix(invt_flat_mask, invt_flat_pred).ravel()
                        
                        # Calculate metrics for image-based evaluation 
                        acc = ((itp + itn)/(itp + itn + ifn + ifp))*100  
                        sp = (itn/(itn + ifp + ep))*100
                        p = (itp/(itp + ifp + ep))*100
                        r = (itp/(itp + ifn + ep))*100
                        dice = 0#(2 * itp / (2 * itp + ifp + ifn))*100
                        iou = (itp/(itp + ifp + ifn + ep)) * 100
            
                        print("Img # {:1s}, Image {:1s}: acc: {:3f}, sp: {:3f}, iou: {:3f}, p: {:3f}, r: {:3f}, dice: {:3f}".format(str(i+1), name, acc, sp, iou, p, r, dice))
                        
                        # Calculate the confusion matrix for data-based evaluation
                        # Do not do inversion (logical NOT). There will be some fp and tn. tp and fn will be 0.
                        tn, fp, fn, tp = confusion_matrix(flat_mask, flat_pred).ravel()
            
            else:
                'Case II: If there is some GT pixels in the image'
                tn, fp, fn, tp = confusion_matrix(flat_mask, flat_pred).ravel()
                
                # Calculate metrics
                acc = ((tp + tn)/(tp + tn + fn + fp))*100  
                sp = (tn/(tn + fp + ep))*100
                p = (tp/(tp + fp + ep))*100
                r = (tp/(tp + fn + ep))*100
                dice = (2 * tp / (2 * tp + fp + fn))*100
                iou = (tp/(tp + fp + fn + ep)) * 100
                print("Img # {:1s}, Image {:1s}: acc: {:3f}, sp: {:3f}, iou: {:3f}, p: {:3f}, r: {:3f}, dice: {:3f}".format(str(i+1), name, acc, sp, iou, p, r, dice))
           
            
            tmp = pd.Series([name, acc, sp, iou, p, r, dice], index=['Name', 'Accuracy', 'Specificity', 'iou', 'Precision', 'Recall', 'Dice'])
            df = df.append(tmp, ignore_index = True)
        
        df.to_excel(writer, sheet_name='cat-'+key, index=False)