def processKedu(zone, index):
ver = projectVertical(zone)
(h1, w1) = zone.shape
newHorizon = np.zeros([h1, w1], np.uint8)
for i in range(0, w1):
for j in range(0, ver[i]):
newHorizon[j, i] = 255
cv2.imwrite(dir_path + '/7_nums_kedu_' + str(index) + '.jpg', newHorizon)
print('img', dir_path + '/7_nums_kedu_' + str(index) + '.jpg')
maxtab, mintab = peakdetective.peakdet(ver, 3)
# print(maxtab)
k_res = list(maxtab[:, 1])
# print(res.index(max(res)),len(res))
k_pos = k_res.index(max(k_res))
k_len = len(k_res)
# print('指针位置',k_res.index(max(k_res)), '总刻度线个数',len(k_res))
# print('position', k_res.index(max(k_res)) / (len(k_res) - 1) * 100)
border1 = maxtab[0][0]
border2 = maxtab[len(maxtab) - 1][0]
pos = maxtab[k_pos][0]
rate = float(pos - border1) / (border2 - border1)
# print('比例计算：', rate)
return k_pos, k_len, rate
def processKedu(arr, type_arr):
# print(len(arr),type_arr)
rate_arr = []
index_arr = []
for ind in range(len(arr)):
kedu1, kedu2 = angle_corract(arr[ind]['kedu1'], arr[ind]['kedu2'])
ver = projectVertical(kedu1)
ver2 = projectVertical(cv2.subtract(kedu2, kedu1))
pos = np.mean(np.where(ver2 == np.max(ver2)))
print(pos)
(h1, w1) = kedu1.shape
newblack = np.zeros([h1, w1], np.uint8)
for i in range(0, w1):
for j in range(0, ver[i]):
newblack[j, i] = 255
cv2.imwrite('./pointer2/4_nums_kedu_' + str(1) + '.jpg', newblack)
maxtab, mintab = peakdetective.peakdet(ver, 5)
maxs = maxtab[:, 1]
tabs = maxtab[:, 0]
max_num = np.max(maxs)
max_index = np.where((maxs < max_num + 10) & (maxs > max_num - 10))[0]
tops = []
for m in max_index:
tops.append(tabs[m])
_left = 0
_right = 0
pos_index = 0
# print(i)
if type_arr[ind] == 0:
tops.reverse()
tops.append(tabs[0])
for t in range(len(tops)):
if tops[t] <= pos:
_left = tops[t - 1]
_right = tops[t]
pos_index = t - 1
break
else:
tops.append(tabs[-1])
for t in range(len(tops)):
if tops[t] >= pos:
_left = tops[t - 1]
_right = tops[t]
pos_index = t - 1
break
print('节点数', len(tops), tops)
rate = (pos - _left) / (_right - _left)
print(rate, pos_index)
rate_arr.append(rate)
index_arr.append(pos_index)
return rate_arr, index_arr
def split_area(src):
# index=0
# _areas=[]
# for src in src_arr:
# src1 = src.copy()
# src1 = cv2.dilate(src1,kernel8,iterations=2)
# un,area,un,un = findContours(src1,src)
# area = cv2.erode(area,kernel3)
# area = cv2.dilate(area,kernel4)
# ret, area = cv2.threshold(area, 127, 255, 0)
# lines,un = getLineBorder(area,20,20)
# print(lines)
lines, un = getLineBorder(src, 10, 300)
print(un)
src_cp = src.copy()
for i in lines:
src_cp[i, :] = 255
cv2.imwrite('./pointer3/4_area' + str(0) + '.jpg', src_cp)
# test_approx(src)
nums = src[:lines[2], :]
kedu = src[lines[1]:, :]
cv2.imwrite('./pointer3/4_nums' + str(0) + '.jpg', nums)
kedu = cv2.morphologyEx(kedu, cv2.MORPH_CLOSE, cross1, iterations=4)
ret, kedu = cv2.threshold(kedu, 100, 255, 0)
_, contours, hierarchy = cv2.findContours(kedu, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
c = sorted(contours, key=cv2.contourArea, reverse=True)[0]
x, y, w, h = cv2.boundingRect(c)
kedu = kedu[:, x:x + w]
# lines1, un = getLineBorder(kedu, 10, 50)
# for i in lines1:
# kedu[i,:]=255
ver = projectVertical(kedu)
maxtab, mintab = peakdetective.peakdet(ver, 5)
maxs = maxtab[:, 1]
tabs = maxtab[:, 0]
max_pos = np.where(maxs == max(maxs))[0][0]
print(tabs[max_pos])
cv2.imwrite('./pointer3/4_kedu' + str(0) + '.jpg', kedu)
def processSmallKedu(zone2polar, one_heart, numsShape, polar_, line_border_):
polar = cv2.logPolar(zone2polar, (one_heart[0] * 8, one_heart[1] * 8), 600,
cv2.WARP_FILL_OUTLIERS)
polar = cv2.rotate(polar, cv2.ROTATE_90_COUNTERCLOCKWISE)
cv2.imwrite('./v1/cut_polar_1.jpg', polar)
unused, numsCanny_1, unused1 = findContours(cv2.dilate(polar_,
kernel5,
iterations=2),
polar,
offset1=40,
offset2=30,
big_index=0)
numsCanny_1 = cv2.resize(numsCanny_1, (numsShape[1] * 4, numsShape[0] * 4))
cv2.imwrite('./v1/nums_canny1.jpg', numsCanny_1)
kedu_1_pointer = numsCanny_1[line_border_[0]:line_border_[1] + 60, :]
kedu_1_pointer = cv2.erode(kedu_1_pointer, kernel3)
cv2.imwrite('./v1/nums_kedu_1_pointer.jpg', kedu_1_pointer)
ver_ = projectVertical(kedu_1_pointer)
(h1, w1) = kedu_1_pointer.shape
newHorizon_ = np.zeros([h1, w1], np.uint8)
for i in range(0, w1):
for j in range(0, ver_[i]):
newHorizon_[j, i] = 255
cv2.imwrite('./v1/nums_kedu_111.jpg', newHorizon_)
maxtab, mintab = peakdetective.peakdet(ver_, 30)
# from matplotlib.pyplot import plot, scatter, show
# plot(ver)
# scatter(np.array(maxtab)[:, 0], np.array(maxtab)[:, 1], color='blue')
# scatter(np.array(mintab)[:, 0], np.array(mintab)[:, 1], color='red')
# show()
k_res = list(maxtab[:, 1])
# print(res.index(max(res)),len(res))
k_pos = k_res.index(max(k_res))
k_len = len(k_res)
print('position', k_res.index(max(k_res)), '总刻度线数：', (len(k_res) - 1))
return k_pos, k_len
def processBigKedu(lineZone):
border, ho = getLineBorder(lineZone, 20)
lineZone = cv2.erode(lineZone, kernel3)
numsCannyshape = lineZone.shape
kedu = lineZone[border[2]:border[2] + 500, :]
cv2.imwrite('./v1/nums_kedu.jpg', kedu)
ver = projectVertical(kedu)
(h1, w1) = kedu.shape
newHorizon = np.zeros([h1, w1], np.uint8)
for i in range(0, w1):
for j in range(0, ver[i]):
newHorizon[j, i] = 255
cv2.imwrite('./v1/nums_kedu_.jpg', newHorizon)
maxtab, mintab = peakdetective.peakdet(ver, 3)
k_res = list(maxtab[:, 1])
# print(res.index(max(res)),len(res))
k_pos = k_res.index(max(k_res))
k_len = len(k_res)
print('指针位置', k_res.index(max(k_res)), '总刻度线个数', len(k_res))
# print('position', k_res.index(max(k_res)) / (len(k_res) - 1) * 100)
return border, k_pos, k_len
def findPointer(src, heart1, heart2):
r1 = heart1[2]
hei1 = r1 / 3.5
r2 = heart2[2]
hei2 = r2 / 2
mask = np.zeros(src.shape, np.uint8)
cv2.circle(mask, (heart1[0], heart1[1]), int(r1 + hei1), 255, -1)
cv2.circle(mask, (heart2[0], heart2[1]), int(r2 + hei2), 0, -1)
cut1 = cv2.bitwise_and(mask, src)
cv2.imwrite('./pointer3/3_adp1.jpg', cut1)
mask = np.zeros(src.shape, np.uint8)
cv2.circle(mask, (heart1[0], heart1[1]), int(r1 + hei1), 255, -1)
cut_tmp = cv2.bitwise_and(mask, src)
mask = np.zeros(src.shape, np.uint8)
cv2.circle(mask, (heart2[0], heart2[1]), int(r2 + hei2), 255, -1)
cut2 = cv2.bitwise_and(mask, cut_tmp)
cv2.imwrite('./pointer3/3_adp2.jpg', cut2)
h1 = r1 / 2.5
mask = np.zeros(src.shape, np.uint8)
cv2.circle(mask, (heart1[0], heart1[1]), int(r1 + hei1), 255, -1)
cv2.circle(mask, (heart1[0], heart1[1]), int(r1 + hei1 / 3), 0, -1)
cut_num1 = cv2.bitwise_and(mask, cut1)
cv2.imwrite('./pointer3/3_num1.jpg', cut_num1)
h2 = r2 / 2.5
mask = np.zeros(src.shape, np.uint8)
cv2.circle(mask, (heart2[0], heart2[1]), int(r2 + hei2), 255, -1)
cv2.circle(mask, (heart2[0], heart2[1]), int(r2 + hei2 / 2.5), 0, -1)
cut_num2 = cv2.bitwise_and(mask, cut2)
cv2.imwrite('./pointer3/3_num2.jpg', cut_num2)
theta1 = detect_pointer(cut1, heart1, 1)
theta2 = detect_pointer(cut2, heart2, 2)
cut1 = make_kedu_area(cut1, heart1, theta1)
cut2 = make_kedu_area(cut2, heart2, theta2)
# mask = np.zeros(src.shape, np.uint8)
# # cv2.ellipse(mask,(heart1[0],heart1[1]),0,0,0,180,255)
# angle1 = -180/np.pi * theta1
# angle2 = 180/np.pi * theta2
# cv2.ellipse(mask, (heart1[0],heart1[1]), (int(r1 + hei1), int(r1 + hei1)), 0, angle1-30, angle1+30, 255, -1)
# cut1 = cv2.bitwise_and(mask, cut1)
# cv2.imwrite('./pointer3/3_ellipse.jpg', cut1)
kedu1, area_sets1 = polar_area(cut1, heart1)
ver1 = projectVertical(kedu1)
# print(ver1)
kedu2, area_sets2 = polar_area(cut2, heart2)
ver2 = projectVertical(kedu2)
maxtab1, mintab1 = peakdetective.peakdet(ver1, 10)
maxtab2, mintab2 = peakdetective.peakdet(ver2, 10)
rate1 = findeadge(maxtab1)
rate2 = findeadge(maxtab2)
# drawplot(maxtab1,ver1)
drawplot(maxtab2, ver2)
polar_num(cut_num1, heart1, area_sets1, 1)
# polar_num(cut_num2,heart2,area_sets2,2)
return rate1, rate2