Hi,
In the below code the extracted features are fed into the training and test sets into a RandomForestClassifier. However, the clf.predict() function in train_test.py is throwing array dimension error. Please can you comment?
#Globe.py
#-----------------------------------
GLOBAL FEATURE EXTRACTION
#-----------------------------------
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import MinMaxScaler
import numpy as np
import mahotas
import cv2
import os
import h5py
#--------------------
tunable-parameters
#--------------------
images_per_class = 80
fixed_size = tuple((500, 500))
train_path = “dataset/train”
h5_data = ‘output/data.h5’
h5_labels = ‘output/labels.h5’
bins = 8
feature-descriptor-1: Hu Moments
def fd_hu_moments(image):
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
feature = cv2.HuMoments(cv2.moments(image)).flatten()
return feature
feature-descriptor-2: Haralick Texture
def fd_haralick(image):
# convert the image to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# compute the haralick texture feature vector
haralick = mahotas.features.haralick(gray).mean(axis=0)
# return the result
return haralick
feature-descriptor-3: Color Histogram
def fd_histogram(image, mask=None):
# convert the image to HSV color-space
image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
# compute the color histogram
hist = cv2.calcHist([image], [0, 1, 2], None, [bins, bins, bins], [0, 256, 0, 256, 0, 256])
# normalize the histogram
cv2.normalize(hist, hist)
# return the histogram
return hist.flatten()
get the training labels
train_labels = os.listdir(train_path)
sort the training labels
train_labels.sort()
print(train_labels)
empty lists to hold feature vectors and labels
global_features = []
labels = []
loop over the training data sub-folders
for training_name in train_labels:
# join the training data path and each species training folder
dir = os.path.join(train_path, training_name)
# get the current training label
current_label = training_name
# loop over the images in each sub-folder
for x in range(1,images_per_class+1):
# get the image file name
file = dir + "/" + str(x) + ".jpg"
# read the image and resize it to a fixed-size
image = cv2.imread(file)
image = cv2.resize(image, fixed_size)
####################################
# Global Feature extraction
####################################
fv_hu_moments = fd_hu_moments(image)
fv_haralick = fd_haralick(image)
fv_histogram = fd_histogram(image)
###################################
# Concatenate global features
###################################
global_feature = np.hstack([fv_histogram, fv_haralick, fv_hu_moments])
# update the list of labels and feature vectors
labels.append(current_label)
global_features.append(global_feature)
print("[STATUS] processed folder: {}".format(current_label))
print("[STATUS] completed Global Feature Extraction…")
get the overall feature vector size
print("[STATUS] feature vector size {}".format(np.array(global_features).shape))
get the overall training label size
print("[STATUS] training Labels {}".format(np.array(labels).shape))
encode the target labels
targetNames = np.unique(labels)
le = LabelEncoder()
target = le.fit_transform(labels)
print("[STATUS] training labels encoded…")
scale features in the range (0-1)
scaler = MinMaxScaler(feature_range=(0, 1))
rescaled_features = scaler.fit_transform(global_features)
print("[STATUS] feature vector normalized…")
print("[STATUS] target labels: {}".format(target))
print("[STATUS] target labels shape: {}".format(target.shape))
save the feature vector using HDF5
h5f_data = h5py.File(h5_data, ‘w’)
h5f_data.create_dataset(‘dataset_1’, data=np.array(rescaled_features))
h5f_label = h5py.File(h5_labels, ‘w’)
h5f_label.create_dataset(‘dataset_1’, data=np.array(target))
h5f_data.close()
h5f_label.close()
print("[STATUS] end of training…")
#Train_test.py
#-----------------------------------
TRAINING OUR MODEL
#-----------------------------------
import h5py
import numpy as np
import os
import glob
import cv2
import warnings
from matplotlib import pyplot
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.model_selection import KFold, StratifiedKFold
from sklearn.metrics import confusion_matrix, accuracy_score, classification_report
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
import joblib
from globe import *
warnings.filterwarnings(‘ignore’)
#--------------------
tunable-parameters
#--------------------
num_trees = 100
test_size = 0.10
seed = 9
train_path = “dataset/train”
test_path = “dataset/test”
h5_data = ‘output/data.h5’
h5_labels = ‘output/labels.h5’
scoring = “accuracy”
get the training labels
train_labels = os.listdir(train_path)
sort the training labels
train_labels.sort()
if not os.path.exists(test_path):
os.makedirs(test_path)
create all the machine learning models
models = []
models.append((‘LR’, LogisticRegression(random_state=seed)))
models.append((‘LDA’, LinearDiscriminantAnalysis()))
models.append((‘KNN’, KNeighborsClassifier()))
models.append((‘CART’, DecisionTreeClassifier(random_state=seed)))
models.append((‘RF’, RandomForestClassifier(n_estimators=num_trees, random_state=seed)))
models.append((‘NB’, GaussianNB()))
models.append((‘SVM’, SVC(random_state=seed)))
variables to hold the results and names
results = []
names = []
import the feature vector and trained labels
h5f_data = h5py.File(h5_data, ‘r’)
h5f_label = h5py.File(h5_labels, ‘r’)
global_features_string = h5f_data[‘dataset_1’]
global_labels_string = h5f_label[‘dataset_1’]
global_features = np.array(global_features_string)
global_labels = np.array(global_labels_string)
h5f_data.close()
h5f_label.close()
verify the shape of the feature vector and labels
print("[STATUS] features shape: {}".format(global_features.shape))
print("[STATUS] labels shape: {}".format(global_labels.shape))
print("[STATUS] training started…")
split the training and testing data
(trainDataGlobal, testDataGlobal, trainLabelsGlobal, testLabelsGlobal) = train_test_split(np.array(global_features),
np.array(global_labels),
test_size=test_size,
random_state=seed)
print("[STATUS] splitted train and test data…")
print(“Train data : {}”.format(trainDataGlobal.shape))
print(“Test data : {}”.format(testDataGlobal.shape))
print(“Train labels: {}”.format(trainLabelsGlobal.shape))
print(“Test labels : {}”.format(testLabelsGlobal.shape))
10-fold cross validation
for name, model in models:
kfold = KFold(n_splits=10, random_state=seed)
cv_results = cross_val_score(model, trainDataGlobal, trainLabelsGlobal, cv=kfold, scoring=scoring)
results.append(cv_results)
names.append(name)
msg = “%s: %f (%f)” % (name, cv_results.mean(), cv_results.std())
print(msg)
boxplot algorithm comparison
fig = pyplot.figure()
fig.suptitle(‘Machine Learning algorithm comparison’)
ax = fig.add_subplot(111)
pyplot.boxplot(results)
ax.set_xticklabels(names)
pyplot.show()
#-----------------------------------
TESTING OUR MODEL
#-----------------------------------
to visualize results
import matplotlib.pyplot as plt
create the model - Random Forests
clf = RandomForestClassifier(n_estimators=num_trees, random_state=seed)
fit the training data to the model
clf.fit(trainDataGlobal, trainLabelsGlobal)
loop through the test images
for file in glob.glob(test_path + “/*.jpg”):
# read the image
image = cv2.imread(file)
# resize the image
image = cv2.resize(image, fixed_size)
####################################
# Global Feature extraction
####################################
fv_hu_moments = fd_hu_moments(image)
fv_haralick = fd_haralick(image)
fv_histogram = fd_histogram(image)
###################################
# Concatenate global features
###################################
global_feature = np.hstack([fv_histogram, fv_haralick, fv_hu_moments])
# scale features in the range (0-1)
scaler = MinMaxScaler(feature_range=(0, 1))
rescaled_feature = scaler.fit_transform(global_feature)
# predict label of test image
prediction = clf.predict(rescaled_feature.reshape(1,-1))
# show predicted label on image
cv2.putText(image, train_labels[prediction], (20,30), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0,255,255), 3)
# display the output image
plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
plt.show()
In train_test.py ,
prediction = clf.predict(rescaled_feature.reshape(1,-1))
is showing as error.For your clarity globe.py is also attached.
Please let me know how to fix this.
Regards