Caffe confusion matrix, precision and recall and F1 Score script!

confusionMatrix_convnet_test_BatchMode(VeryFast).py

#in the name of God the most compassionate the most merciful
# added mean subtraction so that, the accuracy can be reported accurately just like caffe when doing a mean subtraction
# Seyyed Hossein Hasan Pour
# [email protected]
# 7/3/2016 
# Added Recall/Precision/F1-Score as well 
# 01/03/2017
# Added batch processing, not what used to take a minute or so, takes only several seconds!
# 07/25/2017
#info: 
#if on windows, one can use these command in a batch file and ease him/her self 
#REM Calculating Confusing Matrix 
#python confusionMatrix_convnet_test.py --proto cifar10_deploy.prototxt --model cifar10.caffemodel --mean mean.binaryproto --lmdb cifar10_test_lmdb  
#pause


import sys
import caffe
import numpy as np
import lmdb
import argparse
from collections import defaultdict
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import itertools
from sklearn.metrics import roc_curve, auc
import random

def flat_shape(x):
    "Returns x without singleton dimension, eg: (1,28,28) -> (28,28)"
    return np.reshape(x,x.shape)

def plot_confusion_matrix(cm #confusion matrix
                         ,classes 
                          ,normalize=False
                          ,title='Confusion matrix'
                          ,cmap=plt.cm.Blues):
    """
    This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`.
    """
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, rotation=45)
    plt.yticks(tick_marks, classes)

    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        print("confusion matrix is normalized!")
    
    #print(cm)

    thresh = cm.max() / 2.
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, cm[i, j],
                 horizontalalignment="center",
                 color="white" if cm[i, j] > thresh else "black")

    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label')
    
   
def db_reader(fpath, type='lmdb'):
    if type == 'lmdb':
        return lmdb_reader(fpath)
    else:
       return leveldb_reader(fpath)

    
def lmdb_reader(fpath):
    import lmdb
    lmdb_env = lmdb.open(fpath)
    lmdb_txn = lmdb_env.begin()
    lmdb_cursor = lmdb_txn.cursor()

    for key, value in lmdb_cursor:
        datum = caffe.proto.caffe_pb2.Datum()
        datum.ParseFromString(value)
        label = int(datum.label)
        image = caffe.io.datum_to_array(datum).astype(np.uint8)
        yield (key, flat_shape(image), label)

def leveldb_reader(fpath):
    import leveldb
    db = leveldb.LevelDB(fpath)

    for key, value in db.RangeIter():
        datum = caffe.proto.caffe_pb2.Datum()
        datum.ParseFromString(value)
        label = int(datum.label)
        image = caffe.io.datum_to_array(datum).astype(np.uint8)
        yield (key, flat_shape(image), label)

        
def ShowInfo(correct, count, true_labels, predicted_lables, class_names, misclassified,
             filename='misclassifieds.txt',
             title='Receiver Operating Characteristic_ROC',
             title_CM='Confusion matrix, without normalization',
             title_CM_N='Normalized confusion matrix'):
    sys.stdout.write("\rAccuracy: %.1f%%" % (100.*correct/count))
    sys.stdout.flush()
    print(", %i/%i corrects" % (correct, count))
    
    np.savetxt(filename,misclassified,fmt="%s")     
    print( classification_report(y_true=true_labels,
                                 y_pred=predicted_lables,
                                 target_names=class_names))
                                 
    cm = confusion_matrix(y_true=true_labels,
                            y_pred=predicted_lables)    
    print(cm)   

    # print(title)    
    # false_positive_rate, true_positive_rate, thresholds = roc_curve(true_labels, predicted_lables)
    # roc_auc = auc(false_positive_rate, true_positive_rate)                                     
    # plt.title('Receiver Operating Characteristic_ROC 1')
    # plt.plot(false_positive_rate, true_positive_rate, 'b',
    # label='AUC = %0.2f'% roc_auc)
    # plt.legend(loc='lower right')
    # plt.plot([0,1],[0,1],'r--')
    # plt.xlim([-0.1,1.2])
    # plt.ylim([-0.1,1.2])
    # plt.ylabel('True Positive Rate')
    # plt.xlabel('False Positive Rate')
    # plt.show()                      

    # Compute confusion matrix
    cnf_matrix = cm
    np.set_printoptions(precision=2)
    # Plot non-normalized confusion matrix
    plt.figure()
    plot_confusion_matrix(cnf_matrix, classes=class_names,
                          title=title_CM)
    # Plot normalized confusion matrix
    plt.figure()
    plot_confusion_matrix(cnf_matrix, classes=class_names, normalize=True,
                          title=title_CM_N)
    plt.show()
    

        
if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument('--proto', help='path to the network prototxt file(deploy)', type=str, required=True)
    parser.add_argument('--model', help='path to your caffemodel file', type=str, required=True)
    parser.add_argument('--mean', help='path to the mean file(.binaryproto)', type=str, required=True)
    #group = parser.add_mutually_exclusive_group(required=True)
    parser.add_argument('--db_type', help='lmdb or leveldb', type=str, required=True)
    parser.add_argument('--db_path', help='path to your lmdb/leveldb dataset', type=str, required=True)
    args = parser.parse_args()

   # Extract mean from the mean image file
    mean_blobproto_new = caffe.proto.caffe_pb2.BlobProto()
    f = open(args.mean, 'rb')
    mean_blobproto_new.ParseFromString(f.read())
    mean_image = caffe.io.blobproto_to_array(mean_blobproto_new)
    f.close()
    
	#mu = np.load('mean.npy')
    #mu = np.array([ 104,  117,  123])#imagenet mean
    
    caffe.set_mode_gpu() 
    #CNN reconstruction and loading the trained weights 
    #print ("args", vars(args))
    
  
    
    predicted_lables=[]
    true_labels = []
    misclassified =[]
    #class_names = ['unsafe','safe']
    class_names = ['airplane','automobile','bird','cat','deer','dog','frog','horse','ship','truck']
    count=0
    correct = 0
    idx=0
    batch=[]
    plabe_ls=[]
    batch_size = 50
    net1 = caffe.Net(args.proto, args.model, caffe.TEST) 
    transformer = caffe.io.Transformer({'data': net1.blobs['data'].data.shape})
    #transformer.set_transpose('data', (2,0,1))  
    transformer.set_mean('data', mean_image[0])         
    #transformer.set_raw_scale('data', 1)      
    transformer.set_channel_swap('data', (2,1,0)) 
    net1.blobs['data'].reshape(batch_size, 3,32, 32)
    data_blob_shape = net1.blobs['data'].data.shape
    data_blob_shape = list(data_blob_shape)
    #net1.blobs['data'].reshape(batch_size, data_blob_shape[1], data_blob_shape[2], data_blob_shape[3])  
    i=0
    #check and see if its lmdb or leveldb
    if(args.db_type.lower() == 'lmdb'):
        lmdb_env = lmdb.open(args.db_path)
        lmdb_txn = lmdb_env.begin()
        lmdb_cursor = lmdb_txn.cursor()
        for key, value in lmdb_cursor:
            count += 1 
            datum = caffe.proto.caffe_pb2.Datum()
            datum.ParseFromString(value)
            label = int(datum.label)
            image = caffe.io.datum_to_array(datum).astype(np.uint8)
            #key,image,label
            #buffer n image
            if(count%2000==0): 			
                print('count: ',count)
            if(i < batch_size):
                i+=1
                inf= key,image,label
                batch.append(inf)
                #print(key) 				
            if(i >= batch_size):
                #process n image 
                ims=[]				
                images = [image_info for image_info in batch ]
                for x in range(len(batch)):
				     #using transformer here decreases performance! 
                    ims.append(batch[x][1]-mean_image[0]) #ims.append(transformer.preprocess('data',batch[x][1] ))
                net1.blobs['data'].data[...] = ims[:]
                out_1 = net1.forward()
                plabe_ls = out_1['prob']#.argmax(axis=0)
                plbl = np.asarray(plabe_ls)
                #print(plbl)
                #print(plbl.argmax(axis=1))   				
                plbl = plbl.argmax(axis=1)
                for j in range(len(batch)):
                    if (plbl[j] == batch[j][2]):
                        correct+=1
                    else:
                        misclassified.append(batch[j][0])
						
                    predicted_lables.append(plbl[j])        
                    true_labels.append(batch[j][2])	
                batch.clear()
                i=0  				
                 				 
                
        ShowInfo(correct,count, true_labels, predicted_lables, class_names, misclassified,
        filename='misclassifieds.txt',
        title='Receiver Operating Characteristic_ROC' )                        
                               
                                     
                                      
                                      
    else:#leveldb
        import leveldb
        db = leveldb.LevelDB(args.db_path)
        for key, value in db.RangeIter():
            datum = caffe.proto.caffe_pb2.Datum()
            datum.ParseFromString(value)
            label = int(datum.label)
            image = caffe.io.datum_to_array(datum).astype(np.uint8)      
            #key,image,label
            #buffer n image
            #print('count: ',count)
            if(i < batch_size):
                i+=1
                inf= key,image,label
                batch.append(inf)
                #print(key) 				
            if(i >= batch_size):
                #process n image 
                ims=[]				
                images = [image_info for image_info in batch ]
                for x in range(len(batch)):
                    ims.append(batch[x][1]-mean_image[0]) #ims.append(transformer.preprocess('data',batch[x][1]))
                net1.blobs['data'].data[...] = ims[:]
                out_1 = net1.forward()
                plabe_ls = out_1['prob']#.argmax(axis=0)
                plbl = np.asarray(plabe_ls)
                #print(plbl)
                #print(plbl.argmax(axis=1))   				
                plbl = plbl.argmax(axis=1)
                for j in range(len(batch)):
                    if (plbl[j] == batch[j][2]):
                        correct+=1
                    else:
                        misclassified.append(batch[j][0])
						
                    predicted_lables.append(plbl[j])        
                    true_labels.append(batch[j][2])	
                batch.clear()
                i=0  				
                 				 
                
        ShowInfo(correct,count, true_labels, predicted_lables, class_names, misclassified,
        filename='misclassifieds.txt',
        title='Receiver Operating Characteristic_ROC' )