二值化邏輯運算¶
概要¶
本節， 阿凱創建了兩個二值化圖像， 演示了各種二值化運算對應的效果。并給出了詳細的二值化邏輯運算對應的真值表(Truth Table)。
keywords 二值化 Binary Bool 邏輯運算
1. 創建二值化圖像¶
首先我們定義兩個圖形, 一個是正方形，另外一個為圓形。
中間白色的區域是1 (灰度值為255)
黑色的區域即為0 (灰度值為0)
圖形1 正方形
rectangle = np.zeros((300, 300), dtype="uint8") cv2.rectangle(rectangle, (25, 25), (275, 275), 255, -1) cv2.imwrite("bitwise_rectangle.png", rectangle)
圖形2 圓形
circle = np.zeros((300, 300), dtype="uint8") cv2.circle(circle, (150, 150), 150, 255, -1) cv2.imwrite("bitwise_circle.png", circle)
完整的代碼如下:
src/create-binary-image.py
''' 創建二值化的矩形還有圓形 ''' import cv2 import numpy as np rectangle = np.zeros((300, 300), dtype="uint8") cv2.rectangle(rectangle, (25, 25), (275, 275), 255, -1) cv2.imwrite("bitwise_rectangle.png", rectangle) circle = np.zeros((300, 300), dtype="uint8") cv2.circle(circle, (150, 150), 150, 255, -1) cv2.imwrite("bitwise_circle.png", circle)
然后我們對其進行邏輯運算。
2. 邏輯非 - not¶
邏輯非其實也相當于反色。 原來是白色的地方變成黑色， 原來是黑色的地方變成白色。
bitwiseNOT = cv2.bitwise_not(circle) cv2.imwrite("bitwise_not_circle.png", bitwiseNOT)
真值表
| A | not A |
|---|---|
| 0 | 1 |
| 1 | 0 |
效果
完整源碼
bitwise_not_circle.py
''' 測試二值化非 ''' import cv2 circle = cv2.imread('bitwise_circle.png', cv2.IMREAD_GRAYSCALE) bitwiseNOT = cv2.bitwise_not(circle) cv2.imwrite("bitwise_not_circle.png", bitwiseNOT)
3. 邏輯與 - and¶
邏輯與經常被用于遮蓋層(MASK), 即去除背景, 選取自己感興趣的區域.
bitwiseAnd = cv2.bitwise_and(rectangle, circle) cv2.imwrite("bitwise_and.png", bitwiseAnd)
真值表
| A | B | A AND B |
|---|---|---|
| 0 | 0 | 0 |
| 0 | 1 | 0 |
| 1 | 0 | 0 |
| 1 | 1 | 1 |
效果
完整源碼
src/bitwise_and.py
''' 二值化圖像邏輯與 ''' import cv2 circle = cv2.imread('bitwise_circle.png', cv2.IMREAD_GRAYSCALE) rectangle = cv2.imread('bitwise_rectangle.png', cv2.IMREAD_GRAYSCALE) bitwiseAnd = cv2.bitwise_and(rectangle, circle) cv2.imwrite("bitwise_and.png", bitwiseAnd)
4. 邏輯或 - or¶
bitwiseOR = cv2.bitwise_or(rectangle, circle) cv2.imwrite("bitwise_or.png", bitwiseOR)
真值表
| A | B | A OR B |
|---|---|---|
| 0 | 0 | 0 |
| 0 | 1 | 1 |
| 1 | 0 | 1 |
| 1 | 1 | 1 |
效果
完整源碼
bitwise_or.py
''' 二值化圖像-或 ''' import cv2 circle = cv2.imread('bitwise_circle.png', cv2.IMREAD_GRAYSCALE) rectangle = cv2.imread('bitwise_rectangle.png', cv2.IMREAD_GRAYSCALE) bitwiseOR = cv2.bitwise_or(rectangle, circle) cv2.imwrite("bitwise_or.png", bitwiseOR)
5. 邏輯與非 - nand¶
bitwiseNAnd = cv2.bitwise_not(bitwiseAnd) cv2.imwrite("bitwise_nand.png", bitwiseNAnd)
真值表
| A | B | A NOT AND B |
|---|---|---|
| 0 | 0 | 1 |
| 0 | 1 | 0 |
| 1 | 0 | 0 |
| 1 | 1 | 0 |
效果
完整源碼
bitwise_nand.py
''' 二值化圖像-與非 ''' import cv2 circle = cv2.imread('bitwise_circle.png', cv2.IMREAD_GRAYSCALE) rectangle = cv2.imread('bitwise_rectangle.png', cv2.IMREAD_GRAYSCALE) bitwiseAnd = cv2.bitwise_and(rectangle, circle) bitwiseNAnd = cv2.bitwise_not(bitwiseAnd) cv2.imwrite("bitwise_nand.png", bitwiseNAnd)
6. 邏輯或非 - nor¶
bitwiseNOR = cv2.bitwise_and(cv2.bitwise_not(rectangle), cv2.bitwise_not(circle)) cv2.imwrite("bitwise_nor.png", bitwiseNOR)
真值表
| A | B | A NOR B |
|---|---|---|
| 0 | 0 | 1 |
| 0 | 1 | 0 |
| 1 | 0 | 0 |
| 1 | 1 | 0 |
效果
7. 邏輯異或 - xor¶
bitwiseXOR = cv2.bitwise_xor(rectangle, circle) cv2.imwrite("bitwise_xor.png", bitwiseXOR)
真值表
| A | B | A OR B |
|---|---|---|
| 0 | 0 | 0 |
| 0 | 1 | 1 |
| 1 | 0 | 1 |
| 1 | 1 | 0 |
效果
完整源碼
bitwise_xor.py
''' 二值化圖像-異或 ''' import cv2 circle = cv2.imread('bitwise_circle.png', cv2.IMREAD_GRAYSCALE) rectangle = cv2.imread('bitwise_rectangle.png', cv2.IMREAD_GRAYSCALE) bitwiseXOR = cv2.bitwise_xor(rectangle, circle) cv2.imwrite("bitwise_xor.png", bitwiseXOR)
8. 邏輯異或非 - xnor¶
bitwiseXNOR = cv2.bitwise_or(bitwiseAnd, bitwiseNOR) cv2.imwrite("bitwise_xnor.png", bitwiseXNOR)
真值表
| A | B | A OR B |
|---|---|---|
| 0 | 0 | 1 |
| 0 | 1 | 0 |
| 1 | 0 | 0 |
| 1 | 1 | 1 |
效果
完整源碼
bitwise_xnor.py
''' 二值化圖像抑或非 ''' import cv2 circle = cv2.imread('bitwise_circle.png', cv2.IMREAD_GRAYSCALE) rectangle = cv2.imread('bitwise_rectangle.png', cv2.IMREAD_GRAYSCALE) bitwiseAnd = cv2.bitwise_and(rectangle, circle) bitwiseNOR = cv2.bitwise_not(cv2.bitwise_or(rectangle, circle)) bitwiseXNOR = cv2.bitwise_or(bitwiseAnd, bitwiseNOR) cv2.imwrite("bitwise_xnor.png", bitwiseXNOR)
