ich bekomme für folgende Zeile immer einen SyntaxError
Code: Alles auswählen
le = LabelEncoder()Anbei noch den kompletten Code
Code: Alles auswählen
# import the necessary packages
from sklearn.preprocessing import LabelEncoder
from sklearn.svm import LinearSVC
from sklearn.metrics import classification_report
#from sklearn.cross_validation import train_test_split
from sklearn.model_selection import train_test_split
from imutils import paths #a set of image processing convenience functions
import numpy as np
import argparse
import imutils
import cv2
import os
def extract_color_histogram(image, bins=(8, 8, 8)):
# extract a 3D color histogram from the HSV color space using
# the supplied number of `bins` per channel
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
hist = cv2.calcHist([hsv], [0, 1, 2], None, bins,
[0, 180, 0, 256, 0, 256])
# handle normalizing the histogram if we are using OpenCV 2.4.X
if imutils.is_cv2():
hist = cv2.normalize(hist)
# otherwise, perform "in place" normalization in OpenCV 3 (I
# personally hate the way this is done
else:
cv2.normalize(hist, hist)
# return the flattened histogram as the feature vector
return hist.flatten()
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-d", "--dataset", required=True,
help="path to input dataset")
args = vars(ap.parse_args())
# grab the list of images that we'll be describing
print("[INFO] describing images...")
imagePaths = list(paths.list_images(args["dataset"]))
# initialize the data matrix and labels list
data = []
labels = []
# loop over the input images
for (i, imagePath) in enumerate(imagePaths):
# load the image and extract the class label (assuming that our
# path as the format: /path/to/dataset/{class}.{image_num}.jpg
image = cv2.imread(imagePath)
label = imagePath.split(os.path.sep)[-1].split(".")[0]
# extract a color histogram from the image, then update the
# data matrix and labels list
hist = extract_color_histogram(image)
data.append(hist)
labels.append(label)
# show an update every 1,000 images
if i > 0 and i % 1000 == 0:
print("[INFO] processed {}/{}".format(i, len(imagePaths))
# encode the labels, converting them from strings to integers
le = LabelEncoder()
labels = le.fit_transform(labels)
# partition the data into training and testing splits, using 75%
# of the data for training and the remaining 25% for testing
print("[INFO] constructing training/testing split...")
(trainData, testData, trainLabels, testLabels) = train_test_split(
np.array(data), labels, test_size=0.25, random_state=42)
# train the linear regression clasifier
print("[INFO] training Linear SVM classifier...")
model = LinearSVC()
model.fit(trainData, trainLabels)
# evaluate the classifier
print("[INFO] evaluating classifier...")
predictions = model.predict(testData)
print(classification_report(testLabels, predictions,
target_names=le.classes_))
