Python opencv remove image noise

Question

I am trying to isolate certain colored lines (field lines) in a set of hockey images. I applied a hue saturation (HLS) color filter and managed to transmit all the components of the original image that are in the specified HLS range. However, some parts of the image are also transmitted because they satisfy the color scheme, for example, part of the crowd and part of the playing field. However, I want to isolate only the line that is required. How can i do this?

NOTE. I have separate programs for blue and yellow because they require different ranges of HLS. Some images have several lines. Also, as you can see from the second image that I put, the lines may be slightly curved. In the second image this would be enough if I could get only the straight part.

I tried various image transformations and morphological operations with no luck. I also experimented quite a bit with the HLS ranges and set them so that they displayed the best result on the set of images that I have, but I still have not received a satisfactory result.

Original Images:

the code:

import cv2
import numpy as np

frame = cv2.imread('hockey4.jpg')
width=900
height=600
frame = cv2.resize(frame,(width,height))
# Convert BGR to HLS
hls = cv2.cvtColor(frame, cv2.COLOR_BGR2HLS)

#HLS ranges for blue
#lower array defines the lower limit and upper array defines the upper limit of the range
#The mask is a binary image where the output is white if the corresponding pixel in the input image is between the range specified by upper and lower limits

#blue
lower = np.array([90,90,19])  #90,90,19
upper = np.array([130,190,100]) #130,190,100
mask = cv2.inRange(hls, lower, upper)

# Bitwise-AND mask and original image
res = cv2.bitwise_and(frame,frame, mask= mask)

Answer 1

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, cv2.inRange() cv2.morphologyEx():   1 , 1 , 2 , 2

:

import cv2
import numpy as np

img = cv2.imread('0.jpg')

# crop out the top of the image where the scores are
h, w = img.shape[:2]
score_h = int(h/8)
img = img[score_h:h, :]
h, w = img.shape[:2]

# blur for better thresholding
blur = cv2.GaussianBlur(img, (5,5), 1)

# threshold in HSV space
hsv = cv2.cvtColor(blur, cv2.COLOR_BGR2HSV)

# threshold for yellow
lb_y = np.array([11, 0, 153])
ub_y = np.array([52, 255, 255])
bin_y = cv2.inRange(hsv, lb_y, ub_y)
# cv2.imshow("thresh yellow", bin_y)
# cv2.waitKey(0)

# open to remove spurious yellow bits
open_kern = np.ones((3,3), dtype=np.uint8)
bin_y = cv2.morphologyEx(bin_y, cv2.MORPH_OPEN, open_kern, iterations=2)
# cv2.imshow("opened yellow", bin_y)
# cv2.waitKey(0)

# threshold for blue
lb_b = np.array([113, 41, 191])
ub_b = np.array([119, 76, 232])
bin_b = cv2.inRange(hsv, lb_b, ub_b)
# cv2.imshow("thresh blue", bin_b)
# cv2.waitKey(0)

# open to remove spurious blue bits
kern = np.ones((3,3), dtype=np.uint8)
bin_b = cv2.morphologyEx(bin_b, cv2.MORPH_OPEN, kern, iterations=2)
# cv2.imshow("opened blue", bin_b)
# cv2.waitKey(0)

# combine to show yellow detection
rip_y = img.copy()
rip_y[bin_y==0] = 0
mark_y = cv2.addWeighted(img, .4, rip_y, .6, 1)
cv2.imshow("marked yellow", mark_y)
cv2.waitKey(0)
# cv2.imwrite('0-y.jpg',mark_y)

# combine to show blue detection
rip_b = img.copy()
rip_b[bin_b==0] = 0
mark_b = cv2.addWeighted(img, .4, rip_b, .6, 1)
cv2.imshow("marked blue", mark_b)
cv2.waitKey(0)
# cv2.imwrite('0-b.jpg',mark_b)

:

import cv2
import numpy as np

img = cv2.imread('1.jpg')

# crop out the top of the image where the scores are
h, w = img.shape[:2]
score_h = int(h/10)
img = img[score_h:h, :]
h, w = img.shape[:2]

# blur for better thresholding
blur = cv2.GaussianBlur(img, (5,5), 1)

# threshold in HSV space
hsv = cv2.cvtColor(blur, cv2.COLOR_BGR2HSV)

# threshold for yellow
lb_y = np.array([14, 79, 140])
ub_y = np.array([25, 255, 217])
bin_y = cv2.inRange(hsv, lb_y, ub_y)
# cv2.imshow("thresh yellow", bin_y)
# cv2.waitKey(0)

# open to remove spurious yellow bits
open_kern = np.ones((3,3), dtype=np.uint8)
bin_y = cv2.morphologyEx(bin_y, cv2.MORPH_OPEN, open_kern, iterations=2)
# cv2.imshow("opened yellow", bin_y)
# cv2.waitKey(0)

# threshold for blue
lb_b = np.array([113, 50, 150])
ub_b = np.array([135, 255, 255])
bin_b = cv2.inRange(hsv, lb_b, ub_b)
# cv2.imshow("thresh blue", bin_b)
# cv2.waitKey(0)

# close to fill in blue lines
kern = np.ones((3,3), dtype=np.uint8)
bin_b = cv2.morphologyEx(bin_b, cv2.MORPH_CLOSE, kern, iterations=2)
# cv2.imshow("closed blue", bin_b)
# cv2.waitKey(0)

# open to remove spurious lines
bin_b = cv2.morphologyEx(bin_b, cv2.MORPH_OPEN, kern, iterations=2)
# cv2.imshow("opened blue", bin_b)
# cv2.waitKey(0)

# combine to show yellow detection
rip_y = img.copy()
rip_y[bin_y==0] = 0
mark_y = cv2.addWeighted(img, .4, rip_y, .6, 1)
cv2.imshow("marked yellow", mark_y)
cv2.waitKey(0)
cv2.imwrite('1-y.jpg',mark_y)

# combine to show blue detection
rip_b = img.copy()
rip_b[bin_b==0] = 0
mark_b = cv2.addWeighted(img, .4, rip_b, .6, 1)
cv2.imshow("marked blue", mark_b)
cv2.waitKey(0)
cv2.imwrite('1-b.jpg',mark_b)

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