def gaugeRead(frame):
img_cont = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
#frame_m, frame_n = np.shape(img_cont)
img_cont = cv2.filter2D(img_cont, -1, mean_kernel)
_, img_bin = cv2.threshold(img_cont, 80, 255, cv2.THRESH_BINARY)
imgOut(img_bin, 'bin.jpg')
#提取边沿
binary, contours, hierarchy = cv2.findContours(img_bin, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
figIndex = 0
locs = []
for c in contours:
area = cv2.contourArea(c)
if (area > 1000 and area < 3000):
cv2.drawContours(frame, c, -1, (0, 255, 0), 2)
figIndex = figIndex + 1
x, y, w, h = cv2.boundingRect(c)
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 0, 255), 2)
numFig = img_bin[y:y + h, x:x + w]
imgOut(numFig, str(figIndex) + '.jpg')
keyPoint = (x + w / 2, y + h / 2)
bottomPoint = y + h
res, _ = Templ.match(numFig)
if (res > -1):
print([keyPoint, bottomPoint, res])
#print(figIndex, ' - ', res, ' ', _)
imgOut(frame, 'frame.jpg')
'''
templ = templ[y:y+h, x:x+w]
imgOut(templ, 'templ.jpg')
locs = []
figIndex = 0
for c in cnts:
area = cv2.contourArea(c)
if (area < 1500 and area > 500):
figIndex = figIndex+1
x, y, w, h = cv2.boundingRect(c)
cv2.rectangle(gauge, (x,y), (x+w, y+h), (0, 255, 0), 2)
numFig = warped[y:y+h, x:x+w]
keyPoint = (x+w/2, y+h/2)
bottomPoint = y+h
res = Templ.match(numFig)
if (res != -1):
#print('Times ', figIndex, ': ', res)
imgOut(numFig, str(figIndex)+'.jpg')
locs.append([keyPoint, bottomPoint, res])
dial = []
for i in range(np.shape(locs)[0]):
PointA = locs[i][0]
if PointA[0] > gauge_n/2:
continue
for j in range(i+1, np.shape(locs)[0]):
PointB = locs[j][0]
dist = np.sqrt(np.power(PointA[0]-PointB[0], 2) + 1.5*np.power(PointA[1]-PointB[1], 2))
if (dist < 100):
if (PointA[0] > PointB[0]):
def gaugeRead(frame):
img_cont = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
#frame_m, frame_n = np.shape(img_cont)
#img_cont = cv2.filter2D(img_gray, -1, my_kernel)
#提取水尺边沿
edge_flag, edge = edgeDetect(img_cont, frame)
if (edge_flag):
#获得近似四边形
epsilon = 0.02 * cv2.arcLength(edge, True)
approx = cv2.approxPolyDP(edge, epsilon, True)
x, y, w, h = cv2.boundingRect(approx)
for p in approx:
cv2.circle(frame, (p[0][0], p[0][1]), 10, (0, 255, 0), -1)
#cv2.rectangle(frame, (x,y), (x+w, y+h), (0, 0, 255), 2)
#imgOut(frame, 'frame.jpg')
#自适应四个顶点
if (np.shape(approx)[0] >= 4):
mindis = getDist((x, y), (x + w, y + h))
#cv2.circle(frame, (x+w-1, y), 10, (0, 255, 0), -1)
#imgOut(frame, 'frame.jpg')
for point in approx:
dis = getDist(point[0], (x + w - 1, y))
if dis < mindis:
mindis = dis
ex1 = point[0][0]
ey1 = point[0][1]
mindis = getDist((x, y), (x + w, y + h))
for point in approx:
dis = getDist(point[0], (x + w - 1, y + h - 1))
if dis < mindis:
mindis = dis
ex2 = point[0][0]
ey2 = point[0][1]
mindis = getDist((x, y), (x + w, y + h))
for point in approx:
dis = getDist(point[0], (x, y + h - 1))
if dis < mindis:
mindis = dis
ex3 = point[0][0]
ey3 = point[0][1]
mindis = getDist((x, y), (x + w, y + h))
for point in approx:
dis = getDist(point[0], (x, y))
if dis < mindis:
mindis = dis
ex4 = point[0][0]
ey4 = point[0][1]
else:
return (False, 'NA', 'No edge')
#计算变换矩阵
maxlength = int(
max(np.sqrt(np.power(ex1 - ex2, 2) + np.power(ey1 - ey2, 2)),
np.sqrt(np.power(ex3 - ex4, 2) + np.power(ey3 - ey4, 2))))
maxwidth = int(
max(np.sqrt(np.power(ex3 - ex2, 2) + np.power(ey3 - ey2, 2)),
np.sqrt(np.power(ex1 - ex4, 2) + np.power(ey1 - ey4, 2))))
dst = np.array([[maxwidth - 1, 0], [maxwidth - 1, maxlength - 1],
[0, maxlength - 1], [0, 0]],
dtype="float32")
rect = np.array([[ex1, ey1], [ex2, ey2], [ex3, ey3], [ex4, ey4]],
dtype="float32")
M = cv2.getPerspectiveTransform(rect, dst)
#将水尺变换为矩形
gauge = cv2.warpPerspective(frame, M, (maxwidth, maxlength))
gauge_m, gauge_n, _ = np.shape(gauge)
#二值化
warped = cv2.cvtColor(gauge, cv2.COLOR_BGR2GRAY)
warped = cv2.filter2D(warped, -1, sharp_kernel)
_, warped = cv2.threshold(warped, 210, 255, cv2.THRESH_BINARY_INV)
#腐蚀膨胀
warped = cv2.dilate(warped, erode_kernel, iterations=1)
mor_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (4, 4))
warped = cv2.morphologyEx(warped, cv2.MORPH_CLOSE, mor_kernel)
warped = cv2.morphologyEx(warped, cv2.MORPH_OPEN, mor_kernel)
_, warped = cv2.threshold(warped, 210, 255, cv2.THRESH_BINARY_INV)
#提取边沿
binary, cnts, hierarchy = cv2.findContours(warped, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
#提取数字的边沿
locs = []
figIndex = 0
for c in cnts:
area = cv2.contourArea(c)
if (area < 1500 and area > 500):
figIndex = figIndex + 1
x, y, w, h = cv2.boundingRect(c)
cv2.rectangle(gauge, (x, y), (x + w, y + h), (0, 255, 0), 2)
numFig = warped[y:y + h, x:x + w]
#设置关键点、最底点和识别结果
keyPoint = (x + w / 2, y + h / 2)
bottomPoint = y + h
res, _ = Templ.match(numFig)
#imgOut(numFig, str(figIndex)+'.jpg')
#在约束范围内的数字储存在数组中
if (res != -1 and keyPoint[0] > 50
and keyPoint[0] < gauge_n / 2):
locs.append([keyPoint, bottomPoint, res])
#imgOut(gauge, 'gauge.jpg')
#生成比例尺
dial = []
for i in range(np.shape(locs)[0]):
PointA = locs[i][0]
for j in range(i + 1, np.shape(locs)[0]):
PointB = locs[j][0]
dist = np.sqrt(
np.power(PointA[0] - PointB[0], 2) +
10 * np.power(PointA[1] - PointB[1], 2))
#当距离小于最大距离
if (dist < 80):
#print(dist)
if (PointA[0] > PointB[0]):
index_i = j
index_j = i
else:
index_i = i
index_j = j
num = locs[index_i][2] + 0.1 * locs[index_j][2]
point = locs[index_i][1]
dial.append([point, num])
#计算结果
numA = 0
numB = 0
if (np.shape(dial)[0] >= 2):
for i in range(np.shape(dial)[0]):
if (dial[i][1] == 3.4 or dial[i][1] == 3.2 or dial[i][1] == 3.0
or dial[i][1] == 2.8):
if (numA == 0):
numA = dial[i][1]
potA = dial[i][0]
continue
if (numB == 0):
numB = dial[i][1]
potB = dial[i][0]
continue
break
if (numA == 0 or numB == 0):
return (False, 'NA', 'No Dial')
ans = numA - 0.92 * (gauge_m - potA) * (numA - numB) / (potB -
potA)
return (True, ans, dial)
else:
return (False, 'NA', 'No Dial')
#cv2.drawContours(gauge, locs, -1, (0, 255, 0), 2)
#imgOut(gauge, 'gauge.jpg')
#imgOut(warped, 'warped.jpg')
else:
return (False, 'NA', 'No edge')
def reader_m2(gauge):
gauge_m, gauge_n, _ = np.shape(gauge)
#二值化
warped = cv2.cvtColor(gauge, cv2.COLOR_BGR2GRAY)
warped = cv2.filter2D(warped, -1, mean_kernel)
_, warped = cv2.threshold(warped, 210, 255, cv2.THRESH_BINARY_INV)
#腐蚀膨胀
mor_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (4, 4))
warped = cv2.morphologyEx(warped, cv2.MORPH_CLOSE, mor_kernel)
warped = cv2.morphologyEx(warped, cv2.MORPH_OPEN, mor_kernel)
_, warped = cv2.threshold(warped, 127, 255, cv2.THRESH_BINARY_INV)
#提取边沿
binary, cnts, hierarchy = cv2.findContours(warped, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
#提取数字的边沿
locs = []
nums = []
figIndex = 0
for c in cnts:
area = cv2.contourArea(c)
if (area < 1500 and area > 500):
figIndex = figIndex + 1
x, y, w, h = cv2.boundingRect(c)
cv2.rectangle(gauge, (x, y), (x + w, y + h), (0, 255, 0), 2)
numFig = warped[y:y + h, x:x + w]
#设置关键点、最底点和识别结果
keyPoint = (x + w / 2, y + h / 2)
bottomPoint = y + h
res, _ = Templ.match(numFig)
#在约束范围内的数字储存在数组中
if (res != -1 and keyPoint[0] > 110 and keyPoint[0] < gauge_n / 2):
nums.append([keyPoint, bottomPoint, res])
locs.append([keyPoint, bottomPoint, res])
#生成比例尺
if (np.shape(nums)[0] <= 0):
return (False, 'No Dial', 'NA')
dial = []
numId = nums[0]
minDis = gauge_m
minIndex = -1
for i in range(np.shape(locs)[0]):
PointA = locs[i]
if (numId[1] - PointA[1] > 0 and numId[1] - PointA[1] < minDis):
minIndex = i
minDis = numId[1] - PointA[1]
dial.append([numId[1], 2 + 0.1 * numId[2]])
dial.append([locs[minIndex][1], 2 + 0.1 * numId[2] + 0.1])
#print(dial)
#计算结果
potA = dial[0][0]
numA = dial[0][1]
potB = dial[1][0]
numB = dial[1][1]
if (potB - potA != 0):
ans = numA - 0.92 * (gauge_m - potA) * (numA - numB) / (potB - potA)
return (True, ans, dial)
else:
return (False, 'No Dial', 'NA')
def reader_m1(gauge):
gauge_m, gauge_n, _ = np.shape(gauge)
#二值化
warped = cv2.cvtColor(gauge, cv2.COLOR_BGR2GRAY)
warped = cv2.filter2D(warped, -1, sharp_kernel)
_, warped = cv2.threshold(warped, 210, 255, cv2.THRESH_BINARY_INV)
#腐蚀膨胀
warped = cv2.dilate(warped, erode_kernel, iterations=1)
mor_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (4, 4))
warped = cv2.morphologyEx(warped, cv2.MORPH_CLOSE, mor_kernel)
warped = cv2.morphologyEx(warped, cv2.MORPH_OPEN, mor_kernel)
_, warped = cv2.threshold(warped, 210, 255, cv2.THRESH_BINARY_INV)
#提取边沿
binary, cnts, hierarchy = cv2.findContours(warped, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
#提取数字的边沿
locs = []
figIndex = 0
for c in cnts:
area = cv2.contourArea(c)
if (area < 1500 and area > 500):
figIndex = figIndex + 1
x, y, w, h = cv2.boundingRect(c)
cv2.rectangle(gauge, (x, y), (x + w, y + h), (0, 255, 0), 2)
numFig = warped[y:y + h, x:x + w]
#设置关键点、最底点和识别结果
keyPoint = (x + w / 2, y + h / 2)
bottomPoint = y + h
res, _ = Templ.match(numFig)
#在约束范围内的数字储存在数组中
if (res != -1 and keyPoint[0] > 50 and keyPoint[0] < gauge_n / 2):
locs.append([keyPoint, bottomPoint, res])
#生成比例尺
dial = []
for i in range(np.shape(locs)[0]):
PointA = locs[i][0]
for j in range(i + 1, np.shape(locs)[0]):
PointB = locs[j][0]
dist = np.sqrt(
np.power(PointA[0] - PointB[0], 2) +
10 * np.power(PointA[1] - PointB[1], 2))
#当距离小于最大距离
if (dist < 80):
#print(dist)
if (PointA[0] > PointB[0]):
index_i = j
index_j = i
else:
index_i = i
index_j = j
num = locs[index_i][2] + 0.1 * locs[index_j][2]
point = locs[index_i][1]
dial.append([point, num])
#计算结果
numA = 0
numB = 0
if (np.shape(dial)[0] >= 2):
for i in range(np.shape(dial)[0]):
if (dial[i][1] == 3.4 or dial[i][1] == 3.2 or dial[i][1] == 3.0
or dial[i][1] == 2.8):
if (numA == 0):
numA = dial[i][1]
potA = dial[i][0]
continue
if (numB == 0):
numB = dial[i][1]
potB = dial[i][0]
continue
break
if (numA == 0 or numB == 0):
return (False, 'No Dial', 'NA')
ans = numA - 0.92 * (gauge_m - potA) * (numA - numB) / (potB - potA)
return (True, ans, dial)
else:
return (False, 'No Dial', 'NA')
def gaugeRead(frame):
img_cont = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
imgOut(frame, 'frame.jpg')
#frame_m, frame_n = np.shape(img_cont)
#img_cont = cv2.filter2D(img_cont, -1, sharp_kernel)
#img_cont = cv2.Canny(img_cont, 50, 100)
#imgOut(img_cont, 'cont.jpg')
#提取水尺边沿
edge_flag, edge = edgeDetect(img_cont, frame)
if (edge_flag):
#获得近似四边形
epsilon = 0.02 * cv2.arcLength(edge, True)
approx = cv2.approxPolyDP(edge, epsilon, True)
x, y, w, h = cv2.boundingRect(approx)
#for p in approx:
# cv2.circle(frame, (p[0][0], p[0][1]), 10, (0, 0, 255), -1)
#cv2.rectangle(frame, (x,y), (x+w, y+h), (0, 0, 255), 2)
#imgOut(frame, 'frame.jpg')
#自适应四个顶点
if (np.shape(approx)[0] >= 4):
mindis = getDist((x, y), (x + w, y + h))
#cv2.circle(frame, (x+w-1, y), 10, (0, 255, 0), -1)
for point in approx:
dis = getDist(point[0], (x + w - 1, y))
if dis < mindis:
mindis = dis
ex1 = point[0][0]
ey1 = point[0][1]
mindis = getDist((x, y), (x + w, y + h))
for point in approx:
dis = getDist(point[0], (x + w - 1, y + h - 1))
if dis < mindis:
mindis = dis
ex2 = point[0][0]
ey2 = point[0][1]
mindis = getDist((x, y), (x + w, y + h))
for point in approx:
dis = getDist(point[0], (x, y + h - 1))
if dis < mindis:
mindis = dis
ex3 = point[0][0]
ey3 = point[0][1]
mindis = getDist((x, y), (x + w, y + h))
for point in approx:
dis = getDist(point[0], (x, y))
if dis < mindis:
mindis = dis
ex4 = point[0][0]
ey4 = point[0][1]
#cv2.rectangle(frame, (ex4, ey4), (ex2, ey2), (0, 0, 255), 2)
#imgOut(frame, 'frame.jpg')
else:
return (False, 'NA', 'No edge')
#计算变换矩阵
maxlength = int(
max(np.sqrt(np.power(ex1 - ex2, 2) + np.power(ey1 - ey2, 2)),
np.sqrt(np.power(ex3 - ex4, 2) + np.power(ey3 - ey4, 2))))
maxwidth = int(
max(np.sqrt(np.power(ex3 - ex2, 2) + np.power(ey3 - ey2, 2)),
np.sqrt(np.power(ex1 - ex4, 2) + np.power(ey1 - ey4, 2))))
dst = np.array([[maxwidth - 1, 0], [maxwidth - 1, maxlength - 1],
[0, maxlength - 1], [0, 0]],
dtype="float32")
rect = np.array([[ex1, ey1], [ex2, ey2], [ex3, ey3], [ex4, ey4]],
dtype="float32")
M = cv2.getPerspectiveTransform(rect, dst)
#将水尺变换为矩形
gauge = cv2.warpPerspective(frame, M, (maxwidth, maxlength))
gauge_m, gauge_n, _ = np.shape(gauge)
#二值化
warped = cv2.cvtColor(gauge, cv2.COLOR_BGR2GRAY)
#warped = np.uint8(np.clip((1.2*warped - 50), 0, 255))
warped = cv2.filter2D(warped, -1, mean_kernel)
imgOut(warped, 'sharp.jpg')
_, warped = cv2.threshold(warped, 210, 255, cv2.THRESH_BINARY_INV)
#腐蚀膨胀
#warped = cv2.dilate(warped, erode_kernel, iterations = 1)
mor_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (4, 4))
warped = cv2.morphologyEx(warped, cv2.MORPH_CLOSE, mor_kernel)
warped = cv2.morphologyEx(warped, cv2.MORPH_OPEN, mor_kernel)
_, warped = cv2.threshold(warped, 127, 255, cv2.THRESH_BINARY_INV)
imgOut(warped, 'warped.jpg')
#提取边沿
binary, cnts, hierarchy = cv2.findContours(warped, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
#提取数字的边沿
locs = []
nums = []
figIndex = 0
for c in cnts:
area = cv2.contourArea(c)
if (area < 1500 and area > 500):
figIndex = figIndex + 1
x, y, w, h = cv2.boundingRect(c)
cv2.rectangle(gauge, (x, y), (x + w, y + h), (0, 255, 0), 2)
numFig = warped[y:y + h, x:x + w]
imgOut(numFig, str(figIndex) + '.jpg')
#设置关键点、最底点和识别结果
keyPoint = (x + w / 2, y + h / 2)
bottomPoint = y + h
res, _ = Templ.match(numFig)
#imgOut(numFig, str(figIndex)+'.jpg')
#在约束范围内的数字储存在数组中
if (res != -1 and keyPoint[0] > 110
and keyPoint[0] < gauge_n / 2):
nums.append([keyPoint, bottomPoint, res])
#print(figIndex, res, _, keyPoint)
locs.append([keyPoint, bottomPoint, res])
#print(locs)
imgOut(gauge, 'gauge.jpg')
#生成比例尺
if (np.shape(nums)[0] <= 0):
return (False, 'NA', 'No Dial')
dial = []
numId = nums[0]
minDis = gauge_m
minIndex = -1
for i in range(np.shape(locs)[0]):
PointA = locs[i]
if (numId[1] - PointA[1] > 0 and numId[1] - PointA[1] < minDis):
minIndex = i
minDis = numId[1] - PointA[1]
dial.append([numId[1], 2 + 0.1 * numId[2]])
dial.append([locs[minIndex][1], 2 + 0.1 * numId[2] + 0.1])
#print(dial)
#计算结果
potA = dial[0][0]
numA = dial[0][1]
potB = dial[1][0]
numB = dial[1][1]
if (potB - potA != 0):
ans = numA - 0.92 * (gauge_m - potA) * (numA - numB) / (potB -
potA)
return (True, ans, dial)
else:
return (False, 'NA', 'No Dial')
#cv2.drawContours(gauge, locs, -1, (0, 255, 0), 2)
#imgOut(gauge, 'gauge.jpg')
#imgOut(warped, 'warped.jpg')
else:
return (False, 'NA', 'No edge')
locs = []
figIndex = 0
for c in cnts:
area = cv2.contourArea(c)
if (area < 1500 and area > 500):
figIndex = figIndex + 1
x, y, w, h = cv2.boundingRect(c)
cv2.rectangle(gauge, (x, y), (x + w, y + h), (0, 255, 0), 2)
numFig = warped[y:y + h, x:x + w]
##print('Figure: ', figIndex)
imgOut(numFig, str(figIndex) + '.jpg')
keyPoint = (x + w / 2, y + h / 2)
bottomPoint = y + h
res, _ = Templ.match(numFig)
#print(' Result: ', res)
if (res != -1 and keyPoint[0] > 50 and keyPoint[0] < gauge_n / 2):
locs.append([keyPoint, bottomPoint, res])
dial = []
for i in range(np.shape(locs)[0]):
PointA = locs[i][0]
for j in range(i + 1, np.shape(locs)[0]):
PointB = locs[j][0]
dist = np.sqrt(
np.power(PointA[0] - PointB[0], 2) +
10 * np.power(PointA[1] - PointB[1], 2))
if (dist < 80):
#print(dist)
