使用Python检测照片中的特定水印(无SciPy)

Question

我有大量的图像(数十万),对于每一个,我需要说明它的右上角是否有水印.水印总是相同的并且处于相同的位置.它采用带有符号和一些文本的功能区的形式.我正在寻找简单快速的方法来做到这一点,理想情况下,不使用SciPy(因为它在我正在使用的服务器上不可用 - 但它可以使用NumPy)

到目前为止,我已经尝试使用PIL和裁剪功能来隔离图像中应该有水印的区域,然后将直方图与RMS函数进行比较(请参阅http://snipplr.com/view/757/compare-两个pil-images-in-python /).由于两个方向都存在很多错误,因此效果不佳.

任何想法将不胜感激.谢谢

Answer 1

另一种可能性是使用机器学习.我的背景是自然语言处理(不是计算机视觉),但我尝试使用你的问题的描述创建一个训练和测试集,它似乎工作(对看不见的数据100%准确).

训练集

训练集由具有水印的相同图像(正例)和没有水印(负例)组成.

测试集

测试集由不在训练集中的图像组成.

示例数据

如果有兴趣,您可以尝试使用示例培训和测试图像.

码:

完整版可作为要点.摘录如下:

import glob

from classify import MultinomialNB
from PIL import Image


TRAINING_POSITIVE = 'training-positive/*.jpg'
TRAINING_NEGATIVE = 'training-negative/*.jpg'
TEST_POSITIVE = 'test-positive/*.jpg'
TEST_NEGATIVE = 'test-negative/*.jpg'

# How many pixels to grab from the top-right of image.
CROP_WIDTH, CROP_HEIGHT = 100, 100
RESIZED = (16, 16)


def get_image_data(infile):
    image = Image.open(infile)
    width, height = image.size
    # left upper right lower
    box = width - CROP_WIDTH, 0, width, CROP_HEIGHT
    region = image.crop(box)
    resized = region.resize(RESIZED)
    data = resized.getdata()
    # Convert RGB to simple averaged value.
    data = [sum(pixel) / 3 for pixel in data]
    # Combine location and value.
    values = []
    for location, value in enumerate(data):
        values.extend([location] * value)
    return values


def main():
    watermark = MultinomialNB()
    # Training
    count = 0
    for infile in glob.glob(TRAINING_POSITIVE):
        data = get_image_data(infile)
        watermark.train((data, 'positive'))
        count += 1
        print 'Training', count
    for infile in glob.glob(TRAINING_NEGATIVE):
        data = get_image_data(infile)
        watermark.train((data, 'negative'))
        count += 1
        print 'Training', count
    # Testing
    correct, total = 0, 0
    for infile in glob.glob(TEST_POSITIVE):
        data = get_image_data(infile)
        prediction = watermark.classify(data)
        if prediction.label == 'positive':
            correct += 1
        total += 1
        print 'Testing ({0} / {1})'.format(correct, total)
    for infile in glob.glob(TEST_NEGATIVE):
        data = get_image_data(infile)
        prediction = watermark.classify(data)
        if prediction.label == 'negative':
            correct += 1
        total += 1
        print 'Testing ({0} / {1})'.format(correct, total)
    print 'Got', correct, 'out of', total, 'correct'


if __name__ == '__main__':
    main()

示例输出

Training 1
Training 2
Training 3
Training 4
Training 5
Training 6
Training 7
Training 8
Training 9
Training 10
Training 11
Training 12
Training 13
Training 14
Testing (1 / 1)
Testing (2 / 2)
Testing (3 / 3)
Testing (4 / 4)
Testing (5 / 5)
Testing (6 / 6)
Testing (7 / 7)
Testing (8 / 8)
Testing (9 / 9)
Testing (10 / 10)
Got 10 out of 10 correct
[Finished in 3.5s]