in services/read-gauge.py [0:0]
def get_current_value(img, min_angle, max_angle, min_value, max_value, x, y, r, img_path, image_key, file_type):
#for testing purposes
#img = cv2.imread('gauge-%s.%s' % (gauge_number, file_type))
gray2 = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Set threshold and maxValue
thresh = 175
maxValue = 255
# for testing purposes, found cv2.THRESH_BINARY_INV to perform the best
# th, dst1 = cv2.threshold(gray2, thresh, maxValue, cv2.THRESH_BINARY);
# th, dst2 = cv2.threshold(gray2, thresh, maxValue, cv2.THRESH_BINARY_INV);
# th, dst3 = cv2.threshold(gray2, thresh, maxValue, cv2.THRESH_TRUNC);
# th, dst4 = cv2.threshold(gray2, thresh, maxValue, cv2.THRESH_TOZERO);
# th, dst5 = cv2.threshold(gray2, thresh, maxValue, cv2.THRESH_TOZERO_INV);
# cv2.imwrite('gauge-%s-dst1.%s' % (gauge_number, file_type), dst1)
# cv2.imwrite('gauge-%s-dst2.%s' % (gauge_number, file_type), dst2)
# cv2.imwrite('gauge-%s-dst3.%s' % (gauge_number, file_type), dst3)
# cv2.imwrite('gauge-%s-dst4.%s' % (gauge_number, file_type), dst4)
# cv2.imwrite('gauge-%s-dst5.%s' % (gauge_number, file_type), dst5)
# apply thresholding which helps for finding lines
th, dst2 = cv2.threshold(gray2, thresh, maxValue, cv2.THRESH_BINARY_INV);
# found Hough Lines generally performs better without Canny / blurring, though there were a couple exceptions where it would only work with Canny / blurring
#dst2 = cv2.medianBlur(dst2, 5)
#dst2 = cv2.Canny(dst2, 50, 150)
#dst2 = cv2.GaussianBlur(dst2, (5, 5), 0)
# for testing, show image after thresholding
cv2.imwrite('%s%s-dst2.%s' % ( img_path, image_key, file_type), dst2)
# find lines
minLineLength = 10
maxLineGap = 0
lines = cv2.HoughLinesP(image=dst2, rho=3, theta=np.pi / 180, threshold=100,minLineLength=minLineLength, maxLineGap=0) # rho is set to 3 to detect more lines, easier to get more then filter them out later
print("line count: " ,len(lines))
#for testing purposes, show all found lines
for i in range(0, len(lines)):
for x1, y1, x2, y2 in lines[i]:
cv2.line(img, (x1, y1), (x2, y2), (0, 255, 0), 2)
cv2.imwrite('%s%s-linetest.%s' % ( img_path, image_key, file_type), img)
# remove all lines outside a given radius
final_line_list = []
# print "x: %s y: %s radius: %s" %(x,y,r)
diff1LowerBound = 0.15 #diff1LowerBound and diff1UpperBound determine how close the line should be from the center
diff1UpperBound = 0.25
diff2LowerBound = 0.5 #diff2LowerBound and diff2UpperBound determine how close the other point of the line should be to the outside of the gauge
diff2UpperBound = 1.0
for i in range(0, len(lines)):
for x1, y1, x2, y2 in lines[i]:
# print('Line x1: %s, y1: %s, x2: %s , y2: %s' % (x1, y1, x2, y2))
diff1 = dist_2_pts(x, y, x1, y1) # x, y is center of circle
diff2 = dist_2_pts(x, y, x2, y2) # x, y is center of circle
#set diff1 to be the smaller (closest to the center) of the two), makes the math easier
if (diff1 > diff2):
temp = diff1
diff1 = diff2
diff2 = temp
# check if line is within an acceptable range
if (((diff1<diff1UpperBound*r) and (diff1>diff1LowerBound*r) and (diff2<diff2UpperBound*r)) and (diff2>diff2LowerBound*r)):
line_length = dist_2_pts(x1, y1, x2, y2)
# add to final list
final_line_list.append([x1, y1, x2, y2])
# print('Found x1: %s, y1: %s, x2: %s , y2: %s !!!' % (x1, y1, x2, y2))
#testing only, show all lines after filtering
# for i in range(0,len(final_line_list)):
# x1 = final_line_list[i][0]
# y1 = final_line_list[i][1]
# x2 = final_line_list[i][2]
# y2 = final_line_list[i][3]
# cv2.line(img, (x1, y1), (x2, y2), (0, 255, 0), 2)
print(final_line_list)
# assumes the first line is the best one
if final_line_list:
x1 = final_line_list[0][0]
y1 = final_line_list[0][1]
x2 = final_line_list[0][2]
y2 = final_line_list[0][3]
cv2.line(img, (x1, y1), (x2, y2), (0, 255, 0), 2)
#for testing purposes, show the line overlayed on the original image
cv2.imwrite('%s%s-lines-2.%s' % (img_path, image_key, file_type), img)
#find the farthest point from the center to be what is used to determine the angle
dist_pt_0 = dist_2_pts(x, y, x1, y1)
dist_pt_1 = dist_2_pts(x, y, x2, y2)
if (dist_pt_0 > dist_pt_1):
x_angle = x1 - x
y_angle = y - y1
else:
x_angle = x2 - x
y_angle = y - y2
# take the arc tan of y/x to find the angle
res = np.arctan(np.divide(float(y_angle), float(x_angle)))
#np.rad2deg(res) #coverts to degrees
print (x_angle)
print (y_angle)
print (res)
print (np.rad2deg(res))
#these were determined by trial and error
res = np.rad2deg(res)
if x_angle > 0 and y_angle > 0: #in quadrant I
final_angle = 270 - res
if x_angle < 0 and y_angle > 0: #in quadrant II
final_angle = 90 - res
if x_angle < 0 and y_angle < 0: #in quadrant III
final_angle = 90 - res
if x_angle > 0 and y_angle < 0: #in quadrant IV
final_angle = 270 - res
print ("final angle:",final_angle)
old_min = float(min_angle)
old_max = float(max_angle)
new_min = float(min_value)
new_max = float(max_value)
old_value = final_angle
old_range = (old_max - old_min)
new_range = (new_max - new_min)
new_value = (((old_value - old_min) * new_range) / old_range) + new_min
return new_value