这是我想做的:
我定期用网络摄像头拍照。就像时间流逝一样。然而,如果没有什么真正的改变,也就是说,图片看起来几乎相同,我不想存储最新的快照。
我想有某种方法可以量化这种差异,我必须根据经验确定一个阈值。
我追求的是简单而不是完美。 我用的是python。
这是我想做的:
我定期用网络摄像头拍照。就像时间流逝一样。然而,如果没有什么真正的改变,也就是说,图片看起来几乎相同,我不想存储最新的快照。
我想有某种方法可以量化这种差异,我必须根据经验确定一个阈值。
我追求的是简单而不是完美。 我用的是python。
当前回答
通过计算均方误差,numpy有一个简单而快速的解决方案:
before = np.array(get_picture())
while True:
now = np.array(get_picture())
MSE = np.mean((now - before)**2)
if MSE > threshold:
break
before = now
其他回答
通过计算均方误差,numpy有一个简单而快速的解决方案:
before = np.array(get_picture())
while True:
now = np.array(get_picture())
MSE = np.mean((now - before)**2)
if MSE > threshold:
break
before = now
给出的大多数答案都不涉及照明水平。
在进行比较之前,我首先将图像归一化到标准的光照水平。
另一个衡量两张图片相似度的好方法是:
import sys
from skimage.measure import compare_ssim
from skimage.transform import resize
from scipy.ndimage import imread
# get two images - resize both to 1024 x 1024
img_a = resize(imread(sys.argv[1]), (2**10, 2**10))
img_b = resize(imread(sys.argv[2]), (2**10, 2**10))
# score: {-1:1} measure of the structural similarity between the images
score, diff = compare_ssim(img_a, img_b, full=True)
print(score)
如果其他人对更强大的比较图像相似性的方法感兴趣,我将使用Tensorflow测量和可视化相似图像的教程和web应用程序放在一起。
import os
from PIL import Image
from PIL import ImageFile
import imagehash
#just use to the size diferent picture
def compare_image(img_file1, img_file2):
if img_file1 == img_file2:
return True
fp1 = open(img_file1, 'rb')
fp2 = open(img_file2, 'rb')
img1 = Image.open(fp1)
img2 = Image.open(fp2)
ImageFile.LOAD_TRUNCATED_IMAGES = True
b = img1 == img2
fp1.close()
fp2.close()
return b
#through picturu hash to compare
def get_hash_dict(dir):
hash_dict = {}
image_quantity = 0
for _, _, files in os.walk(dir):
for i, fileName in enumerate(files):
with open(dir + fileName, 'rb') as fp:
hash_dict[dir + fileName] = imagehash.average_hash(Image.open(fp))
image_quantity += 1
return hash_dict, image_quantity
def compare_image_with_hash(image_file_name_1, image_file_name_2, max_dif=0):
"""
max_dif: The maximum hash difference is allowed, the smaller and more accurate, the minimum is 0.
recommend to use
"""
ImageFile.LOAD_TRUNCATED_IMAGES = True
hash_1 = None
hash_2 = None
with open(image_file_name_1, 'rb') as fp:
hash_1 = imagehash.average_hash(Image.open(fp))
with open(image_file_name_2, 'rb') as fp:
hash_2 = imagehash.average_hash(Image.open(fp))
dif = hash_1 - hash_2
if dif < 0:
dif = -dif
if dif <= max_dif:
return True
else:
return False
def compare_image_dir_with_hash(dir_1, dir_2, max_dif=0):
"""
max_dif: The maximum hash difference is allowed, the smaller and more accurate, the minimum is 0.
"""
ImageFile.LOAD_TRUNCATED_IMAGES = True
hash_dict_1, image_quantity_1 = get_hash_dict(dir_1)
hash_dict_2, image_quantity_2 = get_hash_dict(dir_2)
if image_quantity_1 > image_quantity_2:
tmp = image_quantity_1
image_quantity_1 = image_quantity_2
image_quantity_2 = tmp
tmp = hash_dict_1
hash_dict_1 = hash_dict_2
hash_dict_2 = tmp
result_dict = {}
for k in hash_dict_1.keys():
result_dict[k] = None
for dif_i in range(0, max_dif + 1):
have_none = False
for k_1 in result_dict.keys():
if result_dict.get(k_1) is None:
have_none = True
if not have_none:
return result_dict
for k_1, v_1 in hash_dict_1.items():
for k_2, v_2 in hash_dict_2.items():
sub = (v_1 - v_2)
if sub < 0:
sub = -sub
if sub == dif_i and result_dict.get(k_1) is None:
result_dict[k_1] = k_2
break
return result_dict
def main():
print(compare_image('image1\\815.jpg', 'image2\\5.jpg'))
print(compare_image_with_hash('image1\\815.jpg', 'image2\\5.jpg', 7))
r = compare_image_dir_with_hash('image1\\', 'image2\\', 10)
for k in r.keys():
print(k, r.get(k))
if __name__ == '__main__':
main()
输出: 假 真正的 image2 jpg image1 5. \ \ 815. jpg image2 jpg image1 6. \ \ 819. jpg image2 jpg image1 7. \ \ 900. jpg image2 jpg image1 8. \ \ 998. jpg image2 jpg image1 9. \ \ 1012. jpg 示例图片: 815. jpg 5. jpg
推土机的距离可能正是你所需要的。 不过,要实时实现它可能有点重。