def Recognize(self):
multi = MLP()
multi.read_xml("MLP.xml")
original_image = cv2.imread(self.original_path)
output_image = cv2.imread(self.original_path)
getimages = Segmentation()
Coordinates_images = getimages.Segment(self.segmented_path)
for coordinate in Coordinates_images:
crop_img = original_image[coordinate.y:coordinate.y + coordinate.h,
coordinate.x:coordinate.x + coordinate.w]
image = cv2.resize(crop_img, (50, 50))
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = np.reshape(gray, 2500)
feautures = generante_features_sample(gray, pca_data.totalimages,
pca_data.finalEigenVectors)
type = multi.determine_class(np.asarray(feautures)) - 1
output_image = add_rectangle(output_image, coordinate.x,
coordinate.y,
coordinate.x + coordinate.w,
coordinate.y + coordinate.h, type)
cv2.imshow('IMAGE', output_image)
cv2.waitKey(400000)
def Recognize(self):
original_image = cv2.imread(self.original_path)
output_image = cv2.imread(self.original_path)
getimages = Segmentation()
Coordinates_images = getimages.Segment(self.segmented_path)
r = read_rbf_data()
for coordinate in Coordinates_images:
crop_img = original_image[coordinate.y:coordinate.y + coordinate.h,
coordinate.x:coordinate.x + coordinate.w]
image = cv2.resize(crop_img, (50, 50))
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = np.reshape(gray, 2500)
feautures = generante_features_sample(gray, pca_data.totalimages,
pca_data.finalEigenVectors)
type = classify_from_file(feautures, r.k, r.num_classes,
r.avg_list, r.mx_list, r.mn_list,
r.centers, r.weights)
output_image = add_rectangle(output_image, coordinate.x,
coordinate.y,
coordinate.x + coordinate.w,
coordinate.y + coordinate.h, type)
cv2.imshow('IMAGE', output_image)
cv2.waitKey(400000)
