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our_Zshoot.py
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# conda env:clip_for_test_chb
import sys
from os.path import abspath, dirname
parent_dir = abspath(dirname(dirname(__file__)))
sys.path.append(parent_dir)
from clip import CLIP
import sklearn.metrics as metrics
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
from PIL import Image
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import random
from sklearn.metrics import accuracy_score
# --------------------------------------------------------------------------------------------------
def fun_dataset(dataset_select):
if dataset_select == 'Kather':
captions=['MUCOSA',
'LYMPHO',
'DEBRIS',
'TUMOR',
'ADIPOSE',
'COMPLEX',
'STROMA',
'EMPTY']
if dataset_select == 'DigestPath':
captions=['Tumor', 'Solid Tissue Normal']
if dataset_select == 'PanNuke':
captions=['Uterus',
'Lung',
'Prostate',
'Cervix',
'Bile-duct',
'Skin',
'Testis',
'Thyroid',
'Ovarian',
'Kidney',
'Breast',
'Esophagus',
'Adrenal_gland',
'Stomach',
'Bladder',
'HeadNeck',
'Colon',
'Pancreatic',
'Liver']
if dataset_select == 'WSSS4LUAD':
captions=['Tumor', 'Solid Tissue Normal']
if dataset_select == 'our':
captions=['cancer_classification is Stomach adenocarcinoma',
'cancer_classification is Uveal Melanoma',
'cancer_classification is Kidney Chromophobe',
'cancer_classification is Uterine Carcinosarcoma',
'cancer_classification is Lung adenocarcinoma',
'cancer_classification is Kidney renal clear cell carcinoma',
'cancer_classification is Glioblastoma multiforme',
'cancer_classification is Colon adenocarcinoma',
'cancer_classification is Esophageal carcinoma ',
'cancer_classification is Pheochromocytoma and Paraganglioma',
'cancer_classification is Lymphoid Neoplasm Diffuse Large B-cell Lymphoma',
'cancer_classification is Pancreatic adenocarcinoma',
'cancer_classification is Prostate adenocarcinoma',
'cancer_classification is Thymoma',
'cancer_classification is Bladder Urothelial Carcinoma',
'cancer_classification is Uterine Corpus Endometrial Carcinoma',
'cancer_classification is Adrenocortical carcinoma',
'cancer_classification is Thyroid carcinoma',
'cancer_classification is Skin Cutaneous Melanoma',
'cancer_classification is Cervical squamous cell carcinoma and endocervical adenocarcinoma',
'cancer_classification is Rectum adenocarcinoma',
'cancer_classification is Kidney renal papillary cell carcinoma',
'cancer_classification is Testicular Germ Cell Tumors',
'cancer_classification is Mesothelioma',
'cancer_classification is Brain Lower Grade Glioma',
'cancer_classification is Sarcoma',
'cancer_classification is Lung squamous cell carcinoma',
'cancer_classification is Breast invasive carcinoma',
'cancer_classification is Liver hepatocellular carcinoma',
'cancer_classification is Cholangiocarcinoma',
'cancer_classification is Ovarian serous cystadenocarcinoma',
'cancer_classification is Head and Neck squamous cell carcinoma']
return captions
# --------------------------------------------------------------------------------------------------
if __name__ == "__main__":
clip = CLIP()
Best_F1 = [0,0,0]
Best_ACC = [0,0,0]
while True:
dataset_select = 'our' # Kather PanNuke WSSS4LUAD our
captions = fun_dataset(dataset_select)
ACC_LS = []
ls_F1 = []
# device = "cuda" if torch.cuda.is_available() else "cpu"
for number_one_class in [10,50,100]:
dataset = '../test_csv/{}.csv'.format(dataset_select)
csv_original = pd.read_csv(dataset)
tru_lable = []
predict = []
acc_dic = {caption: 0 for caption in captions}
sum = 0
right = 0
csv = csv_original.sample(number_one_class)
for index, row in csv.iterrows():
sum = sum+1
# print(number_one_class,"percentage: {:.4f}".format(sum/number_one_class))
# image_path = row['file_name']
real_classification = row['classification']
if dataset_select == 'our':
image_path = row['file_name']
else:
image_path = '../test_img/{}/'.format(dataset_select)+row['file_name']
image = Image.open(image_path)
probs = clip.detect_image(image, captions)
discribtion_match = captions[np.argmax(probs[0])]
tru_lable.append(real_classification)
predict.append(discribtion_match)
tru_lable.append(real_classification)
predict.append(discribtion_match)
if discribtion_match == real_classification:
right = right+1
# print(discribtion_match)
# print(real_classification)
# print()
# ------------------------------------------------
# print('predict over')
#
confusion_mat = confusion_matrix(tru_lable, predict, labels=captions)
# print("confusion_mat:\n", confusion_mat)
F1_Score = metrics.f1_score(tru_lable, predict, average='weighted')
# F1_Score = calculate_f1_score(confusion_mat)
ACC_66 = accuracy_score(tru_lable, predict)
print('F1_score:',round(F1_Score,3))
print('ACC:',round(ACC_66,4))
ls_F1.append(F1_Score)
ACC_LS.append(round(ACC_66,4))
# print('10 50 100')
# print('F1:',ls_F1)
# print('ACC:',ACC_LS)
#-------------------------------------
Best_F1 = [max(x, y) for x, y in zip(Best_F1, ls_F1)]
Best_ACC = [max(x, y) for x, y in zip(Best_ACC, ACC_LS)]
print()
print('***************************************our model-{}***************************************'.format(dataset_select))
print('10 50 100')
print('Best_F1:',Best_F1)
print('Best_ACC:',Best_ACC)
print('****************************************')
print()