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Commit f5e6d58

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fix to ch20
1 parent 9798606 commit f5e6d58

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12 files changed

+26
-22
lines changed

12 files changed

+26
-22
lines changed
File renamed without changes.

‎ch13-颜色空间转换/13.VideoCapture_blue_object.py‎ renamed to ‎ch13-颜色空间转换/2.物体跟踪_blue_object.py‎

Lines changed: 11 additions & 9 deletions
Original file line numberDiff line numberDiff line change
@@ -18,32 +18,34 @@
1818
'''
1919

2020
cap = cv2.VideoCapture(0)
21+
ret = cap.set(3, 640)
22+
ret = cap.set(4, 480)
2123
while True:
2224
# 获取每一帧
2325
ret, frame = cap.read()
2426
# 换到 HSV
2527
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
2628
# 定蓝色的阈值
27-
# lower_blue = np.array([110, 50, 50])
28-
# upper_blue = np.array([130, 255, 255])
29+
lower_blue = np.array([110, 50, 50])
30+
upper_blue = np.array([130, 255, 255])
2931

3032
# 黑色
31-
lower_black = np.array([0, 0, 0])
32-
upper_black = np.array([180, 255, 30])
33+
# lower_black = np.array([0, 0, 0])
34+
# upper_black = np.array([180, 255, 30])
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3436
# 根据阈值构建掩模
35-
# mask = cv2.inRange(hsv, lower_blue, upper_blue)
36-
mask = cv2.inRange(hsv, lower_black, upper_black)
37+
mask = cv2.inRange(hsv, lower_blue, upper_blue)
38+
# mask = cv2.inRange(hsv, lower_black, upper_black)
3739
# 对原图像和掩模位运算
3840
res = cv2.bitwise_and(frame, frame, mask=mask)
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4042
# 显示图像
4143
cv2.imshow('frame', frame)
4244
cv2.imshow('mask', mask)
4345
cv2.imshow('res', res)
44-
k = cv2.waitKey(0) # & 0xFF
45-
if k == 27:
46+
47+
k = cv2.waitKey(1) # & 0xFF
48+
if k == ord('q'):
4649
break
4750
# 关闭窗口
4851
cv2.destroyAllWindows()
49-

‎ch14-几何变换/14.resize.py‎ renamed to ‎ch14-几何变换/14.1.扩展缩放resize.py‎

Lines changed: 5 additions & 8 deletions
Original file line numberDiff line numberDiff line change
@@ -20,14 +20,11 @@
2020

2121
# OR
2222
# 我们直接设置输出图像的尺寸 所以不用设置缩放因子
23-
height, width = img.shape[:2]
24-
res = cv2.resize(img, (2 * width, 2 * height), interpolation=cv2.INTER_CUBIC)
23+
# height, width = img.shape[:2]
24+
# res = cv2.resize(img, (2 * width, 2 * height), interpolation=cv2.INTER_CUBIC)
2525

26-
while True:
27-
cv2.imshow('resize', res)
28-
cv2.imshow('src img', img)
26+
cv2.imshow('resize', res)
27+
cv2.imshow('src img', img)
2928

30-
key = cv2.waitKey(1)
31-
if key == ord("q"):
32-
break
29+
cv2.waitKey(0)
3330
cv2.destroyAllWindows()

‎ch14-几何变换/14.2平移.py‎

Lines changed: 3 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -13,6 +13,8 @@
1313
import numpy as np
1414

1515
cap = cv2.VideoCapture(0)
16+
ret = cap.set(3, 640)
17+
ret = cap.set(4, 480)
1618
while True:
1719
# 获取每一帧
1820
ret, frame = cap.read()
@@ -32,7 +34,7 @@
3234
cv2.imshow('mask', mask)
3335
cv2.imshow('res', res)
3436

35-
k = cv2.waitKey(0) # & 0xFF
37+
k = cv2.waitKey(1) # & 0xFF
3638
if k == ord('q'):
3739
break
3840
# 关 窗口
File renamed without changes.
File renamed without changes.
File renamed without changes.
File renamed without changes.

‎ch16-图像平衡/图像模糊-平均.py‎

Lines changed: 3 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -19,7 +19,7 @@
1919
'''
2020
现在把卷积核换成 斯核 简单来 方框不变 将原来每个方框的值是 相等的 现在 的值是符合 斯分布的 方框中心的值最大 其余方框根据 离中心元素的 离 减 构成一个 斯小山包。原来的求平均数现在变成求 加权平均数 全就是方框 的值 。
2121
'''
22-
# 0 是指根据窗口大小 5,5 来计算高斯函数标准差
22+
# 0 是指根据窗口大小 (5,5) 来计算高斯函数标准差
2323
blur = cv2.GaussianBlur(img, (5, 5), 0) # 高斯模糊
2424

2525
'''
@@ -43,10 +43,11 @@
4343
因为边界处的灰度值变化比较大。
4444
'''
4545

46+
#16.4 双边滤波
4647
# cv2.bilateralFilter(src, d, sigmaColor, sigmaSpace)
4748
# d – Diameter of each pixel neighborhood that is used during filtering. # If it is non-positive, it is computed from sigmaSpace
4849
# 9 域直径 两个 75 分别是空 斯函数标准差 灰度值相似性 斯函数标准差
49-
blur = cv2.bilateralFilter(img, 9, 75, 75)
50+
# blur = cv2.bilateralFilter(img, 9, 75, 75)
5051

5152
plt.subplot(121), plt.imshow(img), plt.title('Original')
5253
plt.xticks([]), plt.yticks([])

‎ch18-图像梯度/一个重要的事.py‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -22,7 +22,7 @@
2222
import numpy as np
2323
from matplotlib import pyplot as plt
2424

25-
img = cv2.imread('../data/box.png', 0)
25+
img = cv2.imread('../data/box.jpg', 0)
2626

2727
# Output dtype = cv2.CV_8U
2828
sobelx8u = cv2.Sobel(img, cv2.CV_8U, 1, 0, ksize=5)

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