def do_classify(
img, # image matrix
train_data_file_name # image matrix
):
# read the test image
source_image = cv2.imread(img)
prediction = 'n.a.'
# checking whether the training data is ready
PATH = './training.data'
PATH = train_data_file_name
if os.path.isfile(PATH) and os.access(PATH, os.R_OK):
# print('training data is ready, classifier is loading...')
print("")
else:
print('training data is being created...')
open('training.data', 'w')
color_histogram_feature_extraction.training()
print('training data is ready, classifier is loading...')
# get the prediction
color_histogram_feature_extraction.color_histogram_of_test_image(
source_image)
prediction = knn_classifier.main(train_data_file_name, 'test.data')
print('training data is over,result', train_data_file_name, prediction)
def __init__(self):
print("Initializing ....")
if os.path.isfile(PATH) and os.access(PATH, os.R_OK):
print('training data is ready, classifier is loading...')
else:
print('training data is being created...')
open('training.data', 'w')
color_histogram_feature_extraction.training()
print('training data is ready, classifier is loading...')
def training(self):
# checking whether the training data is ready
self.PATH = './training.data'
if os.path.isfile(self.PATH) and os.access(self.PATH, os.R_OK):
print('training data is ready, classifier is loading...')
else:
print('training data is being created...')
open('training.data', 'w')
color_histogram_feature_extraction.training()
print('training data is ready, classifier is loading...')
def getColor(imageinput):
source_image=cv2.imread(imageinput)
prediction = 'n.a.'
# checking whether the training data is ready
PATH = './training.data'
if os.path.isfile(PATH) and os.access(PATH, os.R_OK):
print ('training data is ready, classifier is loading...')
else:
print ('training data is being created...')
open('training.data', 'w')
color_histogram_feature_extraction.training()
# get the prediction
color_histogram_feature_extraction.color_histogram_of_test_image(source_image)
prediction = knn_classifier.main('training.data', 'test.data')
print('Detected color is:', prediction)
return prediction
def color_detect():
rospy.init_node('color_detection', anonymous=True)
color_pub = rospy.Publisher('detect_color', String, queue_size=10)
r = rospy.Rate(10)
cap = cv2.VideoCapture(0)
(ret, frame) = cap.read()
prediction = 'n.a.'
PATH = './training.data'
if os.path.isfile(PATH) and os.access(PATH, os.R_OK):
print('training data is ready, classifier is loading...')
else:
print('training data is being created...')
open('training.data', 'w')
color_histogram_feature_extraction.training()
print('training data is ready, classifier is loading...')
old_predict = prediction
count = 0
while not rospy.is_shutdown():
(ret, frame) = cap.read() # record
cv2.putText(frame, 'Prediction: ' + prediction, (15, 45),
cv2.FONT_HERSHEY_PLAIN, 4, 300)
# Display the resulting frame
cv2.imshow('color classifier', frame)
color_histogram_feature_extraction.color_histogram_of_test_image(frame)
prediction = knn_classifier.main('training.data', 'test.data')
if cv2.waitKey(1) & 0xFF == ord('q'):
break
if old_predict == prediction:
count += 1
print(">> " + prediction)
else:
count = 0
old_predict = prediction
if count > 20:
color_pub.publish(prediction)
r.sleep()
cap.release()
cv2.destroyAllWindows()
import os
import os.path
# read the test image
source_image = cv2.imread('black_cat.jpg')
prediction = 'n.a.'
# checking whether the training data is ready
PATH = './training.data'
if os.path.isfile(PATH) and os.access(PATH, os.R_OK):
print ('training data is ready, classifier is loading...')
else:
print ('training data is being created...')
open('training.data', 'w')
color_histogram_feature_extraction.training()
print ('training data is ready, classifier is loading...')
# get the prediction
color_histogram_feature_extraction.color_histogram_of_test_image(source_image)
prediction = knn_classifier.main('training.data', 'test.data')
cv2.putText(
source_image,
'Prediction: ' + prediction,
(15, 45),
cv2.FONT_HERSHEY_PLAIN,
3,
200,
)
# Display the resulting frame
