How do I get all the pixels in an image using opencv?

Question

I'm using opencv right now to display different colours by masking everything but that colour. What I want to achieve is to get all the pixel coordinates that are green, black etc. Some screenshots:

the first image is of a black line and the second image is of a green square. I would like to be able to record the pixel coordinates that have black or green on them. Here's the main code:

import sys
sys.path.append("\Python\Opencv_codes")
import line_following_testing as lf

from line_following_testing import lower_green as lg
from line_following_testing import upper_green as ug

from line_following_testing import lower_black as lb
from line_following_testing import upper_black as ub

import numpy as np
from time import sleep as wait
import cv2
from PIL import Image

green_boundaries = [
    ([75, 52, 60], [106, 255, 255])
    ]

cap = cv2.VideoCapture(0)

while True:
    _, img = cap.read()

    lf.percentage_calculator(green_boundaries, "green", img)

    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)

    #cropping and getting the hsv value and converting it to see of the value is their
    mask = cv2.inRange(hsv, lg, ug)
    green_result = cv2.bitwise_and(hsv, hsv, mask=mask)

    #cropping and getting the hsv value and converting it to see of the value is their
    mask1 = cv2.inRange(hsv, lb, ub)
    black_result = cv2.bitwise_and(hsv, hsv, mask=mask1)
    black_canny = cv2.Canny(black_result, 700,900)

    cv2.imshow("green", green_result)
    cv2.imshow('black', black_result)
    cv2.imshow("hsv", hsv)
    cv2.imshow('img', img)
    cv2.imshow('black_canny', black_canny)


    k = cv2.waitKey(30) & 0xff
    if k==27:
        break

and here is the imported script:

import numpy as np
from time import sleep as wait
import cv2
from PIL import Image

lower_green = np.array([75, 52, 60])
upper_green = np.array([106, 255, 255])

lower_black = np.array([0,0,0])
upper_black = np.array([180,255,45])

def percentage_calculator(boundaries, colour, image):

    for(lower, upper) in boundaries:

        lower = np.array(lower)
        upper = np.array(upper)

        # finds colors in boundaries a applies a mask
        mask = cv2.inRange(image, lower, upper)
        output = cv2.bitwise_and(image, image, mask = mask)

        tot_pixel = image.size
        pixel = np.count_nonzero(output)
        percentage = round(pixel * 100 / tot_pixel, 2)

        print(colour + " pixels: " + str(pixel))
        print("Total pixels: " + str(tot_pixel))
        print("Percentage of " + colour + " pixels: " + str(percentage) + "%")

New code:

import sys
sys.path.append("\Python\Opencv_codes")
import line_following_testing as lf

from line_following_testing import lower_green as lg
from line_following_testing import upper_green as ug

from line_following_testing import lower_black as lb
from line_following_testing import upper_black as ub

import numpy as np
from time import sleep as wait
import cv2

green_boundaries = [
    ([75, 52, 60], [106, 255, 255])
    ]

cap = cv2.VideoCapture(0)

while True:
    _, img = cap.read()

    lf.percentage_calculator(green_boundaries, "green", img)

    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)

    #cropping and getting the hsv value and converting it to see of the value is their
    mask = cv2.inRange(hsv, lg, ug)
    green_result = cv2.bitwise_and(hsv, hsv, mask=mask)

    #cropping and getting the hsv value and converting it to see of the value is their
    mask1 = cv2.inRange(hsv, lb, ub)
    black_result = cv2.bitwise_and(hsv, hsv, mask=mask1)
    x, y = mask1[-5:].nonzero()
    x_min = min(x)
    x_max = max(x)
    y_min = min(y)
    y_max = max(y)

    center_coords_min = (x_min, y_min)
    center_coords_max = (x_max, y_max)

    cv2.circle(img, center_coords_min, 10, (0, 0, 255), 2)
    cv2.circle(img, center_coords_max, 10, (255, 0, 0), 2)
    print(x_min, y_min)
    print(x_max, y_max)
    black_canny = cv2.Canny(black_result, 700,900)

    cv2.imshow("green", green_result)
    cv2.imshow('black', black_result)
    cv2.imshow("hsv", hsv)
    cv2.imshow('img', img)
    cv2.imshow('black_canny', black_canny)


    k = cv2.waitKey(30) & 0xff
    if k==27:
        break

Answer 1

Using this code get all colors hsv !

import cv2
import numpy as np

img_path = r"img_path"


def nothing(x):
    pass


def crop_image_contours(image_copy):
    cropped_image = None
    # convert the image to grayscale format
    img_gray = cv2.cvtColor(image_copy, cv2.COLOR_BGR2GRAY)

    # apply binary thresholding
    ret, thresh = cv2.threshold(img_gray, 100, 255, cv2.THRESH_BINARY)
    contours1, hierarchy1 = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

    buckle = []
    # computes the bounding box for the contour, and draws it on the frame,
    for contour in contours1:
        # Find bounding rectangles
        if cv2.contourArea(contour) > 100:
            box = cv2.minAreaRect(contour)
            box = cv2.boxPoints(box)
            box = np.array(box, dtype="int")
            x, y, w, h = cv2.boundingRect(contour)
            cropped_image = image_copy[y:y+h, x:x+w]
            buckle.append(box)
    return cropped_image


def crop_img(image, coord):
    image_cpy = image.copy()
    cropped_image = image_cpy[coord[1]:coord[3], coord[0]:coord[2]]
    return cropped_image


def check_sum(image, coord, hsv_value):
    c_image = crop_img(image, coord)
    [[l_h, l_s, l_v], [u_h, u_s, u_v]] = hsv_value
    lower_range = np.array([l_h, l_s, l_v])
    upper_range = np.array([u_h, u_s, u_v])
    img_crop = cv2.cvtColor(c_image, cv2.COLOR_BGR2HSV)
    val = cv2.inRange(img_crop, lower_range, upper_range)
    hsv_score = val.sum()
    # print(hsv_score)


useCamera = False

cv2.namedWindow('image')

# create trackbars for color change
cv2.createTrackbar('HMin', 'image', 0, 179, nothing)  # Hue is from 0-179 for Opencv
cv2.createTrackbar('SMin', 'image', 0, 255, nothing)
cv2.createTrackbar('VMin', 'image', 0, 255, nothing)
cv2.createTrackbar('HMax', 'image', 0, 179, nothing)
cv2.createTrackbar('SMax', 'image', 0, 255, nothing)
cv2.createTrackbar('VMax', 'image', 0, 255, nothing)

# Set default value for MAX HSV trackbars.
cv2.setTrackbarPos('HMax', 'image', 179)
cv2.setTrackbarPos('SMax', 'image', 255)
cv2.setTrackbarPos('VMax', 'image', 255)

# Initialize to check if HSV min/max value changes
hMin = sMin = vMin = hMax = sMax = vMax = 0
phMin = psMin = pvMin = phMax = psMax = pvMax = 0

# Output Image to display
if useCamera:
    cap = cv2.VideoCapture(0)
    # Wait longer to prevent freeze for videos.
    waitTime = 330
else:
    img = cv2.imread(img_path)
    output = img
    waitTime = 33

while True:

    if useCamera:
        # Capture frame-by-frame
        ret, img = cap.read()
        output = img

    # get current positions of all trackbars
    hMin = cv2.getTrackbarPos('HMin', 'image')
    sMin = cv2.getTrackbarPos('SMin', 'image')
    vMin = cv2.getTrackbarPos('VMin', 'image')

    hMax = cv2.getTrackbarPos('HMax', 'image')
    sMax = cv2.getTrackbarPos('SMax', 'image')
    vMax = cv2.getTrackbarPos('VMax', 'image')

    # Set minimum and max HSV values to display
    lower = np.array([hMin, sMin, vMin])
    upper = np.array([hMax, sMax, vMax])

    # Create HSV Image and threshold into a range.
    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
    mask = cv2.inRange(hsv, lower, upper)
    output = cv2.bitwise_and(img, img, mask=mask)

    # Print if there is a change in HSV value
    if((phMin != hMin) | (psMin != sMin) | (pvMin != vMin) | (phMax != hMax) | (psMax != sMax) | (pvMax != vMax)):
        print("[[ %d , %d, %d],[%d , %d, %d]]" % (hMin, sMin, vMin, hMax, sMax,vMax))
        phMin = hMin
        psMin = sMin
        pvMin = vMin
        phMax = hMax
        psMax = sMax
        pvMax = vMax
        clr_val = [[phMin, psMin, pvMin], [phMax, psMax, pvMax]]
    cv2.imshow('image', output)
    # Wait longer to prevent freeze for videos.
    if cv2.waitKey(waitTime) & 0xFF == ord('q'):
        break

# Release resources
if useCamera:
    cap.release()
cv2.destroyAllWindows()