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confusion.py
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confusion.py
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# make confusion matrix from ground_truth.txt and prediction.txt
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
import numpy as np
import pandas as pd
import math
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.metrics import f1_score
from sklearn.metrics import fbeta_score
from sklearn.metrics import cohen_kappa_score
from sklearn.metrics import matthews_corrcoef
from sklearn.metrics import jaccard_score
from add_bbox import *
from import_results import *
sns.set_palette('viridis')
def get_the_csv(ground_truth_file, prediction_file):
# reading the given csv file
# and creating dataframe
ground_truth = pd.read_csv(ground_truth_file,
delimiter = ' ')
# store dataframe into csv file
ground_truth.to_csv(ground_truth_file[:-4]+'.csv',
index = None)
prediction = pd.read_csv(prediction_file,
delimiter = ' ')
prediction.to_csv(prediction_file[:-4]+'.csv',
index = None)
def get_confusion_matrix(ground_truth_file, prediction_file):
filoo = open( ground_truth_file, 'r')
actual = []
for line in filoo:
lin = line.split(',')
# print(lin)
image = lin[0]
classes = (lin[1])
actual.append(classes)
filoo.close()
filoo = open( prediction_file, 'r')
predicted = []
for line in filoo:
lin = line.split(',')
# print(lin)
image = lin[0]
classes = (lin[1])
predicted.append(classes)
filoo.close()
y_actu = pd.Series(actual, name='Actual')
y_pred = pd.Series(predicted, name='Predicted')
df_confusion = pd.crosstab(y_actu, y_pred, dropna=False)
# df_confusion = pd.crosstab(y_actu, y_pred, rownames=['Actual'], colnames=['Predicted'], margins=True)
return df_confusion
def get_normalised_confusion_matrix(ground_truth_file, prediction_file):
filoo = open( ground_truth_file, 'r')
actual = []
for line in filoo:
lin = line.split(',')
# print(lin)
image = lin[0]
classes = (lin[1])
actual.append(classes)
filoo.close()
filoo = open( prediction_file, 'r')
predicted = []
for line in filoo:
lin = line.split(',')
# print(lin)
image = lin[0]
classes = (lin[1])
predicted.append(classes)
filoo.close()
y_actu = pd.Series(actual, name='Actual')
y_pred = pd.Series(predicted, name='Predicted')
df_confusion = pd.crosstab(y_actu, y_pred, dropna=False)
df_conf_norm = df_confusion.div(df_confusion.sum(axis=1), axis="index")
return df_conf_norm
# print("Confusion Matrix:")
# print(get_confusion_matrix('/home/as-hunt/ground_truth.csv', '/home/as-hunt/predictions.csv'))
# print("Normalised Confusion Matrix:")
# print(get_normalised_confusion_matrix('/home/as-hunt/ground_truth.csv', '/home/as-hunt/predictions.csv'))
def plot_confusion_matrix(ground_truth_file, prediction_file, title='Confusion matrix'):
df_confusion = get_confusion_matrix(ground_truth_file, prediction_file)
# print(df_confusion[1])
# plt.figure(figsize = (10,7))
plt.title(title)
sns.set(font_scale=1.4) # for label size
sns.heatmap(df_confusion, cmap='coolwarm', annot=True, annot_kws={"size": 16}) # font size
tick_marks = np.arange(len(df_confusion.columns))
plt.savefig(title + '.png')
def plot_normalised_confusion_matrix(ground_truth_file, prediction_file, title='Normalised Confusion matrix'):
df_confusion = get_normalised_confusion_matrix(ground_truth_file, prediction_file)
# print(df_confusion[1])
# plt.figure(figsize = (10,7))
plt.title(title)
sns.set(font_scale=1.4) # for label size
sns.heatmap(df_confusion, cmap='coolwarm', annot=True, annot_kws={"size": 16}) # font size
tick_marks = np.arange(len(df_confusion.columns))
plt.savefig(title + '.png')
# plot_normalised_confusion_matrix('/home/as-hunt/ground_truth.csv', '/home/as-hunt/predictions.csv', title='Normalised Confusion Matrix')
def do_math(gt_file, pd_file, title):
do_math_leukall(gt_file, pd_file, title)
def do_math_4(gt_file, pd_file, title):
gt = open(gt_file, 'r')
gt_array = []
gt_len = 0
gt_cm = []
pud = open(pd_file, 'r')
pd_len = 0
pd_array = []
pd_cm = []
TP_ECHY = 0
TP_ERY = 0
TP_PLT = 0
TP_WBC = 0
FP_ECHY = 0
FP_PLT = 0
FP_ERY = 0
FP_WBC = 0
FN_ECHY = 0
FN_ERY = 0
FN_PLT = 0
FN_WBC = 0
MissClass_ECHY = 0
MissClass_ECHY_as_ERY = 0
MissClass_ECHY_as_PLT = 0
MissClass_ECHY_as_WBC = 0
MissClass_ERY = 0
MissClass_ERY_as_ECHY = 0
MissClass_ERY_as_PLT = 0
MissClass_ERY_as_WBC = 0
MissClass_PLT = 0
MissClass_PLT_as_ECHY = 0
MissClass_PLT_as_ERY = 0
MissClass_PLT_as_WBC = 0
MissClass_WBC = 0
MissClass_WBC_as_ECHY = 0
MissClass_WBC_as_ERY = 0
MissClass_WBC_as_PLT = 0
for line in pud:
# print(line)
li = line.split(' ')
name = li[0]
classes = li[1]
bbox = [int(li[2]), int(li[3]), int(li[4]), int(li[5])]
confidence = li[6]
pd_array.append([name, bbox, classes, confidence])
pd_len += 1
for lune in gt:
lu = lune.split(' ')
nome = lu[0]
clisses = lu[1]
bbax = [int(lu[2]), int(lu[3]), int(lu[4]), int(lu[5])]
gt_array.append([nome, bbax, clisses])
gt_len += 1
for item in pd_array:
# print(item[0])
name = item[0]
bbox = item[1]
classes = item[2]
confidence = item[3]
for thing in gt_array:
nome = thing[0]
bbax = thing[1]
clisses = thing[2]
if name in thing[0]:
# print("Found")
place = gt_array.index(thing)
if iou(bbox, bbax) >= 0.5:
# print("overlap")
# print(iou(bbox,bbax))
gt_cm.append(clisses)
pd_cm.append(classes)
if classes == clisses:
# print("Classes match! Success!")
# print("Classes: ", classes)
if classes == '0':
TP_ECHY += 1
# print("TP_ECHY: ", TP_ECHY)
elif classes == '1':
TP_ERY += 1
# print("TP_ERY: ", TP_ERY)
elif classes == '2':
TP_PLT += 1
# print("TP_WBC: ", TP_WBC)
elif classes == '3':
TP_WBC += 1
# print("TP_WBC: ", TP_WBC)
gt_array.pop(place)
# print("item removed")
else:
# print("Classes do not match, detection error")
if classes == '0':
if clisses == '1':
MissClass_ERY_as_ECHY += 1
elif clisses == '2':
MissClass_PLT_as_ECHY += 1
elif clisses == '3':
MissClass_WBC_as_ECHY += 1
elif classes == '1':
if clisses == '0':
MissClass_ECHY_as_ERY += 1
elif clisses == '2':
MissClass_PLT_as_WBC += 1
elif clisses == '3':
MissClass_WBC_as_ERY += 1
elif classes == '2':
if clisses == '0':
MissClass_ECHY_as_PLT += 1
elif clisses == '1':
MissClass_ERY_as_PLT += 1
elif clisses == '3':
MissClass_WBC_as_PLT += 1
elif classes == '3':
if clisses == '0':
MissClass_ECHY_as_WBC += 1
elif clisses == '1':
MissClass_ERY_as_WBC += 1
elif clisses == '2':
MissClass_PLT_as_WBC += 1
gt_array.pop(place)
else:
# print("no overlap")
if classes== '0':
FP_ECHY += 1
elif classes == '1':
FP_ERY += 1
elif classes == '2':
FP_PLT += 1
elif classes == '3':
FP_WBC += 1
for item in gt_array:
# print(item)
name = item[0]
bbox = item[1]
classes = item[2]
if classes== '0':
FN_ECHY += 1
elif classes == '1':
FN_ERY += 1
elif classes == '2':
FN_PLT += 1
elif classes == '3':
FN_WBC += 1
FNECHY = FN_ECHY + MissClass_ECHY_as_ERY + MissClass_ECHY_as_PLT + MissClass_ECHY_as_WBC
FNERY = FN_ERY + MissClass_ERY_as_ECHY + MissClass_ERY_as_PLT + MissClass_ERY_as_WBC
FNPLT = FN_PLT + MissClass_PLT_as_ECHY + MissClass_PLT_as_ERY + MissClass_PLT_as_WBC
FNWBC = FN_WBC + MissClass_WBC_as_ECHY + MissClass_WBC_as_ERY + MissClass_WBC_as_PLT
FPERY = FP_ERY + MissClass_ECHY_as_ERY + MissClass_PLT_as_ERY + MissClass_WBC_as_ERY
FPECHY = FP_ECHY + MissClass_ERY_as_ECHY + MissClass_PLT_as_ECHY + MissClass_WBC_as_ECHY
FPPLT = FP_PLT + MissClass_ECHY_as_PLT + MissClass_ERY_as_PLT + MissClass_WBC_as_PLT
FPWBC = FP_WBC + MissClass_ECHY_as_WBC + MissClass_ERY_as_WBC + MissClass_PLT_as_WBC
print("True Positives ECHY: " + str(TP_ECHY))
print("True Positives ERY: " + str(TP_ERY))
print("True Positives PLT: " + str(TP_PLT))
print("True Positives WBC: " + str(TP_WBC))
TP = TP_ECHY + TP_ERY + TP_WBC + TP_PLT
print("True Positives: " + str(TP))
print("False Positives ECHY: " + str(FP_ECHY))
print("False Positives ERY: " + str(FP_ERY))
print("False Positives PLT: " + str(FP_PLT))
print("False Positives WBC: " + str(FP_WBC))
FP = FPECHY + FPERY + FPWBC + FPPLT
print("False Positives: " + str(FP))
print("False Negatives ECHY: " + str(FN_ECHY))
print("False Negatives ERY: " + str(FN_ERY))
print("False Negatives PLT: " + str(FN_PLT))
print("False Negatives WBC: " + str(FN_WBC))
FN = FNECHY + FNERY + FNWBC + FNPLT
print("False Negatives: " + str(FN))
print("MissClass ECHY: " + str(MissClass_ECHY))
print("MissClass ERY: " + str(MissClass_ERY))
print("MissClass PLT: " + str(MissClass_PLT))
print("MissClass WBC: " + str(MissClass_WBC))
predicted = pd_len
print("Predicted: " + str(predicted))
actual = gt_len
if TP_ECHY + FPECHY != 0:
precision_ECHY = TP_ECHY / (TP_ECHY + FPECHY)
print("Precision ECHY: " + str(precision_ECHY))
else:
print("Precision ECHY: 0")
if TP_ERY + FPERY != 0:
precision_ERY = TP_ERY / (TP_ERY + FPERY)
print("Precision ERY: " + str(precision_ERY))
else:
print("Precision ERY: 0")
if TP_PLT + FPPLT != 0:
precision_PLT = TP_PLT / (TP_PLT + FPPLT)
print("Precision PLT: " + str(precision_PLT))
else:
print("Precision PLT: 0")
precision_PLT = 0
if TP_WBC + FPWBC != 0:
precision_WBC = TP_WBC / (TP_WBC + FPWBC)
print("Precision WBC: " + str(precision_WBC))
else:
print("Precision WBC: 0")
if TP_ECHY + FNECHY != 0:
recall_ECHY = TP_ECHY / (TP_ECHY + FNECHY)
print("Recall ECHY: " + str(recall_ECHY))
else:
print("Recall ECHY: 0")
if TP_ERY + FNERY != 0:
recall_ERY = TP_ERY / (TP_ERY + FNERY)
print("Recall ERY: " + str(recall_ERY))
else:
print("Recall ERY: 0")
if TP_PLT + FNPLT != 0:
recall_PLT = TP_PLT / (TP_PLT + FNPLT)
print("Recall PLT: " + str(recall_PLT))
else:
print("Recall PLT: 0")
if TP_WBC + FNWBC != 0:
recall_WBC = TP_WBC / (TP_WBC + FNWBC)
print("Recall WBC: " + str(recall_WBC))
else:
print("Recall WBC: 0")
accuracy = (TP ) / (TP + FP + FN)
print("Actual: " + str(actual))
print("Accuracy: " + str(round(accuracy, 3)))
if TP + FP + FN != 0:
F1 = (TP)/(TP+ ((FP + FN)/2))
print("F1: " + str(round(F1, 3)))
else:
print("F1: 0")
if TP + FP != 0:
precision = TP / (TP + FP)
print("Precision: " + str(round(precision, 3)))
else:
print("Precision: 0")
if TP + FN != 0:
recall = TP / (TP + FN)
print("Recall: " + str(round(recall, 3)))
# if TP_ECHY + FP_ECHY + FN_ECHY != 0:
# F1_ECHY = 2 * (precision_ECHY * recall_ECHY) / (precision_ECHY + recall_ECHY)
# print("F1 ECHY: " + str(F1_ECHY))
# else:
# print("F1 ECHY: 0")
# if TP_ERY + FP_ERY + FN_ERY != 0:
# F1_ERY = 2 * (precision_ERY * recall_ERY) / (precision_ERY + recall_ERY)
# print("F1 ERY: " + str(F1_ERY))
# else:
# print("F1 ERY: 0")
# if precision_PLT + recall_PLT != 0:
# F1_PLT = 2 * (precision_PLT * recall_PLT) / (precision_PLT + recall_PLT)
# print("F1 PLT: " + str(F1_PLT))
# else:
# print("F1 PLT: 0")
# if TP_WBC + FP_WBC + FN_WBC != 0:
# F1_WBC = 2 * (precision_WBC * recall_WBC) / (precision_WBC + recall_WBC)
# print("F1 WBC: " + str(F1_WBC))
# else:
# print("F1 WBC: 0")
print("MissClass: " + str(MissClass_ECHY_as_ERY + MissClass_ECHY_as_PLT + MissClass_ECHY_as_WBC + MissClass_ERY_as_ECHY + MissClass_ERY_as_PLT + MissClass_ERY_as_WBC + MissClass_PLT_as_ECHY + MissClass_PLT_as_ERY + MissClass_PLT_as_WBC + MissClass_WBC_as_ECHY + MissClass_WBC_as_ERY + MissClass_WBC_as_PLT))
# name = pd_file.split('/')
path = '/home/as-hunt/Etra-Space/5-class-more/'
name = 'Normalised Confusion Matrix ' + title + ' Post bbox matching'
# print(gt_cm)
# print(pd_cm)
y_actu = pd.Series(gt_cm, name='Ground Truth')
y_pred = pd.Series(pd_cm, name='Predicted')
F1m = f1_score(y_actu, y_pred, average='macro')
print("F1 macro: " + str(F1m))
F1w = f1_score(y_actu, y_pred, average='weighted')
print("F1 weighted: " + str(F1w))
F1n = f1_score(y_actu, y_pred, average=None)
print("F1 none: " + str(F1n))
acc = accuracy_score(y_actu, y_pred)
print("Accuracy score sklearn: " + str(acc))
# target_names = ['Echinocyte', 'Erythrocyte', 'Leukocyte']
# target_names = ['Echinocyte', 'Erythrocyte', 'Platelet']
target_names = ['Echinocyte', 'Erythrocyte', 'Platelet', 'Leukocyte']
print(classification_report(y_actu, y_pred, target_names=target_names))
precision_score_weighted = precision_score(y_actu, y_pred, average='weighted')
print("Precision score weighted: " + str(precision_score_weighted))
precision_score_macro = precision_score(y_actu, y_pred, average='macro')
print("Precision score macro: " + str(precision_score_macro))
precision_score_none = precision_score(y_actu, y_pred, average=None)
print("Precision score none: " + str(precision_score_none))
recall_score_weighted = recall_score(y_actu, y_pred, average='weighted')
print("Recall score weighted: " + str(recall_score_weighted))
recall_score_macro = recall_score(y_actu, y_pred, average='macro')
print("Recall score macro: " + str(recall_score_macro))
recall_score_none = recall_score(y_actu, y_pred, average=None)
print("Recall score none: " + str(recall_score_none))
fbeta05_score_weighted = fbeta_score(y_actu, y_pred, average='weighted', beta=0.5)
print("F0.5 measure weighted: " + str(fbeta05_score_weighted))
fbeta05_score_macro = fbeta_score(y_actu, y_pred, average='macro', beta=0.5)
print("F0.5 measure macro: " + str(fbeta05_score_macro))
fbeta05_score_none = fbeta_score(y_actu, y_pred, average=None, beta=0.5)
print("F0.5 measure none: " + str(fbeta05_score_none))
fbeta2_score_weighted = fbeta_score(y_actu, y_pred, average='weighted', beta=2)
print("F2 measure weighted: " + str(fbeta2_score_weighted))
fbeta2_score_macro = fbeta_score(y_actu, y_pred, average='macro', beta=2)
print("F2 measure macro: " + str(fbeta2_score_macro))
fbeta2_score_none = fbeta_score(y_actu, y_pred, average=None, beta=2)
print("F2 measure none: " + str(fbeta2_score_none))
df_confusion = pd.crosstab(y_actu, y_pred, dropna=False)
df_conf_norm = df_confusion.div(df_confusion.sum(axis=1), axis="index")
# plt.figure(figsize = (10,7))
plt.title(title)
sns.set(font_scale=1.4) # for label size
sns.heatmap(df_conf_norm, cmap='coolwarm', annot=True, annot_kws={"size": 16}, xticklabels=target_names, yticklabels=target_names) # font size
tick_marks = np.arange(len(df_conf_norm.columns))
plt.savefig(path + name + '.png', bbox_inches='tight')
plt.clf()
name = 'Confusion Matrix ' + title + ' Post bbox matching'
# plt.figure(figsize = (10,7))
plt.title(title)
sns.set(font_scale=1.4) # for label size
sns.heatmap(df_confusion, cmap='coolwarm', annot=True, annot_kws={"size": 16}, xticklabels=target_names, yticklabels=target_names) # font size
tick_marks = np.arange(len(df_confusion.columns))
plt.savefig(path + name + '.png', bbox_inches='tight')
count_classes_file(gt_file, True, title + '_split.png', target_names)
def do_math_two(gt_file, pd_file, title):
gt = open(gt_file, 'r')
gt_array = []
gt_len = 0
gt_cm = []
pud = open(pd_file, 'r')
pd_len = 0
pd_array = []
pd_cm = []
TP_ECHY = 0
TP_ERY = 0
FP_ECHY = 0
FP_ERY = 0
FN_ECHY = 0
FN_ERY = 0
MissClass_ECHY = 0
MissClass_ERY = 0
for line in pud:
# print(line)
li = line.split(' ')
name = li[0]
classes = li[1]
bbox = [int(li[2]), int(li[3]), int(li[4]), int(li[5])]
confidence = li[6]
pd_array.append([name, bbox, classes, confidence])
pd_len += 1
for lune in gt:
lu = lune.split(' ')
nome = lu[0]
clisses = lu[1]
bbax = [int(lu[2]), int(lu[3]), int(lu[4]), int(lu[5])]
gt_array.append([nome, bbax, clisses])
gt_len += 1
for item in pd_array:
# print(item[0])
name = item[0]
bbox = item[1]
classes = item[2]
confidence = item[3]
for thing in gt_array:
nome = thing[0]
bbax = thing[1]
clisses = thing[2]
if name in thing[0]:
# print("Found")
place = gt_array.index(thing)
if iou(bbox, bbax) >= 0.5:
# print("overlap")
# print(iou(bbox,bbax))
gt_cm.append(clisses)
pd_cm.append(classes)
if classes == clisses:
# print("Classes match! Success!")
# print("Classes: ", classes)
if classes == '0':
TP_ECHY += 1
# print("TP_ECHY: ", TP_ECHY)
elif classes == '1':
TP_ERY += 1
# print("TP_ERY: ", TP_ERY)
gt_array.pop(place)
# print("item removed")
else:
# print("Classes do not match, detection error")
if classes == '0':
if clisses == '1':
MissClass_ERY += 1
elif classes == '1':
if clisses == '0':
MissClass_ECHY += 1
gt_array.pop(place)
else:
# print("no overlap")
if classes== '0':
FP_ECHY += 1
elif classes == '1':
FP_ERY += 1
for item in gt_array:
# print(item)
name = item[0]
bbox = item[1]
classes = item[2]
if classes== '0':
FN_ECHY += 1
elif classes == '1':
FN_ERY += 1
FN_ECHY = FN_ECHY + MissClass_ECHY
FN_ERY = FN_ERY + MissClass_ERY
FP_ERY = FP_ERY + MissClass_ECHY
FP_ECHY = FP_ECHY + MissClass_ERY
print("True Positives ECHY: " + str(TP_ECHY))
print("True Positives ERY: " + str(TP_ERY))
TP = TP_ECHY + TP_ERY
print("True Positives: " + str(TP))
print("False Positives ECHY: " + str(FP_ECHY))
print("False Positives ERY: " + str(FP_ERY))
FP = FP_ECHY + FP_ERY
print("False Positives: " + str(FP))
print("False Negatives ECHY: " + str(FN_ECHY))
print("False Negatives ERY: " + str(FN_ERY))
FN = FN_ECHY + FN_ERY
print("False Negatives: " + str(FN))
print("MissClass ECHY: " + str(MissClass_ECHY))
print("MissClass ERY: " + str(MissClass_ERY))
predicted = pd_len
print("Predicted: " + str(predicted))
actual = gt_len
accuracy = (TP ) / (TP + FP + FN)
print("Actual: " + str(actual))
print("Accuracy: " + str(round(accuracy, 3)))
if TP + FP + FN != 0:
F1 = (TP)/(TP+ ((FP + FN)/2))
print("F1: " + str(round(F1, 3)))
else:
print("F1: 0")
if TP + FP != 0:
precision = TP / (TP + FP)
print("Precision: " + str(round(precision, 3)))
else:
print("Precision: 0")
if TP + FN != 0:
recall = TP / (TP + FN)
print("Recall: " + str(round(recall, 3)))
print("MissClass: " + str(MissClass_ECHY + MissClass_ERY))
# name = pd_file.split('/')
path = '/home/as-hunt/Etra-Space/neu-mon/'
name = 'Normalised Confusion Matrix ' + title + ' Post bbox matching'
# print(gt_cm)
# print(pd_cm)
y_actu = pd.Series(gt_cm, name='Ground Truth')
y_pred = pd.Series(pd_cm, name='Predicted')
F1m = f1_score(y_actu, y_pred, average='macro')
print("F1 macro: " + str(F1m))
F1w = f1_score(y_actu, y_pred, average='weighted')
print("F1 weighted: " + str(F1w))
F1n = f1_score(y_actu, y_pred, average=None)
print("F1 none: " + str(F1n))
acc = accuracy_score(y_actu, y_pred)
print("Accuracy score sklearn: " + str(acc))
target_names = ['Mononuclear cell', 'Neutrophil']
print(classification_report(y_actu, y_pred, target_names=target_names))
precision_score_weighted = precision_score(y_actu, y_pred, average='weighted')
print("Precision score weighted: " + str(precision_score_weighted))
precision_score_macro = precision_score(y_actu, y_pred, average='macro')
print("Precision score macro: " + str(precision_score_macro))
precision_score_none = precision_score(y_actu, y_pred, average=None)
print("Precision score none: " + str(precision_score_none))
recall_score_weighted = recall_score(y_actu, y_pred, average='weighted')
print("Recall score weighted: " + str(recall_score_weighted))
recall_score_macro = recall_score(y_actu, y_pred, average='macro')
print("Recall score macro: " + str(recall_score_macro))
recall_score_none = recall_score(y_actu, y_pred, average=None)
print("Recall score none: " + str(recall_score_none))
fbeta05_score_weighted = fbeta_score(y_actu, y_pred, average='weighted', beta=0.5)
print("F0.5 measure weighted: " + str(fbeta05_score_weighted))
fbeta05_score_macro = fbeta_score(y_actu, y_pred, average='macro', beta=0.5)
print("F0.5 measure macro: " + str(fbeta05_score_macro))
fbeta05_score_none = fbeta_score(y_actu, y_pred, average=None, beta=0.5)
print("F0.5 measure none: " + str(fbeta05_score_none))
fbeta2_score_weighted = fbeta_score(y_actu, y_pred, average='weighted', beta=2)
print("F2 measure weighted: " + str(fbeta2_score_weighted))
fbeta2_score_macro = fbeta_score(y_actu, y_pred, average='macro', beta=2)
print("F2 measure macro: " + str(fbeta2_score_macro))
fbeta2_score_none = fbeta_score(y_actu, y_pred, average=None, beta=2)
print("F2 measure none: " + str(fbeta2_score_none))
df_confusion = pd.crosstab(y_actu, y_pred, dropna=False)
df_conf_norm = df_confusion.div(df_confusion.sum(axis=1), axis="index")
# plt.figure(figsize = (10,7))
plt.title(title)
sns.set(font_scale=1.4) # for label size
sns.heatmap(df_conf_norm, cmap='coolwarm', annot=True, annot_kws={"size": 16}, xticklabels=target_names, yticklabels=target_names) # font size
tick_marks = np.arange(len(df_conf_norm.columns))
plt.savefig(path + name + '.png', bbox_inches='tight')
plt.clf()
name = 'Confusion Matrix ' + title + ' Post bbox matching'
# plt.figure(figsize = (10,7))
plt.title(title)
sns.set(font_scale=1.4) # for label size
sns.heatmap(df_confusion, cmap='coolwarm', annot=True, annot_kws={"size": 16}, xticklabels=target_names, yticklabels=target_names) # font size
tick_marks = np.arange(len(df_confusion.columns))
plt.savefig(path + name + '.png', bbox_inches='tight')
count_classes_file(gt_file, True, title + '_split.png', target_names)
def do_math_three(gt_file, pd_file, title):
gt = open(gt_file, 'r')
gt_array = []
gt_len = 0
gt_cm = []
pud = open(pd_file, 'r')
pd_len = 0
pd_array = []
pd_cm = []
TP_ECHY = 0
TP_ERY = 0
FP_ECHY = 0
FP_ERY = 0
FN_ECHY = 0
FN_ERY = 0
MissClass_ECHY = 0
MissClass_ERY = 0
for line in pud:
# print(line)
li = line.split(' ')
name = li[0]
classes = li[1]
bbox = [int(li[2]), int(li[3]), int(li[4]), int(li[5])]
confidence = li[6]
pd_array.append([name, bbox, classes, confidence])
pd_len += 1
for lune in gt:
lu = lune.split(' ')
nome = lu[0]
clisses = lu[1]
bbax = [int(lu[2]), int(lu[3]), int(lu[4]), int(lu[5])]
gt_array.append([nome, bbax, clisses])
gt_len += 1
for item in pd_array:
# print(item[0])
name = item[0]
bbox = item[1]
classes = item[2]
confidence = item[3]
for thing in gt_array:
nome = thing[0]
bbax = thing[1]
clisses = thing[2]
if name in thing[0]:
# print("Found")
place = gt_array.index(thing)
if iou(bbox, bbax) >= 0.5:
# print("overlap")
# print(iou(bbox,bbax))
gt_cm.append(clisses)
pd_cm.append(classes)
if classes == clisses:
# print("Classes match! Success!")
# print("Classes: ", classes)
if classes == '0':
TP_ECHY += 1
# print("TP_ECHY: ", TP_ECHY)
elif classes == '1':
TP_ERY += 1
# print("TP_ERY: ", TP_ERY)
gt_array.pop(place)
# print("item removed")
else:
# print("Classes do not match, detection error")
if classes == '0':
if clisses == '1':
MissClass_ERY += 1
elif classes == '1':
if clisses == '0':
MissClass_ECHY += 1
gt_array.pop(place)
else:
# print("no overlap")
if classes== '0':
FP_ECHY += 1
elif classes == '1':
FP_ERY += 1
for item in gt_array:
# print(item)
name = item[0]
bbox = item[1]
classes = item[2]
if classes== '0':
FN_ECHY += 1
elif classes == '1':
FN_ERY += 1
FN_ECHY = FN_ECHY + MissClass_ECHY
FN_ERY = FN_ERY + MissClass_ERY
FP_ERY = FP_ERY + MissClass_ECHY
FP_ECHY = FP_ECHY + MissClass_ERY
print("True Positives ECHY: " + str(TP_ECHY))
print("True Positives ERY: " + str(TP_ERY))
TP = TP_ECHY + TP_ERY
print("True Positives: " + str(TP))
print("False Positives ECHY: " + str(FP_ECHY))
print("False Positives ERY: " + str(FP_ERY))
FP = FP_ECHY + FP_ERY
print("False Positives: " + str(FP))
print("False Negatives ECHY: " + str(FN_ECHY))
print("False Negatives ERY: " + str(FN_ERY))
FN = FN_ECHY + FN_ERY
print("False Negatives: " + str(FN))
print("MissClass ECHY: " + str(MissClass_ECHY))
print("MissClass ERY: " + str(MissClass_ERY))
predicted = pd_len
print("Predicted: " + str(predicted))
actual = gt_len
accuracy = (TP ) / (TP + FP + FN)
print("Actual: " + str(actual))
print("Accuracy: " + str(round(accuracy, 3)))
if TP + FP + FN != 0:
F1 = (TP)/(TP+ ((FP + FN)/2))
print("F1: " + str(round(F1, 3)))
else:
print("F1: 0")
if TP + FP != 0:
precision = TP / (TP + FP)
print("Precision: " + str(round(precision, 3)))
else:
print("Precision: 0")
if TP + FN != 0:
recall = TP / (TP + FN)
print("Recall: " + str(round(recall, 3)))
print("MissClass: " + str(MissClass_ECHY + MissClass_ERY))
# name = pd_file.split('/')
path = '/home/as-hunt/Etra-Space/5-class-more/'
name = 'Normalised Confusion Matrix ' + title + ' Post bbox matching'
# print(gt_cm)
# print(pd_cm)
y_actu = pd.Series(gt_cm, name='Ground Truth')
y_pred = pd.Series(pd_cm, name='Predicted')
F1m = f1_score(y_actu, y_pred, average='macro')
print("F1 macro: " + str(F1m))
F1w = f1_score(y_actu, y_pred, average='weighted')
print("F1 weighted: " + str(F1w))
F1n = f1_score(y_actu, y_pred, average=None)
print("F1 none: " + str(F1n))
acc = accuracy_score(y_actu, y_pred)
print("Accuracy score sklearn: " + str(acc))
target_names = ['Erythrocyte', 'Platelet', 'Leukocyte']
print(classification_report(y_actu, y_pred, target_names=target_names))
precision_score_weighted = precision_score(y_actu, y_pred, average='weighted')
print("Precision score weighted: " + str(precision_score_weighted))
precision_score_macro = precision_score(y_actu, y_pred, average='macro')
print("Precision score macro: " + str(precision_score_macro))
precision_score_none = precision_score(y_actu, y_pred, average=None)
print("Precision score none: " + str(precision_score_none))
recall_score_weighted = recall_score(y_actu, y_pred, average='weighted')
print("Recall score weighted: " + str(recall_score_weighted))
recall_score_macro = recall_score(y_actu, y_pred, average='macro')
print("Recall score macro: " + str(recall_score_macro))
recall_score_none = recall_score(y_actu, y_pred, average=None)
print("Recall score none: " + str(recall_score_none))
fbeta05_score_weighted = fbeta_score(y_actu, y_pred, average='weighted', beta=0.5)
print("F0.5 measure weighted: " + str(fbeta05_score_weighted))
fbeta05_score_macro = fbeta_score(y_actu, y_pred, average='macro', beta=0.5)
print("F0.5 measure macro: " + str(fbeta05_score_macro))
fbeta05_score_none = fbeta_score(y_actu, y_pred, average=None, beta=0.5)
print("F0.5 measure none: " + str(fbeta05_score_none))
fbeta2_score_weighted = fbeta_score(y_actu, y_pred, average='weighted', beta=2)
print("F2 measure weighted: " + str(fbeta2_score_weighted))
fbeta2_score_macro = fbeta_score(y_actu, y_pred, average='macro', beta=2)
print("F2 measure macro: " + str(fbeta2_score_macro))
fbeta2_score_none = fbeta_score(y_actu, y_pred, average=None, beta=2)
print("F2 measure none: " + str(fbeta2_score_none))
df_confusion = pd.crosstab(y_actu, y_pred, dropna=False)
df_conf_norm = df_confusion.div(df_confusion.sum(axis=1), axis="index")
# plt.figure(figsize = (10,7))
plt.title(title)
sns.set(font_scale=1.4) # for label size
sns.heatmap(df_conf_norm, cmap='coolwarm', annot=True, annot_kws={"size": 16}, xticklabels=target_names, yticklabels=target_names) # font size
tick_marks = np.arange(len(df_conf_norm.columns))
plt.savefig(path + name + '.png', bbox_inches='tight')
plt.clf()
name = 'Confusion Matrix ' + title + ' Post bbox matching'
# plt.figure(figsize = (10,7))
plt.title(title)
sns.set(font_scale=1.4) # for label size
sns.heatmap(df_confusion, cmap='coolwarm', annot=True, annot_kws={"size": 16}, xticklabels=target_names, yticklabels=target_names) # font size
tick_marks = np.arange(len(df_confusion.columns))
plt.savefig(path + name + '.png', bbox_inches='tight')
count_classes_file(gt_file, True, title + '_split.png', target_names)
def do_math_all(gt_file, pd_file, title):
gt = open(gt_file, 'r')
gt_array = []
gt_len = 0
gt_cm = []
pud = open(pd_file, 'r')
pd_len = 0
pd_array = []
pd_cm = []
TP_ECHY = 0
TP_ERY = 0
TP_PLT = 0
TP_WBC = 0
TP_SIDE = 0
FP_ECHY = 0
FP_PLT = 0
FP_ERY = 0
FP_WBC = 0
FP_SIDE = 0
FN_ECHY = 0
FN_ERY = 0
FN_PLT = 0
FN_WBC = 0
FN_SIDE = 0
MissClass_ECHY_as_ERY = 0
MissClass_ECHY_as_PLT = 0
MissClass_ECHY_as_WBC = 0
MissClass_ECHY_as_SIDE = 0
MissClass_ERY_as_ECHY = 0
MissClass_ERY_as_PLT = 0
MissClass_ERY_as_WBC = 0
MissClass_ERY_as_SIDE = 0
MissClass_PLT_as_ECHY = 0
MissClass_PLT_as_ERY = 0
MissClass_PLT_as_WBC = 0
MissClass_PLT_as_SIDE = 0
MissClass_WBC_as_ECHY = 0
MissClass_WBC_as_ERY = 0
MissClass_WBC_as_PLT = 0
MissClass_WBC_as_SIDE = 0
MissClass_SIDE_as_ECHY = 0
MissClass_SIDE_as_ERY = 0
MissClass_SIDE_as_PLT = 0
MissClass_SIDE_as_WBC = 0
for line in pud:
li = line.split(' ')
name = li[0]
classes = li[1]
bbox = [int(li[2]), int(li[3]), int(li[4]), int(li[5])]
confidence = li[6]
pd_array.append([name, bbox, classes, confidence])
pd_len += 1
for lune in gt:
lu = lune.split(' ')
nome = lu[0]
clisses = lu[1]
bbax = [int(lu[2]), int(lu[3]), int(lu[4]), int(lu[5])]
gt_array.append([nome, bbax, clisses])
gt_len += 1
for item in pd_array:
name = item[0]
bbox = item[1]
classes = item[2]
confidence = item[3]
for thing in gt_array:
nome = thing[0]
bbax = thing[1]
clisses = thing[2]
if name in thing[0]:
place = gt_array.index(thing)
if iou(bbox, bbax) >= 0.5:
gt_cm.append(clisses)
pd_cm.append(classes)
if classes == clisses:
if classes == '0':
TP_ECHY += 1
elif classes == '1':
TP_ERY += 1
elif classes == '2':
TP_PLT += 1
elif classes == '3':
TP_SIDE += 1
elif classes == '4':
TP_WBC += 1
gt_array.pop(place)
else:
if classes == '0':
if clisses == '1':
MissClass_ERY_as_ECHY += 1
elif clisses == '2':
MissClass_PLT_as_ECHY += 1
elif clisses == '3':
MissClass_SIDE_as_ECHY += 1
elif clisses == '4':
MissClass_WBC_as_ECHY += 1
elif classes == '1':
if clisses == '0':
MissClass_ECHY_as_ERY += 1
elif clisses == '2':
MissClass_PLT_as_WBC += 1
elif clisses == '3':
MissClass_SIDE_as_ERY += 1
elif clisses == '4':
MissClass_WBC_as_ERY += 1
elif classes == '2':
if clisses == '0':
MissClass_ECHY_as_PLT += 1
elif clisses == '1':
MissClass_ERY_as_PLT += 1
elif clisses == '3':
MissClass_SIDE_as_PLT += 1
elif clisses == '4':
MissClass_WBC_as_PLT += 1
elif classes == '3':
if clisses == '0':
MissClass_ECHY_as_SIDE += 1
elif clisses == '1':
MissClass_ERY_as_SIDE += 1
elif clisses == '2':
MissClass_PLT_as_SIDE += 1
elif clisses == '4':
MissClass_WBC_as_SIDE += 1
elif classes == '4':
if clisses == '0':
MissClass_ECHY_as_WBC += 1
elif clisses == '1':
MissClass_ERY_as_WBC += 1
elif clisses == '2':
MissClass_PLT_as_WBC += 1
elif clisses == '3':
MissClass_SIDE_as_WBC += 1
gt_array.pop(place)
else:
if classes== '0':
FP_ECHY += 1
elif classes == '1':
FP_ERY += 1
elif classes == '2':
FP_PLT += 1
elif classes == '3':
FP_SIDE += 1
elif classes == '4':
FP_WBC += 1
for item in gt_array:
name = item[0]
bbox = item[1]
classes = item[2]
if classes== '0':
FN_ECHY += 1
elif classes == '1':
FN_ERY += 1
elif classes == '2':
FN_PLT += 1
elif classes == '3':
FN_WBC += 1
# FNECHY = FN_ECHY + MissClass_ECHY_as_ERY + MissClass_ECHY_as_PLT + MissClass_ECHY_as_WBC + MissClass_ECHY_as_SIDE
# MissClass_ECHY = MissClass_ECHY_as_ERY + MissClass_ECHY_as_PLT + MissClass_ECHY_as_WBC + MissClass_ECHY_as_SIDE
# FNERY = FN_ERY + MissClass_ERY_as_ECHY + MissClass_ERY_as_PLT + MissClass_ERY_as_WBC + MissClass_ERY_as_SIDE
# MissClass_ERY = MissClass_ERY_as_ECHY + MissClass_ERY_as_PLT + MissClass_ERY_as_WBC + MissClass_ERY_as_SIDE
# FNPLT = FN_PLT + MissClass_PLT_as_ECHY + MissClass_PLT_as_ERY + MissClass_PLT_as_WBC + MissClass_PLT_as_SIDE
# MissClass_PLT = MissClass_PLT_as_ECHY + MissClass_PLT_as_ERY + MissClass_PLT_as_WBC + MissClass_PLT_as_SIDE
# FNWBC = FN_WBC + MissClass_WBC_as_ECHY + MissClass_WBC_as_ERY + MissClass_WBC_as_PLT + MissClass_WBC_as_SIDE
# MissClass_WBC = MissClass_WBC_as_ECHY + MissClass_WBC_as_ERY + MissClass_WBC_as_PLT + MissClass_WBC_as_SIDE
# MissClass_SIDE = MissClass_SIDE_as_ECHY + MissClass_SIDE_as_ERY + MissClass_SIDE_as_PLT + MissClass_SIDE_as_WBC
# FNSIDE = FN_SIDE + MissClass_SIDE_as_ECHY + MissClass_SIDE_as_ERY + MissClass_SIDE_as_PLT + MissClass_SIDE_as_WBC
# FPERY = FP_ERY + MissClass_ECHY_as_ERY + MissClass_PLT_as_ERY + MissClass_WBC_as_ERY + MissClass_SIDE_as_ERY
# FPECHY = FP_ECHY + MissClass_ERY_as_ECHY + MissClass_PLT_as_ECHY + MissClass_WBC_as_ECHY + MissClass_SIDE_as_ECHY
# FPPLT = FP_PLT + MissClass_ECHY_as_PLT + MissClass_ERY_as_PLT + MissClass_WBC_as_PLT + MissClass_SIDE_as_PLT
# FPWBC = FP_WBC + MissClass_ECHY_as_WBC + MissClass_ERY_as_WBC + MissClass_PLT_as_WBC + MissClass_SIDE_as_WBC
# FPSIDE = FP_SIDE + MissClass_ECHY_as_SIDE + MissClass_ERY_as_SIDE + MissClass_PLT_as_SIDE + MissClass_WBC_as_SIDE
# print("True Positives ECHY: " + str(TP_ECHY))
# print("True Positives ERY: " + str(TP_ERY))
# print("True Positives PLT: " + str(TP_PLT))
# print("True Positives SIDE: " + str(TP_SIDE))
# print("True Positives WBC: " + str(TP_WBC))
# TP = TP_ECHY + TP_ERY + TP_WBC + TP_PLT
# print("True Positives: " + str(TP))
# print("False Positives ECHY: " + str(FP_ECHY))
# print("False Positives ERY: " + str(FP_ERY))
# print("False Positives PLT: " + str(FP_PLT))
# print("False Positives SIDE: " + str(FP_SIDE))
# print("False Positives WBC: " + str(FP_WBC))
# FP = FPECHY + FPERY + FPWBC + FPPLT
# print("False Positives: " + str(FP))
# print("False Negatives ECHY: " + str(FN_ECHY))
# print("False Negatives ERY: " + str(FN_ERY))
# print("False Negatives PLT: " + str(FN_PLT))
# print("False Negatives SIDE: " + str(FN_SIDE))
# print("False Negatives WBC: " + str(FN_WBC))
# FN = FNECHY + FNERY + FNWBC + FNPLT
# print("False Negatives: " + str(FN))
# print("MissClass ECHY: " + str(MissClass_ECHY))
# print("MissClass ERY: " + str(MissClass_ERY))
# print("MissClass PLT: " + str(MissClass_PLT))
# print("MissClass SIDE: " + str(MissClass_SIDE))
# print("MissClass WBC: " + str(MissClass_WBC))
# predicted = pd_len
# print("Predicted: " + str(predicted))