Asked in: Meesho
Image of the Question
All Testcases Passed ✔
Solution
def closestColor(pixels):
# Write your code here
color = {"Black":(0,0,0),"White":(255,255,255), "Red":(255,0,0),"Green":(0,255,0),"Blue":(0,0,255)}
ans = []
// ... rest of solution available after purchaseExplanation
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To solve this problem, the first step is to understand how a 24-bit binary string encodes color. Since each of the red, green, and blue components is represented by 8 bits, the 24-bit string is divided into three equal segments of 8 bits each. These segments represent the intensity of the red, green, and blue color channels, respectively. The values for each component range from 0 to 255. The binary string must be converted into three integers — one for each channel — to work with them numerically.
After extracting the RGB components, the goal is to determine which one of the five pure colors — Red, Green, Blue, Black, or White — is closest to the given color using Euclidean distance in 3D RGB space. This is a standard geometric operation, where the three axes represent the intensity of red, green, and blue. The Euclidean distance formula between two RGB colors (r1, g1, b1) and (r2, g2, b2) is calculated using the square root of the sum of the squared differences of their respective components.
Since the goal is to find the closest pure color, define those five target colors in RGB format:
- Red: (255, 0, 0)
- Green: (0, 255, 0)
- Blue: (0, 0, 255)
- Black: (0, 0, 0)
- White: (255, 255, 255)
Each of these has only specific components at their maximum intensity while the others are zero, except for white, which has all components at maximum.
Once the input RGB color is converted from binary, the approach is to compute the Euclidean distance from it to each of the five predefined pure colors. This results in five distance values. The smallest of these distances indicates the closest pure color.
To implement this strategy in your mind, follow these conceptual steps:
1. **Binary Parsing**: Break down the 24-bit binary string into three parts. Each 8-bit segment corresponds to a color component. Convert each segment from binary to a decimal integer. This gives you the actual RGB values for the pixel.
2. **Distance Function**: For each of the five reference colors, calculate the Euclidean distance to the input color using the formula: square root of the sum of the squared differences in each RGB channel.
3. **Comparison and Selection**: After computing all five distances, identify the smallest one. The color that corresponds to this minimum distance is the closest pure color to the given pixel.
It’s also worth noting that since all five reference colors are known and fixed, and the computation is relatively light (involving five distance calculations), this approach is efficient and doesn't require any advanced optimization.
Additionally, if two distances are equal, a decision must be made on how to break the tie. Although it's unlikely due to the continuous nature of RGB values, if this scenario arises, a consistent tie-breaking rule such as alphabetical order or priority order can be adopted.
Another important aspect to consider is edge cases. Make sure the input binary string is valid — it must be exactly 24 bits long and consist only of 0s and 1s. If there is any chance of malformed input in a broader implementation context, appropriate validation should be applied.
In terms of practical use, this method is effectively mapping a full-range RGB color to the nearest “simplified” pure color. This can be useful in various scenarios such as image reduction, stylization, or preprocessing data for simpler machine learning models.
To summarize, the strategy relies on:
- Proper decoding of a binary-encoded RGB color.
- Understanding and applying the Euclidean distance metric in 3D space.
- Comparing the pixel to a known, limited set of reference colors.
- Selecting the color with the minimum distance as the closest match.
This conceptual approach ensures accuracy, clarity, and efficiency in determining the closest pure color to any RGB input.
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