lbp = LocalBinaryPatterns(24, 8)
data = []
labels = []
#Finding all images
training = [
os.path.join(root, name)
for root, dirs, files in os.walk("../training_images") for name in files
if name.endswith((".jpeg", ".jpg"))
]
#Training Phase
for imagePath in training:
#Load the image, convert it to grayscale, and compute LBP
image = cv2.imread(imagePath)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
hist = lbp.compute(gray)
#Plotting histogram if needed
#plt.bar(bin_edges[:-1], hist, width = 1)
#plt.xlim(min(bin_edges), max(bin_edges))
#plt.show()
#Extract the label from the image path, then update the label and data lists
labels.append(imagePath.split("/")[-2])
data.append(hist)
#Train classifier
classifier = Classifier("SVM")
classifier.train(data, labels)
#Testing Phase
def run_iteration(iteration, hash_map):
lbp = LocalBinaryPatterns(24, 8)
data = []
labels = []
#Finding all images
images = [os.path.join(root, name) for root, dirs, files in os.walk("../training_images")
for name in files if name.endswith((".jpeg", ".jpg"))]
#Spliting it into training and testing groups
training, testing = train_test_split(images, test_size = 0.25)
#Training Phase
for imagePath in training:
#Load the image, convert it to grayscale, and compute LBP
image = cv2.imread(imagePath)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
if imagePath in hash_map:
hist = hash_map[imagePath]
else:
hist = lbp.compute(gray)
hash_map[imagePath] = hist
print str(iteration) + " DEBUG(Training): Computed LBP Histogram for " + imagePath
#Plotting histogram if needed
#plt.bar(bin_edges[:-1], hist, width = 1)
#plt.xlim(min(bin_edges), max(bin_edges))
#plt.show()
#Extract the label from the image path, then update the label and data lists
labels.append(imagePath.split("/")[-2])
data.append(hist)
#Train classifier
classifier = Classifier("SVM")
print "\n\n" + str(iteration) + " DEBUG: Training Classifier"
classifier.train(data, labels)
print "\n\n" + str(iteration) + " DEBUG: Trained Classifier\n\n"
#Testing Phase
data = []
labels = []
for imagePath in testing:
#Load the image, convert to grayscale, describe it and classify it
image = cv2.imread(imagePath)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
if imagePath in hash_map:
hist = hash_map[imagePath]
else:
hist = lbp.compute(gray)
hash_map[imagePath] = hist
print str(iteration) + " DEBUG(Testing): Computed LBP Histogram for " + imagePath
data.append(hist)
labels.append(imagePath.split("/")[-2])
print "\n\n" + str(iteration) + " DEBUG: Forming predictions"
predictions = classifier.predict(data)
counter = 0
print "\n\n" + str(iteration) + " DEBUG: Printing predictions\n\n"
for index, prediction in enumerate(predictions):
print "Name -> " + testing[index] + " Actual -> " + labels[index] + " Prediction -> " + prediction
if labels[index] == prediction:
counter = counter + 1
accuracy = (float(counter)/float(len(predictions))) * 100.0
print "\n\n" + str(iteration) + " The Classifier Accuracy was " + str(accuracy) + "%"
return accuracy
#Caputuring the Image
#---To be done---
lbp = LocalBinaryPatterns(24, 8)
data = []
labels = []
#Finding all images
training = [os.path.join(root, name) for root, dirs, files in os.walk("../training_images")
for name in files if name.endswith((".jpeg", ".jpg"))]
#Training Phase
for imagePath in training:
#Load the image, convert it to grayscale, and compute LBP
image = cv2.imread(imagePath)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
hist = lbp.compute(gray)
#Extract the label from the image path, then update the label and data lists
labels.append(imagePath.split("/")[-2])
data.append(hist)
#Train classifier
classifier = Classifier("Chi-Squared")
classifier.train(data, labels)
#Testing Phase
data = []
testing = [os.path.join(root, name) for root, dirs, files in os.walk("../testing_images")
for name in files if name.endswith((".jpeg", ".jpg"))]