#0021

Convert RGB Image To Grayscale

EasyML12 min10 XP

Problem

Given an RGB image array of shape (height, width, 3), return a grayscale image using 0.299*R + 0.587*G + 0.114*B.

Why This Matters

Many vision pipelines start by understanding image arrays. This problem makes channels, height, width, and pixel values concrete.

Function Signature

def rgb_to_grayscale(image):

Examples

Example 1

Inputone red pixel [255, 0, 0]

Outputabout 76.245

Red contributes 0.299 of its intensity to luminance.

Constraints

Input shape is (height, width, 3).
Return shape is (height, width).
Do not average channels equally.

CodePython

import numpy as np

def rgb_to_grayscale(image):
    """
    Input example: one red pixel [255, 0, 0]
    Return: about 76.245
    Return the value from the function so the tests can compare it. Do not print unless the prompt asks.
    """
    # write your code here
    return image

import numpy as np

def rgb_to_grayscale(image):
    """
    Input example: one red pixel [255, 0, 0]
    Return: about 76.245
    Return the value from the function so the tests can compare it. Do not print unless the prompt asks.
    """
    # write your code here
    return image

Visible browser tests run here when available.

Testcases1 visible / 4 hidden categories

Known primary colors

Inputred, green, blue pixels

Expectedweighted grayscale intensities

Green contributes most to perceived brightness in this formula.

Hidden Test Categories

Two-row imageBlack imageWhite imageFloating point pixel values