Code for printing the metrics about rvgan

[SharifAmit]

Hi,

Sorry for the late reply. I was busy with other projects.

I have uploaded the code for evaluation. (Link : https://github.com/SharifAmit/RVGAN/blob/master/eval.py)

If you find any problem with running the codes or results , please open an issue.

Thanks!

[htx-rest]

[SharifAmit]

The printed confusion matrix should be 2-by-2. However it looks 3-by-3 in your attached image.

Did you change anything in the eval file ?

In you image it says the error is in 179 line as given below, however in the original eval.py file it is line 188.

tn, fp, fn, tp = confusion.ravel()   

Also can you print the following before the above line ?

print(np.unique(y_true))
print(np.unique(y_pred))

It should give you the same number output for both the lines.

If it doesn't I believe either your y_true or y_pred is not int or float type. So its considering 1 and 1.0 as different values.

[SharifAmit]

Please open the issue, if your problem still persists.

Thanks

[Medicmind]

I had the same issue of a 3x3 confusion matrix but it because had to use jaccard_score instead of jaccard_similarity_score because of sklearn version issues and jaccard_score doesn't have a normalize parameter. So y_true has values 0 an 255 while y_pred is 0 an 1 meaning there is 3 values all up. If add the line y_true[y_true==255]=1 it normalizes y_true and gives you a 2x2 confusion matrix instead of 3x3. I have the entire eval code below:

import glob
import os
import time
import argparse
import numpy as np
from PIL import Image
from libtiff import TIFF
import tensorflow as tf
import cv2
import keras
from tensorflow.keras.optimizers import Adam
from keras.models import Model
import keras.backend as K
from src.model import coarse_generator,fine_generator
from skimage.metrics import structural_similarity as ssim
from sklearn.metrics import jaccard_score
#from sklearn.metrics import jaccard_similarity_score
from sklearn.metrics import confusion_matrix,precision_recall_curve,f1_score,roc_auc_score,auc,recall_score, auc,roc_curve


global g_local_model
global g_global_model

def normalize_pred(img,mask):
    img = np.reshape(img,[1,128,128,3])
    mask = np.reshape(mask,[1,128,128,1])
    img_coarse = tf.image.resize(img, (64,64), method=tf.image.ResizeMethod.LANCZOS3)
    img_coarse = (img_coarse - 127.5) / 127.5
    img_coarse = np.array(img_coarse)
    mask_coarse = tf.image.resize(mask, (64,64), method=tf.image.ResizeMethod.LANCZOS3)
    mask_coarse = (mask_coarse - 127.5) / 127.5
    mask_coarse = np.array(mask_coarse)
    
    _,x_global = g_global_model.predict([img_coarse,mask_coarse])
    #print('uuu',x_global.shape, np.min(x_global),np.max(x_global))  # (1, 64, 64, 128) -4.0635333 16.58276
    img = (img - 127.5) / 127.5
    mask = (mask - 127.5) / 127.5
    X_fakeB = g_local_model.predict([img,mask,x_global])
    X_fakeB = (X_fakeB + 1) /2.0
    X_fakeB = cv2.normalize(X_fakeB, None, alpha = 0, beta = 255, norm_type = cv2.NORM_MINMAX, dtype = cv2.CV_32F)
    pred_img = X_fakeB[:,:,:,0]
    return np.asarray(pred_img,dtype=np.float32)


def strided_crop(img, mask, img_h,img_w,height, width,stride=1):
  
    full_prob = np.zeros((img_h, img_w),dtype=np.float32)
    full_sum = np.ones((img_h, img_w),dtype=np.float32)
    
    max_x = int(((img.shape[0]-height)/stride)+1)
    #print("max_x:",max_x)
    max_y = int(((img.shape[1]-width)/stride)+1)
    #print("max_y:",max_y)
    max_crops = (max_x)*(max_y)
    i = 0
    for h in range(max_x):
        for w in range(max_y):
                crop_img_arr = img[h * stride:(h * stride) + height,w * stride:(w * stride) + width]
                crop_mask_arr = mask[h * stride:(h * stride) + height,w * stride:(w * stride) + width]
                pred = normalize_pred(crop_img_arr,crop_mask_arr)
                crop_img_arr 
                full_prob[h * stride:(h * stride) + height,w * stride:(w * stride) + width] += pred[0]
                full_sum[h * stride:(h * stride) + height,w * stride:(w * stride) + width] += 1
                i = i + 1
    out_img = full_prob / full_sum
    return out_img




if __name__ == "__main__":

    parser = argparse.ArgumentParser()
    parser.add_argument('--test_data', type=str, default='DRIVE', required=True, choices=['DRIVE','CHASE','STARE'])
    parser.add_argument('--weight_name_global',type=str, help='path/to/global/weight/.h5 file', required=True)
    parser.add_argument('--weight_name_local',type=str, help='path/to/local/weight/.h5 file', required=True)
    parser.add_argument('--stride', type=int, default=3, help='For faster inference use stride 16/32, for better result use stride 3.')
    args = parser.parse_args() 

## Input dimensions

image_shape_fine = (128,128,3)
mask_shape_fine = (128,128,1)
label_shape_fine = (128,128,1)
image_shape_x_coarse = (64,64,128)
image_shape_coarse = (64,64,3)
mask_shape_coarse = (64,64,1)
label_shape_coarse = (64,64,1)
img_shape_g = (64,64,3)
ndf=64
ncf=128
nff=128

## Load models
K.clear_session()
opt = Adam()
g_local_model = fine_generator(x_coarse_shape=image_shape_x_coarse,input_shape=image_shape_fine,mask_shape=mask_shape_fine,nff=nff)
print("loc",args.weight_name_local)
g_local_model.load_weights(args.weight_name_local)
g_local_model.compile(loss='mse', optimizer=opt)
g_global_model = coarse_generator(img_shape=image_shape_coarse,mask_shape=mask_shape_coarse,ncf=ncf)
g_global_model.load_weights(args.weight_name_global)
g_global_model.compile(loss='mse',optimizer=opt)


## Find file numbers,paths or names
if args.test_data == 'DRIVE':
    limit = 20
elif args.test_data == 'CHASE':
    filenames = glob.glob("CHASE/test/images/*.jpg")
    limit = len(filenames)

elif args.test_data == 'STARE':
    arr = ["im0001","im0082","im0236","im0319"]
    limit = 4


## Iterating for each image

#960,999
w,h =584,565
y_true = np.zeros((limit,w,h),dtype=np.float32)
y_pred = np.zeros((limit,w,h),dtype=np.float32)
y_pred_auc = np.zeros((limit,w,h),dtype=np.float32)
c = 0
for i in range(0,limit):

    if args.test_data == 'DRIVE':
        
        if i<9:
            ind = str(0)
        else:
            ind = str("")
        label_name = "/home/ubuntu/efs/imagesets/drive/test/1st_manual/"+ind+str(i+1)+"_manual1.gif"
        print("file",label_name,i)
        label = Image.open(label_name)
        label_arr = np.asarray(label,dtype=np.float32)
        img_name = "/home/ubuntu/efs/imagesets/drive/test/images/"+ind+str(i+1)+"_test.tif"
        tif = TIFF.open(img_name)
        img_arr = tif.read_image(tif)
        img_arr = np.asarray(img_arr,dtype=np.float32)
        mask_name = "/home/ubuntu/efs/imagesets/drive/test/mask/"+ind+str(i+1)+"_test_mask.gif"
        mask = Image.open(mask_name)
        mask_arr = np.asarray(mask,dtype=np.float32)

    elif args.test_data == 'CHASE':
        k = filenames[i].split('/')
        k = k[-1].split('.')[0]
        label_name = "CHASE/test/labels/"+k+"_1stHO.png"
        label = Image.open(label_name)
        label_arr = np.asarray(label,dtype=np.float32)
        img_name = "CHASE/test/images/"+k+".jpg"
        img = Image.open(img_name)
        img_arr = np.asarray(img,dtype=np.float32)
        mask_name = "CHASE/test/mask/"+k+"_mask.png"
        mask = Image.open(mask_name)
        mask_arr = np.asarray(mask,dtype=np.float32)

    elif args.test_data == 'STARE':
        label_name = "STARE/test/labels-ah/"+arr[i]+".ah.ppm"
        label = Image.open(label_name)
        label_arr = np.asarray(label,dtype=np.float32)
        img_name = "STARE/test/stare-original-images/"+arr[i]+".ppm"
        img = Image.open(img_name)
        img_arr = np.asarray(img,dtype=np.float32)
        mask_name = "STARE/test/mask/"+arr[i]+"_mask.png"
        mask = Image.open(mask_name)
        mask_arr = np.asarray(mask,dtype=np.float32)


    ## Get the output predictions as array

    ## Stride =3 (best result),  Stride = 32 (faster result).
    out_img = strided_crop(img_arr, mask_arr, mask_arr.shape[0], mask_arr.shape[1], 128, 128,args.stride)

    print("cnt",np.count_nonzero(out_img),out_img.shape)
    out_img[mask_arr==0] = 0
    y_pred_auc[c,:,:] = out_img

    out_img[out_img>=0.5] = 1
    out_img[out_img<0.5] = 0
    y_true[c,:,:] = label_arr 
    y_pred[c,:,:] = out_img
    c = c +1
    
y_true[y_true==255]=1
y_true = y_true.flatten()
y_pred = y_pred.flatten()
y_pred_auc = y_pred_auc.flatten()
print("y_true",y_true.shape,np.unique(y_true),"y_pred",y_pred.shape,np.unique(y_pred))
confusion = confusion_matrix(y_true, y_pred)
print('confusion',confusion)

tn, fp, fn, tp = confusion.ravel()   
metric_cal = time.time()
if float(np.sum(confusion)) != 0:
    accuracy =  float(confusion[0, 0] + confusion[1, 1]) / float(np.sum(confusion))
print("Global Accuracy: " + str(accuracy))
specificity = 0
if float(confusion[0, 0] + confusion[0, 1]) != 0:
    specificity = tn / (tn + fp)
print("Specificity: " + str(specificity))
sensitivity = 0
if float(confusion[1, 1] + confusion[1, 0]) != 0:
    sensitivity = tp / (tp + fn) 
print("Sensitivity: " + str(sensitivity))

precision = 0
if float(confusion[1, 1] + confusion[0, 1]) != 0:
    precision = tp / (tp + fp) 
print("Precision: " + str(precision))


F1_score = 2*tp/(2*tp+fn+fp) 
print("F1 score (F-measure): " + str(F1_score))

AUC_ROC = roc_auc_score(y_true, y_pred_auc)
print("AUC_ROC: " + str(AUC_ROC))

ssim = ssim(y_true, y_pred, data_range=y_true.max()-y_true.min())
print("SSIM: " + str(ssim))

meanIOU = jaccard_score(y_true,y_pred) #,pos_label=255.0) #normalize=True)
print("meanIOU: " + str(meanIOU))