def compare_similarity_hist_with_net_imgs(img1_url, img2_url):
"""
比较两张网络图片的相似度
"""
h1 = calc_bgr_hist(read_img_from_net(img1_url))
h2 = calc_bgr_hist(read_img_from_net(img2_url))
return compare_similar_hist(h1, h2)
def get_hist(img_url):
"""
计算网络图片直方图数值
Args:
url: 图片URL地址
Returns:
dict类型直方图数值，转为json格式字符串返回
"""
img = read_img_from_net(img_url)
return calc_bgr_hist(img)
def feature_similarity(img1_url, img2_url, type='o'):
"""
图片解析为hash值
Args:
img1_url: 第一张图片URL地址
img2_url: 第二张图片URL地址
type: 算法类型，'o' -> ORB
Returns:
{
"similarity": "xxx"
}
"""
img1 = read_img_from_net(img1_url, cv2.IMREAD_GRAYSCALE)
img2 = read_img_from_net(img2_url, cv2.IMREAD_GRAYSCALE)
similarity = 0
if type == 'o':
similarity = orb_similarity(img1, img2)
print(similarity)
return similarity
def img_to_hashes(url, p=False, d=False, a=False, w=False):
"""
图片解析为hash值
Args:
url: 图片URL地址
p: 是否计算pHash值
d: 是否计算dHash值
a: 是否计算aHash值
Returns:
{
"phash": "xxx",
"dhash": "xxx",
"ahash": "xxx",
"whash": "xxx"
}
"""
data = {}
netImg = read_img_from_net(url)
try:
# 分表选择90°、180°、270°
netImgX = img_as_ubyte(transform.rotate(netImg, 90, resize=True))
netImgY = img_as_ubyte(transform.rotate(netImg, 180, resize=True))
netImgZ = img_as_ubyte(transform.rotate(netImg, 270, resize=True))
# 水平翻转
h_flip = cv2.flip(netImg, 1)
# 垂直翻转
v_flip = cv2.flip(netImg, 0)
if p:
data['p'] = {}
data['p']['o'] = pHash(netImg)
data['p']['x'] = pHash(netImgX)
data['p']['y'] = pHash(netImgY)
data['p']['z'] = pHash(netImgZ)
data['p']['h'] = pHash(h_flip)
data['p']['v'] = pHash(v_flip)
if d:
data['d'] = {}
data['d']['o'] = dHash(netImg)
data['d']['x'] = dHash(netImgX)
data['d']['y'] = dHash(netImgY)
data['d']['z'] = dHash(netImgZ)
data['d']['h'] = pHash(h_flip)
data['d']['v'] = pHash(v_flip)
if a:
data['a'] = {}
data['a']['o'] = aHash(netImg)
data['a']['x'] = aHash(netImgX)
data['a']['y'] = aHash(netImgY)
data['a']['z'] = aHash(netImgZ)
data['a']['h'] = pHash(h_flip)
data['a']['v'] = pHash(v_flip)
if w:
data['w'] = {}
data['w']['0'] = wHash(netImg)
data['w']['x'] = wHash(netImgX)
data['w']['y'] = wHash(netImgY)
data['w']['z'] = wHash(netImgZ)
data['w']['h'] = pHash(h_flip)
data['w']['v'] = pHash(v_flip)
except Exception as e:
logger.info(e)
return data
def three_sim(img1_url, img2_url):
return classify_hist_with_split(read_img_from_net(img1_url),
read_img_from_net(img2_url))
def single_sim(img1_url, img2_url):
return calculate(read_img_from_net(img1_url), read_img_from_net(img2_url))