如何检测图像上的物体？

Question

我需要python解决方案.

我有40-60张图片(Happy Holiday set).我需要检测所有这些图像上的对象.

我不知道对象大小,形式,图像上的位置,我没有任何对象模板.我只知道一件事:这个物体几乎存在于所有图像中.我叫它不明飞行物.

例:

如示例所示,从图像到图像,除了UFO之外,一切都会发生变化.检测后我需要得到:

左上角的X坐标

左上角的Y坐标

蓝色对象区域的宽度(我将示例区域标记为红色矩形)

蓝色物体区域的高度

Answer 1

当您将图像数据作为数组时,您可以使用内置的numpy函数轻松快速地执行此操作:

import numpy as np
import PIL

image = PIL.Image.open("14767594_in.png")

image_data = np.asarray(image)
image_data_blue = image_data[:,:,2]

median_blue = np.median(image_data_blue)

non_empty_columns = np.where(image_data_blue.max(axis=0)>median_blue)[0]
non_empty_rows = np.where(image_data_blue.max(axis=1)>median_blue)[0]

boundingBox = (min(non_empty_rows), max(non_empty_rows), min(non_empty_columns), max(non_empty_columns))

print boundingBox

会给你,第一张图片:

(78, 156, 27, 166)

所以你想要的数据是:

• 左上角是(x,y): (27, 78)
• 宽度: 166 - 27 = 139
• 高度: 156 - 78 = 78

我选择"每个像素的蓝色值都大于所有蓝色值的中位数"属于你的对象.我希望这对你有用; 如果没有,尝试别的或提供一些不起作用的例子.

编辑 我将我的代码重新编写为更通用.由于两个具有相同形状颜色的图像不够通用(正如您的注释所示),我会综合创建更多样本.

def create_sample_set(mask, N=36, shape_color=[0,0,1.,1.]):
    rv = np.ones((N, mask.shape[0], mask.shape[1], 4),dtype=np.float)
    mask = mask.astype(bool)
    for i in range(N):
        for j in range(3):
            current_color_layer = rv[i,:,:,j]
            current_color_layer[:,:] *= np.random.random()
            current_color_layer[mask] = np.ones((mask.sum())) * shape_color[j]
    return rv

这里,形状的颜色是可调节的.对于N = 26个图像中的每一个,选择随机背景颜色.也可以将噪声放在背景中,这不会改变结果.

然后,我读取您的样本图像,从中创建一个形状蒙版并使用它来创建样本图像.我将它们绘制在网格上.

# create set of sample image and plot them
image = PIL.Image.open("14767594_in.png")
image_data = np.asarray(image)
image_data_blue = image_data[:,:,2]
median_blue = np.median(image_data_blue)
sample_images = create_sample_set(image_data_blue>median_blue)
plt.figure(1)
for i in range(36):
    plt.subplot(6,6,i+1)
    plt.imshow(sample_images[i,...])
    plt.axis("off")
plt.subplots_adjust(0,0,1,1,0,0)

对于shape_color(参数to create_sample_set(...))的另一个值,这可能如下所示:

接下来,我将确定标准差的每像素可变性.正如您所说,对象在(几乎)所有图像处于相同位置.因此,这些图像中的可变性将很低,而对于其他像素,它将显着更高.

# determine per-pixel variablility, std() over all images
variability = sample_images.std(axis=0).sum(axis=2)

# show image of these variabilities
plt.figure(2)
plt.imshow(variability, cmap=plt.cm.gray, interpolation="nearest", origin="lower")

最后,就像在我的第一个代码片段中一样,确定边界框.现在我也提供了它的情节.

# determine bounding box
mean_variability = variability.mean()
non_empty_columns = np.where(variability.min(axis=0)<mean_variability)[0]
non_empty_rows = np.where(variability.min(axis=1)<mean_variability)[0]
boundingBox = (min(non_empty_rows), max(non_empty_rows), min(non_empty_columns), max(non_empty_columns))

# plot and print boundingBox
bb = boundingBox
plt.plot([bb[2], bb[3], bb[3], bb[2], bb[2]],
         [bb[0], bb[0],bb[1], bb[1], bb[0]],
         "r-")
plt.xlim(0,variability.shape[1])
plt.ylim(variability.shape[0],0)

print boundingBox
plt.show()

而已.我希望这次足够普遍.

完整的复制和粘贴脚本:

import numpy as np
import PIL
import matplotlib.pyplot as plt


def create_sample_set(mask, N=36, shape_color=[0,0,1.,1.]):
    rv = np.ones((N, mask.shape[0], mask.shape[1], 4),dtype=np.float)
    mask = mask.astype(bool)
    for i in range(N):
        for j in range(3):
            current_color_layer = rv[i,:,:,j]
            current_color_layer[:,:] *= np.random.random()
            current_color_layer[mask] = np.ones((mask.sum())) * shape_color[j]
    return rv

# create set of sample image and plot them
image = PIL.Image.open("14767594_in.png")
image_data = np.asarray(image)
image_data_blue = image_data[:,:,2]
median_blue = np.median(image_data_blue)
sample_images = create_sample_set(image_data_blue>median_blue)
plt.figure(1)
for i in range(36):
    plt.subplot(6,6,i+1)
    plt.imshow(sample_images[i,...])
    plt.axis("off")
plt.subplots_adjust(0,0,1,1,0,0)

# determine per-pixel variablility, std() over all images
variability = sample_images.std(axis=0).sum(axis=2)

# show image of these variabilities
plt.figure(2)
plt.imshow(variability, cmap=plt.cm.gray, interpolation="nearest", origin="lower")

# determine bounding box
mean_variability = variability.mean()
non_empty_columns = np.where(variability.min(axis=0)<mean_variability)[0]
non_empty_rows = np.where(variability.min(axis=1)<mean_variability)[0]
boundingBox = (min(non_empty_rows), max(non_empty_rows), min(non_empty_columns), max(non_empty_columns))

# plot and print boundingBox
bb = boundingBox
plt.plot([bb[2], bb[3], bb[3], bb[2], bb[2]],
         [bb[0], bb[0],bb[1], bb[1], bb[0]],
         "r-")
plt.xlim(0,variability.shape[1])
plt.ylim(variability.shape[0],0)

print boundingBox
plt.show()

Answer 2

我创建了第二个答案,而不是更多地扩展我的第一个答案.我使用相同的方法,但在你的新例子.唯一的区别是:我使用一组固定阈值而不是自动确定它.如果你可以玩它,这应该足够了.

import numpy as np
import PIL
import matplotlib.pyplot as plt
import glob

filenames = glob.glob("14767594/*.jpg")
images = [np.asarray(PIL.Image.open(fn)) for fn in filenames]
sample_images = np.concatenate([image.reshape(1,image.shape[0], image.shape[1],image.shape[2]) 
                            for image in images], axis=0)

plt.figure(1)
for i in range(sample_images.shape[0]):
    plt.subplot(2,2,i+1)
    plt.imshow(sample_images[i,...])
    plt.axis("off")
plt.subplots_adjust(0,0,1,1,0,0)

# determine per-pixel variablility, std() over all images
variability = sample_images.std(axis=0).sum(axis=2)

# show image of these variabilities
plt.figure(2)
plt.imshow(variability, cmap=plt.cm.gray, interpolation="nearest", origin="lower")

# determine bounding box
thresholds = [5,10,20]
colors = ["r","b","g"]
for threshold, color in zip(thresholds, colors): #variability.mean()
    non_empty_columns = np.where(variability.min(axis=0)<threshold)[0]
    non_empty_rows = np.where(variability.min(axis=1)<threshold)[0]
    boundingBox = (min(non_empty_rows), max(non_empty_rows), min(non_empty_columns), max(non_empty_columns))

    # plot and print boundingBox
    bb = boundingBox
    plt.plot([bb[2], bb[3], bb[3], bb[2], bb[2]],
             [bb[0], bb[0],bb[1], bb[1], bb[0]],
             "%s-"%![enter image description here][1]color, 
             label="threshold %s" % threshold)
    print boundingBox

plt.xlim(0,variability.shape[1])
plt.ylim(variability.shape[0],0)
plt.legend()

plt.show()

制作的地块:

您的要求与认知神经科学中的ERP密切相关.您拥有的输入图像越多,随着信噪比的增加,此方法的效果就越好.