Develop Reference

Removing blood vessels - denoising the vessels into the background colour

Problem statement:
fundus image, i need to suppress the blood vessels so that they do not appear on the image for the classifier being used.
There were a few thoughts; but easiest approach was to segment out a rough outline of the vasculature. This is achieved.
Next was to get the colour for the surround area and use it to blend it to the white area found; then merge it back to the original image.
Any suggestions for identifying the colour and blending it in. then merging it back to the original image so that the vessels are not visible. One thought was to do a contour of the each white area and find the corresponding colour outside and use it to blend it back.
Any alternate approach is also welcome.
eyepac image - vessels are visible; want to make it the colour of its surrounding so that they are not visible
Here is the code:
def apply_threshold_with_denoising(image):
image = cv2.adaptiveThreshold(image, 250, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 115, 1)
image = cv2.fastNlMeansDenoising(image, 1.5, 5, 5)
return image
def delete_small_components(image, size):
_, blackAndWhite = cv2.threshold(image, 127, 255, cv2.THRESH_BINARY_INV)
nlabels, labels, stats, centroids = cv2.connectedComponentsWithStats(blackAndWhite, None, None, None, 8, cv2.CV_32S)
sizes = stats[1:, -1] # get CC_STAT_AREA component
image = np.zeros(labels.shape, np.uint8)
for i in range(0, nlabels - 1):
if sizes[i] >= 150: # filter small dotted regions
image[labels == i + 1] = 255
return cv2.bitwise_not(image)
def kernel(num1, num2):
return np.ones((num1, num2), np.uint8)
def resize(img):
scale_percent = 50 # percent of original size
width = int(img.shape[1] * scale_percent / 100)
height = int(img.shape[0] * scale_percent / 100)
dim = (width, height)
# resize image
return cv2.resize(img, dim, interpolation=cv2.INTER_AREA)
def get_large_vessels(image):
struct_kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 4))
opening = cv2.morphologyEx(image, cv2.MORPH_OPEN, struct_kernel, iterations=1)
cnts = cv2.findContours(opening, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
area = cv2.contourArea(c)
if area < 250:
cv2.drawContours(opening, [c], -1, (0, 0, 0), -1)
return opening
def get_small_vessels(both, large):
large = cv2.dilate(large, kernel(3, 3), iterations=5)
subtract = cv2.subtract(both, large)
return subtract
def remove_background(image, mask):
image = cv2.bitwise_and(cv2.bitwise_not(image), cv2.bitwise_not(image), mask=mask)
image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernel(2, 2))
return image
img = cv2.imread('2001_left.jpeg')
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
l_b = np.array([0, 0, 30])
u_b = np.array([255, 255, 255])
mask = cv2.inRange(hsv, l_b, u_b)
mask = cv2.erode(mask, kernel(2, 2), iterations=5)
grayscale = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
threshold = apply_threshold_with_denoising(grayscale)
kernel22 = cv2.dilate(threshold, kernel(2, 2), iterations=2)
remove_small1kernel22 = delete_small_components(kernel22, 5)
remove_small1kernel22)
dilation = cv2.dilate(threshold, kernel(2, 1), iterations=2)
remove_small1 = delete_small_components(dilation, 150)
dilation = cv2.dilate(threshold, kernel(1, 2), iterations=2)
remove_small2 = delete_small_components(dilation, 150)
merge = cv2.addWeighted(remove_small1, 0.5, remove_small2, 0.5, 0)
threshold_merge = apply_threshold_with_denoising(merge)
remove_small3 = delete_small_components(threshold_merge, 150)
large_vessels = get_large_vessels(remove_background(remove_small3, mask))
cv2.imshow('Large blood vasculature', large_vessels)
small_vessels = get_small_vessels(remove_background(remove_small3, mask), large_vessels)
cv2.imshow('small vasculature', small_vessels)
I tried the above code.
It produces the vessels - small and large.
I need a way to use this map to recolour using a mean colour value outside each contour such that it blends in when put back.
Is there a way to just identify the segment - colour it and then merge back.

How I can detect recognize text in а shape

Need your help. Now I'm writing python script to recognize text in a shape. This shape can be captured from RTSP (IP Camera) at any angle.
For the example see attached file. My code is here, but coords to crop rotated shape is sets manually
import cv2
import numpy as np
def main():
fn = cv2.VideoCapture("rtsp://admin:Admin123-#172.16.10.254")
flag, img = fn.read()
cnt = np.array([
[[64, 49]],
[[122, 11]],
[[391, 326]],
[[308, 373]]
])
print("shape of cnt: {}".format(cnt.shape))
rect = cv2.minAreaRect(cnt)
print("rect: {}".format(rect))
box = cv2.boxPoints(rect)
box = np.int0(box)
print("bounding box: {}".format(box))
cv2.drawContours(img, [box], 0, (0, 255, 0), 2)
img_crop, img_rot = crop_rect(img, rect)
print("size of original img: {}".format(img.shape))
print("size of rotated img: {}".format(img_rot.shape))
print("size of cropped img: {}".format(img_crop.shape))
new_size = (int(img_rot.shape[1]/2), int(img_rot.shape[0]/2))
img_rot_resized = cv2.resize(img_rot, new_size)
new_size = (int(img.shape[1]/2)), int(img.shape[0]/2)
img_resized = cv2.resize(img, new_size)
cv2.imshow("original contour", img_resized)
cv2.imshow("rotated image", img_rot_resized)
cv2.imshow("cropped_box", img_crop)
# cv2.imwrite("crop_img1.jpg", img_crop)
cv2.waitKey(0)
def crop_rect(img, rect):
# get the parameter of the small rectangle
center = rect[0]
size = rect[1]
angle = rect[2]
center, size = tuple(map(int, center)), tuple(map(int, size))
# get row and col num in img
height, width = img.shape[0], img.shape[1]
print("width: {}, height: {}".format(width, height))
M = cv2.getRotationMatrix2D(center, angle, 1)
img_rot = cv2.warpAffine(img, M, (width, height))
img_crop = cv2.getRectSubPix(img_rot, size, center)
return img_crop, img_rot
if __name__ == "__main__":
main()
example pic

You may start with the example in the following post.
The code sample detects the license plate, and it also detects your "shape" with text.
After detecting the "shape" with the text, you may use the following stages:
Apply threshold the cropped area.
Find contours, and find the contour with maximum area.
Build a mask, and mask area outside the contour (like in the license plate example).
Use minAreaRect (as fmw42 commented), and get the angle of the rectangle.
Rotate the cropped area (by angle+90 degrees).
Apply OCR using pytesseract.image_to_string.
Here is the complete code:
import cv2
import numpy as np
import imutils
import pytesseract
pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files\Tesseract-OCR\tesseract.exe' # I am using Windows
# Read the input image
img = cv2.imread('Admin123.jpg')
# Reused code:
# https://stackoverflow.com/questions/60977964/pytesseract-not-recognizing-text-as-expected/60979089#60979089
################################################################################
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) #convert to grey scale
gray = cv2.bilateralFilter(gray, 11, 17, 17)
edged = cv2.Canny(gray, 30, 200) #Perform Edge detection
cnts = cv2.findContours(edged.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
cnts = sorted(cnts, key = cv2.contourArea, reverse = True)[:10]
screenCnt = None
# loop over our contours
for c in cnts:
# approximate the contour
peri = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.018 * peri, True)
# if our approximated contour has four points, then
# we can assume that we have found our screen
if len(approx) == 4:
screenCnt = approx
break
# Masking the part other than the "shape"
mask = np.zeros(gray.shape,np.uint8)
new_image = cv2.drawContours(mask,[screenCnt],0,255,-1,)
new_image = cv2.bitwise_and(img,img,mask=mask)
# Now crop
(x, y) = np.where(mask == 255)
(topx, topy) = (np.min(x), np.min(y))
(bottomx, bottomy) = (np.max(x), np.max(y))
cropped = gray[topx:bottomx+1, topy:bottomy+1]
################################################################################
# Apply threshold the cropped area
_, thresh = cv2.threshold(cropped, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
# Find contours
cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
cnts = imutils.grab_contours(cnts)
# Get contour with maximum area
c = max(cnts, key=cv2.contourArea)
# Build a mask (same as the code above)
mask = np.zeros(cropped.shape, np.uint8)
new_cropped = cv2.drawContours(mask, [c], 0, 255, -1)
new_cropped = cv2.bitwise_and(cropped, cropped, mask=mask)
# Draw green rectangle for testing
test = cv2.cvtColor(new_cropped, cv2.COLOR_GRAY2BGR)
cv2.drawContours(test, [c], -1, (0, 255, 0), thickness=2)
# Use minAreaRect as fmw42 commented
rect = cv2.minAreaRect(c)
angle = rect[2] # Get angle of the rectangle
# Rotate the cropped rectangle.
rotated_cropped = imutils.rotate(new_cropped, angle + 90)
# Read the text in the "shape"
text = pytesseract.image_to_string(rotated_cropped, config='--psm 3')
print("Extracted text is:\n\n", text)
# Show images for testing:
cv2.imshow('cropped', cropped)
cv2.imshow('thresh', thresh)
cv2.imshow('test', test)
cv2.imshow('rotated_cropped', rotated_cropped)
cv2.waitKey(0)
cv2.destroyAllWindows()
OCR output result:
AB12345
DEPARTMENT OF
INFORMATION
COMMUNICATION
TECHNOLOGY
cropped:
thresh:
test:
rotated_cropped:

Separate objects countours with OpenCV

I have been working with OpenCV in order to detect an squared obstacle. So far this is the image I get after applying filters and canny.
The obstacle I am trying to identify is the horizontal one, the three vertical rectangles are guide lines on the floor.My goal is to keep only the horizontal rectangle, separating it from the others, but after applying find Contours I only get I single object that includes all the shapes.This is the code I have been using in order to fin only the biggest rectangle by their area:
# find the biggest countour (c) by the area
if contours != 0:
if not contours:
print("Empty")
else:
bigone = max(contours, key=cv2.contourArea) if max else None
area = cv2.contourArea(bigone)
if area > 10000:
x, y, w, h = cv2.boundingRect(bigone)
cv2.rectangle(img, (x, y), (x+w, y+h), (0, 0, 255), 2)
cv2.putText(img, "Obstacle", (x+w/2, y-20),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)
pts = np.array(
[[[x, y], [x+w, y], [x+w, y+h], [x, y+h]]], dtype=np.int32)
cv2.fillPoly(mask, pts, (255, 255, 255))
#values = img[np.where((mask == (255, 255, 255)).all(axis=2))]
res = cv2.bitwise_and(img, mask) # View only the obstacle
obs_area = w*h
print(obs_area)
if obs_area <= 168000:
command_publisher.publish("GO")
cv2.putText(
img, "GO", (380, 400), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 255, 255), 1)
else:
command_publisher.publish("STOP")
cv2.putText(img, "STOP", (380, 400),
cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 255, 255), 1)
# show the output image
cv2.imshow("Image", img)
cv2.waitKey(1)
And this is the result I am getting:
Is there a way of separating my obstacle from the lines on the floor with some kind of filter or algorithm?
Here is an example image to work with:

Here is one way to do that using Python/OpenCV.
- Read the input
- Convert to HSV and extract only the saturation channel (black/white/gray have zero saturation)
- Threshold
- Apply morphology open and close to remove the extranous white regions
- Get the contour and approximate to simple polygon
- Draw the polygon on the input
- Save the results
Input:
import cv2
import numpy as np
# read image
img = cv2.imread('board.png')
# convert to HSV and extract saturation channel
sat = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)[:,:,1]
# threshold
thresh = cv2.threshold(sat, 90, 255, 0)[1]
# apply morphology close to fill interior regions in mask
kernel = np.ones((7,7), np.uint8)
morph = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
kernel = np.ones((13,13), np.uint8)
morph = cv2.morphologyEx(morph, cv2.MORPH_CLOSE, kernel)
# get contours (presumably only 1) and fit to simple polygon (quadrilateral)
cntrs = cv2.findContours(morph, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cntrs = cntrs[0] if len(cntrs) == 2 else cntrs[1]
c = cntrs[0]
peri = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.05 * peri, True)
# draw polygon on input
result = img.copy()
cv2.polylines(result, [np.int32(approx)], True, (0,0,255), 1, cv2.LINE_AA)
# write result to disk
cv2.imwrite("board_saturation.png", sat)
cv2.imwrite("board_thresh.png", thresh)
cv2.imwrite("board_morph.png", morph)
cv2.imwrite("board_contour.png", result)
# display it
cv2.imshow("IMAGE", img)
cv2.imshow("SAT", sat)
cv2.imshow("THRESH", thresh)
cv2.imshow("MORPH", morph)
cv2.imshow("RESULT", result)
cv2.waitKey(0)
Saturation channel image:
Thresholded image:
Morphology cleaned image:
Contour on input:

In your image the problem seems white rectangles. My approach is checking each line and if line consist many pixels which are close to white(255,255,255) then make the line black.
Here is my code:
import cv2
import numpy as np
import random as rng
img=cv2.imread("/ur/image/directory/obstacle.png")
height, width, channels = img.shape
cv2.imshow('Source',img)
# Check each line and eliminate white rectangles(if line consist white pixels more than limit)
for x in range(0,height):
white_counter = 0
for y in range(0,width):
if img[x,y,0] >= 180 and img[x,y,1] >= 180 and img[x,y,2] >= 180:
white_counter = white_counter + 1
if white_counter>10:
for y in range(0,width):
img[x,y,0] = 0
img[x,y,1] = 0
img[x,y,2] = 0
cv2.imshow('Elimination White Rectangles', img)
# Find contours and draw rectangle for each
src_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
threshold = 300
canny_output = cv2.Canny(src_gray, threshold, threshold * 2)
contours, _ = cv2.findContours(canny_output, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours_poly = [None]*len(contours)
boundRect = [None]*len(contours)
for i, c in enumerate(contours):
contours_poly[i] = cv2.approxPolyDP(c, 3, True)
boundRect[i] = cv2.boundingRect(contours_poly[i])
rng.seed(12345)
drawing = np.zeros((canny_output.shape[0], canny_output.shape[1], 3), dtype=np.uint8)
for i in range(len(contours)):
color = (rng.randint(0,256), rng.randint(0,256), rng.randint(0,256))
cv2.rectangle(drawing, (int(boundRect[i][0]), int(boundRect[i][1])), \
(int(boundRect[i][0]+boundRect[i][2]), int(boundRect[i][1]+boundRect[i][3])), color, 2)
cv2.imshow('Output', drawing)
cv2.waitKey(0)
cv2.destroyAllWindows()
Eliminate White Rectangles:
Result:

Removing background color from image opencv python

I have many images of specimen which have uncontrollable background color. Some of them have black background. Some of them have white background. Some of them have green background, etc.
I would like to remove these background color of a given image where the object in the image is just only one specimen. I try this code but it does not work as i expect.
def get_holes(image, thresh):
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
im_bw = cv2.threshold(gray, thresh, 255, cv2.THRESH_BINARY)[1]
im_bw_inv = cv2.bitwise_not(im_bw)
_, contour, _ = cv2.findContours(im_bw_inv, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)
for cnt in contour:
cv2.drawContours(im_bw_inv, [cnt], 0, 255, -1)
nt = cv2.bitwise_not(im_bw)
im_bw_inv = cv2.bitwise_or(im_bw_inv, nt)
return im_bw_inv
def remove_background(image, thresh, scale_factor=.25, kernel_range=range(1, 15), border=None):
border = border or kernel_range[-1]
holes = get_holes(image, thresh)
small = cv2.resize(holes, None, fx=scale_factor, fy=scale_factor)
bordered = cv2.copyMakeBorder(small, border, border, border, border, cv2.BORDER_CONSTANT)
for i in kernel_range:
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2*i+1, 2*i+1))
bordered = cv2.morphologyEx(bordered, cv2.MORPH_CLOSE, kernel)
unbordered = bordered[border: -border, border: -border]
mask = cv2.resize(unbordered, (image.shape[1], image.shape[0]))
fg = cv2.bitwise_and(image, image, mask=mask)
return fg
file = your_file_location
img = cv2.imread(file)
nb_img = dm.remove_background(img, 255)
These are some example images
May i have your suggestions?

Here's a simple approach with the assumption that there is only one specimen per image.
Kmeans color quantization. We load the image then perform Kmeans color quantization to segment the image into a specified cluster of colors. For instance with clusters=4, the image will be labeled into four colors.
Obtain binary image. Convert to grayscale, Gaussian blur, adaptive threshold.
Draw largest enclosing circle onto mask. Find contours, sort for largest contour using contour area filtering then draw the largest enclosing circle onto a mask using cv2.minEnclosingCircle.
Bitwise-and. Since we have isolated the desired sections to extract, we simply bitwise-and the mask and input image
Input image -> Kmeans -> Binary image
Detected largest enclosing circle -> Mask -> Result
Here's the output for the second image
Input image -> Kmeans -> Binary image
Detected largest enclosing circle -> Mask -> Result
Code
import cv2
import numpy as np
# Kmeans color segmentation
def kmeans_color_quantization(image, clusters=8, rounds=1):
h, w = image.shape[:2]
samples = np.zeros([h*w,3], dtype=np.float32)
count = 0
for x in range(h):
for y in range(w):
samples[count] = image[x][y]
count += 1
compactness, labels, centers = cv2.kmeans(samples,
clusters,
None,
(cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10000, 0.0001),
rounds,
cv2.KMEANS_RANDOM_CENTERS)
centers = np.uint8(centers)
res = centers[labels.flatten()]
return res.reshape((image.shape))
# Load image and perform kmeans
image = cv2.imread('2.jpg')
original = image.copy()
kmeans = kmeans_color_quantization(image, clusters=4)
# Convert to grayscale, Gaussian blur, adaptive threshold
gray = cv2.cvtColor(kmeans, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (3,3), 0)
thresh = cv2.adaptiveThreshold(blur,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV,21,2)
# Draw largest enclosing circle onto a mask
mask = np.zeros(original.shape[:2], dtype=np.uint8)
cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)
for c in cnts:
((x, y), r) = cv2.minEnclosingCircle(c)
cv2.circle(image, (int(x), int(y)), int(r), (36, 255, 12), 2)
cv2.circle(mask, (int(x), int(y)), int(r), 255, -1)
break
# Bitwise-and for result
result = cv2.bitwise_and(original, original, mask=mask)
result[mask==0] = (255,255,255)
cv2.imshow('thresh', thresh)
cv2.imshow('result', result)
cv2.imshow('mask', mask)
cv2.imshow('kmeans', kmeans)
cv2.imshow('image', image)
cv2.waitKey()

How to detect and extract signature from an image with OpenCV?

I am importing the attached image. After importing the image, I want to remove horizontal lines, detect the signature and then extract it, create rectangle around signature, crop the rectangle and save it. I am struggling to identify entire region of a signature as one contour or a group of contours.
I have already tried findcontour and then various ways to detect signature region. Please refer the code below.
Python Script:
imagePath
#read image
image = cv2.imread(imagePath,cv2.COLOR_BGR2RGB)
#Convert to greyscale
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY) # grayscale
#Apply threshold
ret,thresh1 = cv2.threshold(gray, 0, 255,cv2.THRESH_OTSU|cv2.THRESH_BINARY_INV)
plt.imshow(thresh1,cmap = 'gray')
#preprocessing
rect_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (15,15))
dilation = cv2.dilate(thresh1, rect_kernel, iterations = 1)
plt.imshow(dilation,cmap = 'gray')
#Detect contours
contours, hierarchy = cv2.findContours(dilation, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
contours[0]
height, width, _ = image.shape
min_x, min_y = width, height
max_x = max_y = 0
for contour, hier in zip(contours, hierarchy):
(x,y,w,h) = cv2.boundingRect(contour)
min_x, max_x = min(x, min_x), max(x+w, max_x)
min_y, max_y = min(y, min_y), max(y+h, max_y)
if w > 80 and h > 80:
cv2.rectangle(frame, (x,y), (x+w,y+h), (255, 0, 0), 2)
if max_x - min_x > 0 and max_y - min_y > 0:
fin=cv2.rectangle(image, (min_x, min_y), (max_x, max_y), (255, 0, 0), 2)
plt.imshow(fin)
final=cv2.drawContours(image, contours,-1,(0,0,255),6)
plt.imshow(final,cmap = 'gray')
Final objective is to create rectangle around entire signature
Trying to generalize on the other image:

Instead of removing the horizontal lines, it may be easier to perform HSV color thresholding. The idea is to isolate the signature onto a mask and then extract it. We convert the image to HSV format then use a lower/upper color threshold to generate a mask
lower = np.array([90, 38, 0])
upper = np.array([145, 255, 255])
mask = cv2.inRange(image, lower, upper)
Mask
To detect the signature, we can get the combined bounding box for all of the contours with np.concatenate() then use cv2.boundingRect() to obtain the coordinates
Now that we have the bounding box coordinates, we can use Numpy slicing to crop and extract the ROI
import numpy as np
import cv2
# Load image and HSV color threshold
image = cv2.imread('1.jpg')
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
lower = np.array([90, 38, 0])
upper = np.array([145, 255, 255])
mask = cv2.inRange(hsv, lower, upper)
result = cv2.bitwise_and(image, image, mask=mask)
result[mask==0] = (255, 255, 255)
# Find contours on extracted mask, combine boxes, and extract ROI
cnts = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
cnts = np.concatenate(cnts)
x,y,w,h = cv2.boundingRect(cnts)
cv2.rectangle(image, (x, y), (x + w, y + h), (36,255,12), 2)
ROI = result[y:y+h, x:x+w]
cv2.imshow('result', result)
cv2.imshow('mask', mask)
cv2.imshow('image', image)
cv2.imshow('ROI', ROI)
cv2.waitKey()
Note: The lower/upper color ranges were obtained from choosing the correct upper and lower HSV boundaries for color detection with cv::inRange (OpenCV)