def is_similar(image1, image2):
"Compares two images"
logger.debug("First image: %s, Second image: %s", image1, image2)
image1 = cv2.imread(image1)
image2 = cv2.imread(image2)
first_image_hist = cv2.calcHist([image1], [0], None, [256], [0, 256])
second_image_hist = cv2.calcHist([image2], [0], None, [256], [0, 256])
# img_hist_diff = cv2.compareHist(
# first_image_hist, second_image_hist,
# cv2.HISTCMP_BHATTACHARYYA
# )
img_template_probability_match = cv2.matchTemplate(
first_image_hist, second_image_hist, cv2.TM_CCOEFF_NORMED)[0][0]
img_template_probability_match *= 100
similar = bool(img_template_probability_match >= TRESHOLD_PERCANTAGE)
log_level = logging.WARNING if similar else logging.INFO
logger.log(log_level, "Similarity: %s",
str(img_template_probability_match))
return similar
def on_mouse(event, x, y, flags, img):
global point1, point2
img2 = img.copy()
if event == cv2.EVENT_LBUTTONDOWN: # 左键点击
point1 = (x, y)
cv2.circle(img2, point1, 10, (0, 255, 0), 5)
cv2.imshow('image', img2)
# 按住左键拖曳
elif event == cv2.EVENT_MOUSEMOVE and (flags & cv2.EVENT_FLAG_LBUTTON):
cv2.rectangle(img2, point1, (x, y), (255, 0, 0), 5)
cv2.imshow('image', img2)
elif event == cv2.EVENT_LBUTTONUP: # 左键释放
point2 = (x, y)
cv2.rectangle(img2, point1, point2, (0, 0, 255), 5)
cv2.imshow('image', img2)
min_x = min(point1[0], point2[0])
min_y = min(point1[1], point2[1])
width = abs(point1[0] - point2[0])
height = abs(point1[1] - point2[1])
cut_img = img[min_y:min_y+height, min_x:min_x+width]
# cv2.imwrite('500.jpg', cut_img)
# 显示RGB三个通道的图像
# 方法一：显示在一张图上
# 按R、G、B三个通道分别计算颜色直方图
b_hist = cv2.calcHist([cut_img], [0], None, [256], [0, 256])
g_hist = cv2.calcHist([cut_img], [1], None, [256], [0, 256])
r_hist = cv2.calcHist([cut_img], [2], None, [256], [0, 256])
# 显示3个通道的颜色直方图
plt.plot(b_hist, label='B', color='blue')
plt.plot(g_hist, label='G', color='green')
plt.plot(r_hist, label='R', color='red')
plt.legend(loc='best')
plt.xlim([0, 256])
plt.show()
def hist_compare(image1, image2):
size = image1.shape[:2]
image1 = cv.resize(image1, size)
image2 = cv.resize(image2, size)
image1 = cv.cvtColor(image1, cv.COLOR_BGR2GRAY)
image2 = cv.cvtColor(image2, cv.COLOR_BGR2GRAY)
# image1 = cv.equalizeHist(image1)
# image2 = cv.equalizeHist(image2)
hist1 = cv.calcHist([image1], [0], None, [256], [0, 256])
hist2 = cv.calcHist([image2], [0], None, [256], [0, 256])
match1 = cv.compareHist(hist1, hist2, cv.HISTCMP_BHATTACHARYYA)
match2 = cv.compareHist(hist1, hist2, cv.HISTCMP_CORREL)
match3 = cv.compareHist(hist1, hist2, cv.HISTCMP_CHISQR)
# 巴氏距离越小越相似，相关性越大越相似，卡方越大越不相似
print("巴氏距离", match1)
print("相关性", match2)
print("卡方", match3)
# 直方图绘制
# 2-2-1
plt.subplot(221)
plt.imshow(image1, cmap=plt.cm.gray)
# 2-2-3
plt.subplot(223)
plt.imshow(image2, cmap=plt.cm.gray)
# 2-2-2
plt.subplot(222)
plt.plot(hist1) # 线
# 2-2-4
plt.subplot(224)
plt.plot(hist2) # 线
plt.show()
def _extract_rgb_histogram(image_path):
image = cv2.imread(image_path)
b_histogram = cv2.calcHist([image], [0], None, [256], [0, 256])
g_histogram = cv2.calcHist([image], [1], None, [256], [0, 256])
r_histogram = cv2.calcHist([image], [2], None, [256], [0, 256])
return np.median(r_histogram.flatten()), np.median(g_histogram.flatten()), np.median(b_histogram.flatten())
def make_fg_bg_hist_plot(fg, bg):
# make a plot comparing color histograms of foreground to background
f, axarr = plt.subplots(2, 2)
b, g, r, a = cv2.split(fg)
bData = numpy.extract(a > 0, b)
gData = numpy.extract(a > 0, g)
rData = numpy.extract(a > 0, r)
axarr[0, 0].set_title("Foreground")
axarr[0, 0].set_ylabel("Normalized # of pixels")
for chan, col in zip([rData, gData, bData], ['red', 'green', 'blue']):
hist = cv2.calcHist([chan], [0], None, [256], [0, 256])
hist /= hist.sum() # normalize to compare images of different sizes
axarr[0, 0].plot(hist, color=col)
axarr[0, 0].set_xlim([0, 256])
b, g, r, a = cv2.split(bg)
bData = numpy.extract(a > 0, b)
gData = numpy.extract(a > 0, g)
rData = numpy.extract(a > 0, r)
axarr[0, 1].set_title("Background")
for chan, col in zip([rData, gData, bData], ['red', 'green', 'blue']):
hist = cv2.calcHist([chan], [0], None, [256], [0, 256])
hist /= hist.sum() # normalize to compare images of different sizes
axarr[0, 1].plot(hist, color=col)
axarr[0, 1].set_xlim([0, 256])
axarr[1, 0].imshow(cv2.cvtColor(fg, cv2.COLOR_BGRA2RGBA))
axarr[1, 1].imshow(cv2.cvtColor(bg, cv2.COLOR_BGRA2RGBA))
plt.show()
def main_color_moment(self,img,see_make=False)->list:
xmin,xmax,ymin,ymax,mat=self.mini_img(img)
img=img[ymin:ymax,xmin:xmax]
img1=img.copy()
img1=cv.cvtColor(img,cv.COLOR_BGR2HSV)
img1[mat!=255]=[0,0,0]
# B,G,R=cv.split(img1)
# #hhh=img.copy()
# R[R!=0]=0
# G[G!=0]=0
# hhh=cv.merge([B,G,R])
if see_make:
cv.imshow('rrr',mat)
cv.imshow('ooo',img)
cv.imshow('ppp',img1)
#cv.imshow('yyy',hhh)
#cv.imshow('uuu',mmm)
cv.waitKey(0)
cv.destroyAllWindows()
hist1 = cv.calcHist([img1],[0], None, [15], [1.0,255.0])
#hist2 = cv.calcHist([img1],[1], None, [3], [1.0,255.0])
hist3 = cv.calcHist([img1],[2], None, [5], [1.0,255.0])
#print(hist6)
hist1=hist1/np.sum(hist1)
#hist2=hist2/np.sum(hist2)
hist3=hist3/np.sum(hist3)
hist=np.concatenate((hist1,hist3),axis=0)
return hist,mat,(xmin,xmax,ymin,ymax)
def _extract_hsv_histogram(image_path):
image = cv2.imread(image_path)
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
h_histogram = cv2.calcHist([hsv], [0], None, [256], [0, 256])
s_histogram = cv2.calcHist([hsv], [1], None, [256], [0, 256])
v_histogram = cv2.calcHist([hsv], [2], None, [256], [0, 256])
return np.median(h_histogram.flatten()), np.median(s_histogram.flatten()), np.median(v_histogram.flatten())
def compare(img1, img2):
"""直方图比较两张图片是否相同"""
# 获取图片灰度值分布直方图
hist1 = cv2.calcHist([img1], [0], None, [256], [0, 255])
hist2 = cv2.calcHist([img2], [0], None, [256], [0, 255])
if (hist1 == hist2).all():
return True
else:
return False
def get_HSV_hist(self, img):
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
t = cv2.calcHist([hsv], [0], None, [180], [0, 180])
hist_H = t / np.sum(t)
t = cv2.calcHist([hsv], [1], None, [256], [0, 255])
hist_S = t / np.sum(t)
t = cv2.calcHist([hsv], [2], None, [256], [0, 255])
hist_V = t / np.sum(t)
return hist_H, hist_S, hist_V
def calculate(image1,image2):
hist1 = cv2.calcHist([image1],[0],None,[256],[0.0,255.0])
hist2 = cv2.calcHist([image2],[0],None,[256],[0.0,255.0])
# 计算直方图的重合度
degree = 0
for i in range(len(hist1)):
if hist1[i] != hist2[i]:
degree = degree + (1 - abs(hist1[i]-hist2[i])/max(hist1[i],hist2[i]))
else:
degree = degree + 1
degree = degree/len(hist1)
return degree
def hist_diff(img_1, img_2):
""" Calculates distance between images based on the distance between their
histograms. Example found at:
https://www.pyimagesearch.com/2014/07/14/3-ways-compare-histograms-using-opencv-python/
"""
hist_1 = cv2.calcHist([img_1], [0, 1, 2], None, [8, 8, 8],
[0, 256, 0, 256, 0, 256])
hist_1 = cv2.normalize(hist_1, hist_1).flatten()
hist_2 = cv2.calcHist([img_2], [0, 1, 2], None, [8, 8, 8],
[0, 256, 0, 256, 0, 256])
hist_2 = cv2.normalize(hist_2, hist_2).flatten()
return cv2.compareHist(hist_1, hist_2, cv2.HISTCMP_BHATTACHARYYA)
def OTSU(img, sta, fin):
hist = cv2.calcHist([img], [0], None, [fin - sta], [sta, fin])
hist_norm = hist.ravel() / hist.max()
Q = hist_norm.cumsum()
bins = np.arange(fin - sta)
fn_min = np.inf
thresh = -1
for i in range(1, fin - sta):
p1, p2 = np.hsplit(hist_norm, [i]) # probabilities
q1, q2 = Q[i], Q[fin - sta - 1] - Q[i] # cum sum of classes
b1, b2 = np.hsplit(bins, [i]) # weights
# finding means and variances
m1, m2 = np.sum(p1 * b1) / q1, np.sum(p2 * b2) / q2
v1, v2 = np.sum(((b1 - m1)**2) * p1) / q1, np.sum(
((b2 - m2)**2) * p2) / q2
# calculates the minimization function
fn = v1 * q1 + v2 * q2
if fn < fn_min:
fn_min = fn
thresh = i
thresh += (sta - 1)
return thresh
def image_hist(image): #BGR直方图
color = ('blue', 'green', 'red')
for i, color in enumerate(color):
hist = cv.calcHist([image], [i], None, [256], [0, 256])
plt.plot(hist, color=color)
plt.xlim([0, 256])
plt.show()
def DynamicHist(src,
winname,
section=[0, 256],
removeInZero=False,
displaySize=(480, 640)):
hist = cv2.calcHist([src], [0], None, [section[1] - section[0]], section)
hist = hist[:, 0]
if removeInZero:
hist[0] = 0
binWidth = int(displaySize[1] / hist.shape[0])
k = np.max(hist) / displaySize[0]
hist = hist / k
black = np.zeros((displaySize[0], hist.shape[0] * binWidth), np.uint8)
i = 0
j = 0
for x in np.nditer(hist):
j += binWidth
black[:int(x), i:j] = 200
i += binWidth
p = int(displaySize[0] / 20)
scale = np.arange(0, black.shape[1], 50 * binWidth)
black[-p:, scale] = 0XFF
black = cv2.flip(black, 0)
cv2.imshow(winname, black)
def getImgHist256Img(img):
chn = getImagChannel(img)
hist256Imgs = []
for i in range(chn):
hist = cv2.calcHist([img], [i], None, [256], [0, 256])
hist256Imgs.append(getHist256ImgFromHist(hist))
return hist256Imgs
def create_mpl_histogram_color(img, order=('r', 'g', 'b')):
"""create mpl histogram from the supplied image.
this can deal with bgr, or rgb images.
:param img: the loaded image
:type img: cv2 image
:param order: the channel ordering, defaults to ('r', 'g', 'b')
:type order: tuple, optional
:return: the computed histogram
:rtype: mpl plot
"""
plt.figure()
plt.title("'Flattened' Color Histogram")
plt.xlabel("Bins")
plt.ylabel("# of Pixels")
chans = cv2.split(img)
# loop over the image channels
for (chan, color) in zip(chans, order):
hist = cv2.calcHist([chan], [0], None, [256], [0, 256])
plt.plot(hist, color=color)
plt.xlim([0, 256])
return plt
def OTSU_Threshold(img,sta,fin):
hist = cv2.calcHist([img],[0],None,[fin-sta],[sta,fin])
hist=hist[:,0]
total = np.sum(hist)
current_max, threshold = 0, 0
sumF, sumB = 0, 0
sumT = np.inner(hist,np.arange(0,fin-sta))
weightB, weightF = 0, 0
varBetween, meanB, meanF = 0, 0, 0
for i in range(0,fin-sta):
weightB += hist[i]
weightF = total - weightB
if weightF == 0:
break
sumB += i*hist[i]
sumF = sumT - sumB
meanB = sumB/weightB
meanF = sumF/weightF
varBetween = weightB * weightF
varBetween *= (meanB-meanF)*(meanB-meanF)
if varBetween > current_max:
current_max = varBetween
threshold = i
threshold+= sta+1
return threshold
def otsu(img):
hist = cv2.calcHist([img], [0], None, [256], [0, 256])
hist_norm = hist.ravel() / hist.max()
Q = hist_norm.cumsum()
bins = np.arange(256)
fn_min = np.inf
thresh = -1
for i in range(1, 256):
p1, p2 = np.hsplit(hist_norm, [i]) # probabilities
q1, q2 = Q[i], Q[255] - Q[i] # cum sum of classes
b1, b2 = np.hsplit(bins, [i]) # weights
# finding means and variances
m1, m2 = np.sum(p1 * b1) / q1, np.sum(p2 * b2) / q2
v1, v2 = np.sum(((b1 - m1) ** 2) * p1) / q1, np.sum(((b2 - m2) ** 2) * p2) / q2
# calculates the minimization function
fn = v1 * q1 + v2 * q2
if fn < fn_min:
fn_min = fn
thresh = i
final_img = img.copy()
print(thresh)
final_img[img > thresh] = 255
final_img[img < thresh] = 0
return final_img
def image_hist_demo(image):
color = {"blue", "green", "red"}
# enumerate() 函数用于将一个可遍历的数据对象(如列表、元组或字符串)组合为一个索引序列，同时列出数据下标和数据，一般用在 for 循环当中。
for i, color in enumerate(color):
hist = cv.calcHist([image], [i], None, [256], [0, 256])
plt.plot(hist, color=color) # 线
plt.show()
def show_gray_histogram(self):
numbins = 256
ranges = [0.0, 255.0]
width = 256
height = 256
bytes_per_line = 3 * width
hist_image = np.zeros([height, width, 3], np.uint8)
# hist_image = np.zeros((256,256,3)) #创建用于绘制直方图的全0图像
bins = np.arange(numbins).reshape(numbins, 1) # 直方图中各bin的顶点位置
color = [(255, 0, 0)] # BGR三种颜色
for ch, col in enumerate(color):
origin_hist = cv2.calcHist([self.gray_image], [ch], None, [numbins], ranges)
cv2.normalize(origin_hist, origin_hist, 0, 255 * 0.9, cv2.NORM_MINMAX)
hist = np.int32(np.around(origin_hist))
pts = np.column_stack((bins, hist))
cv2.polylines(hist_image, [pts], False, col)
# print(type(hist_image.data))
hist_image = np.flipud(hist_image)
# cv2.imshow("histogram", hist_image)
demo_utils.show_cvimage_to_label(hist_image, self.gray_histogram_label)
def hist2d_demo(image):
hsv = cv.cvtColor(image,cv.COLOR_BGR2HSV)
hist = cv.calcHist([hsv],[0,1],None,[180,256],[0,180,0,256])
#cv.imshow("2d",hist)
plt.imshow(hist,interpolation='nearest')
plt.title("2D")
plt.show()
def show_histogram(self):
# hist = cv2.calcHist([self.image],
# [0], #使用的通道
# None, #没有使用mask
# [256], #HistSize
# [0.0,255.0])
b, g, r = cv2.split(self.image)
numbins = 256
ranges = [0.0, 256.0]
b_hist = cv2.calcHist([b], [0], None, [numbins], ranges)
g_hist = cv2.calcHist([g], [0], None, [numbins], ranges)
r_hist = cv2.calcHist([r], [0], None, [numbins], ranges)
print(b_hist.shape)
width = 256
height = 256
hist_image = np.zeros([height, width, 3], np.uint8)
cv2.normalize(b_hist, b_hist, 0, height * 0.9, cv2.NORM_MINMAX)
cv2.normalize(g_hist, g_hist, 0, height * 0.9, cv2.NORM_MINMAX)
cv2.normalize(r_hist, r_hist, 0, height * 0.9, cv2.NORM_MINMAX)
for i in range(1, numbins, 1):
cv2.line(hist_image,
(i - 1, height - np.int32(np.around(b_hist[i - 1][0]))),
(i, height - np.int32(np.around(b_hist[i][0]))),
(255, 0, 0)
)
cv2.line(hist_image,
(i - 1, height - np.int32(np.around(g_hist[i - 1][0]))),
(i, height - np.int32(np.around(g_hist[i][0]))),
(0, 255, 0)
)
cv2.line(hist_image,
(i - 1, height - np.int32(np.around(r_hist[i - 1][0]))),
(i, height - np.int32(np.around(r_hist[i][0]))),
(0, 0, 255)
)
# cv2.imshow("Histogram", hist_image)
demo_utils.show_cvimage_to_label(hist_image, self.histogram_label)
self.show_histogram2()
def crear_histograma_img(path):
norm = np.empty(256)
img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
hist = cv2.calcHist([img], [0], None, [256], [0, 256])
h, w = img.shape
for i in range(256):
norm[i] = hist[i][0] / (h * w)
return norm
def plotColorHist(file):
img = cv2.imread(file)
color = ('b', 'g', 'r')
for i, col in enumerate(color):
histr = cv2.calcHist([img], [i], None, [256], [0, 256])
plt.plot(histr, color=col)
plt.xlim([0, 256])
plt.show()
def extractColorHistogramFeatures(url, mask_center):
img = getImage_cv2(url, 4)
if (mask_center):
mask = np.zeros(img.shape[:2], np.uint8)
mask[100:(img.shape[0]-100), 100:(img.shape[1]-100)] = 255
img = cv2.bitwise_and(img,img,mask = mask)
hist_red = cv2.calcHist([img],[2],None,[256],[0,256])
hist_blue = cv2.calcHist([img],[1],None,[256],[0,256])
hist_green = cv2.calcHist([img],[0],None,[256],[0,256])
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
hist_gray = cv2.calcHist([img_gray],[0],None,[256],[0,256])
return hist_red, hist_blue, hist_green, hist_gray
def calc_hist(gray):
hist = cv2.calcHist([gray],
channels=[0],
mask=None,
histSize=[256],
ranges=[0, 255])[:, 0]
hist /= (gray.shape[0] * gray.shape[1])
hist[:5] = 0
return hist
def imageBGR2FlatHist(image, bins=(8, 8, 8)):
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
hist = cv2.calcHist([hsv], [0, 1, 2], None, bins, [0, 180, 0, 256, 0, 256])
if imutils.is_cv2():
hist = cv2.normalize(hist)
else:
cv2.normalize(hist, hist)
return hist.flatten()
def calcAndDrawHist(image, color):
hist = cv2.calcHist([image], [0], None, [256], [0.0, 255.0])
minVal, maxVal, minLoc, maxLoc = cv2.minMaxLoc(hist)
histImg = np.zeros([256, 256, 3], np.uint8)
hpt = int(0.9 * 256)
for h in range(256):
intensity = int(hist[h]*hpt/maxVal)
cv2.line(histImg, (h, 256), (h, 256-intensity), color)
return histImg
def histogram(ImgNo, defThr=127):
img1 = cv2.imread(impDef.select_img(ImgNo))
img2 = cv2.imread(impDef.select_img(ImgNo))
img1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
#OpenCV 함수를 이용해 히스토그램 구하기
hist1 = cv2.calcHist([img1], [0], None, [256], [0, 256])
# cv2.calcHist(img, channel, mask, histSize, range)
# 이미지 히스토그램을 찾아서 Numpy배열로 리턴
# img : 히스토그램을 찾을 이미지, 인자는 반드시 []로 둘러싸야 함
# Channel : grayscal의 경우 [0]을 입력, 컬러이미지의 경우 B,G,R에 대한 [0],[1],[2]를 입력
# mask : 이미지 전체의 히스토그램을 구할경우 None, 특정영역을 구할 경우 이 영역에 해당하는 mask값을 입력
# histSize : BIN 개수, 인자는 []로 둘러싸야 함.
# range : 픽셀값 범위, 보통[0, 256]
# 히스토그램의 구하기 위해 가장 성능좋은 함수는 cv2.calcHist() 함수.....
#numpy를 이용해 히스토그램 구하기
hist2, bins = np.histogram(img1.ravel(), 256, [0, 256])
# np.histogram
# 이미지에서 구한 히스토그램과 BIN의 개수를 리턴
# 1-D 히스토그램의 경우 Numpy가 빠름
hist3 = np.bincount(img1.ravel(), minlength=256)
# np.bincount()
# Grayscale의 경우 np.historam()보다 약 10배 정도 빠르게 결과를 리턴.
# numpy.ravel() : numpy배열을 1차원으로 바꿔주는 함수
# matplotlib으로 히스토그램 그리기
plt.hist(img1.ravel(), 256, [0, 256])
# plt.hist() 하스토그램을 구하지 않고 바로 그림.
# 두번째 인자가 BIN의 개수 이 값을 16으로 바꿔주면 BIN의 개수가 16인 히스토그램이 그려짐.
# 컬러이미지 히스토그램 그리기
color = ('b', 'g', 'r')
for i, col in enumerate(color):
hist = cv2.calcHist([img2], [i], None, [256], [0, 256])
plt.plot(hist, color=col)
plt.xlim([0, 256])
# 가로축을 0 ~ 256까지로 제한
plt.show()
def histograma(img):
# Muestra el histograma de la imagen
color = ('r', 'g', 'b')
for i, c in enumerate(color):
hist = cv2.calcHist([img], [0], None, [256], [0, 256])
plt.plot(hist, color='gray')
plt.xlim([0, 256])
plt.show()
cv2.destroyAllWindows()
