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)