Question

您好在此代码中我无法在目录/ datasets / training中加载我自己的图像，但我可以加载/数据集/动物。那是为什么？

我使用操作系统Windows，有人告诉我路径不正确，但我似乎不明白，我在黑暗中需要一些帮助，在此先感谢！

from sklearn.neighbors import KNeighborsClassifier
from sklearn.cross_validation import train_test_split
from imutils import paths
import numpy as np
import argparse
import imutils
import cv2
import os

def image_to_feature_vector(image, size=(32, 32)):
# resize the image to a fixed size, then flatten the image into
# a list of raw pixel intensities
    return cv2.resize(image, size).flatten()
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()

ap = argparse.ArgumentParser()
ap.add_argument("-d", "--dataset", required=True,
            help="path to input dataset")
ap.add_argument("-k", "--neighbors", type=int, default=1,
            help="# of nearest neighbors for classification")
ap.add_argument("-j", "--jobs", type=int, default=-1,
            help="# of jobs for k-NN distance (-1 uses all available cores)")
args = vars(ap.parse_args())
print("[INFO] describing images...")
imagePaths = list(paths.list_images(args["dataset"]))

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]

pixels = image_to_feature_vector(image)
hist = extract_color_histogram(image)

# update the raw images, features, and labels matricies,
# respectively
rawImages.append(pixels)
features.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)))

# show some information on the memory consumed by the raw images
# matrix and features matrix
rawImages = np.array(rawImages)
features = np.array(features)
labels = np.array(labels)
print("[INFO] pixels matrix: {:.2f}MB".format(
rawImages.nbytes / (1024 * 1000.0)))
print("[INFO] features matrix: {:.2f}MB".format(
features.nbytes / (1024 * 1000.0)))

# partition the data into training and testing splits, using 75%
# of the data for training and the remaining 25% for testing
(trainRI, testRI, trainRL, testRL) = train_test_split(
rawImages, labels, test_size=0.25, random_state=42)
(trainFeat, testFeat, trainLabels, testLabels) = train_test_split(
features, labels, test_size=0.25, random_state=42)

# train and evaluate a k-NN classifer on the raw pixel intensities
print("[INFO] evaluating raw pixel accuracy...")
model = KNeighborsClassifier(n_neighbors=args["neighbors"],
                         n_jobs=args["jobs"])
model.fit(trainRI, trainRL)
acc = model.score(testRI, testRL)
print("[INFO] raw pixel accuracy: {:.2f}%".format(acc * 100))

# train and evaluate a k-NN classifer on the histogram
# representations
print("[INFO] evaluating histogram accuracy...")
model = KNeighborsClassifier(n_neighbors=args["neighbors"],
                         n_jobs=args["jobs"])
model.fit(trainFeat, trainLabels)
acc = model.score(testFeat, testLabels)
print("[INFO] histogram accuracy: {:.2f}%".format(acc * 100))

我真的希望有人能说清楚。感谢。

场景分类，无法在我的图像处理中加载图像

0 个答案: