fix to ch20

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

@@ -18,32 +18,34 @@
 '''
 
 cap = cv2.VideoCapture(0)
+ret = cap.set(3, 640)
+ret = cap.set(4, 480)
 while True:
     # 获取每一帧
     ret, frame = cap.read()
     # 换到 HSV
     hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
     # 定蓝色的阈值
-    # lower_blue = np.array([110, 50, 50])
-    # upper_blue = np.array([130, 255, 255])
+    lower_blue = np.array([110, 50, 50])
+    upper_blue = np.array([130, 255, 255])
 
     # 黑色
-    lower_black = np.array([0, 0, 0])
-    upper_black = np.array([180, 255, 30])
+    # lower_black = np.array([0, 0, 0])
+    # upper_black = np.array([180, 255, 30])
 
     # 根据阈值构建掩模
-    # mask = cv2.inRange(hsv, lower_blue, upper_blue)
-    mask = cv2.inRange(hsv, lower_black, upper_black)
+    mask = cv2.inRange(hsv, lower_blue, upper_blue)
+    # mask = cv2.inRange(hsv, lower_black, upper_black)
     # 对原图像和掩模位运算
     res = cv2.bitwise_and(frame, frame, mask=mask)
 
     # 显示图像
     cv2.imshow('frame', frame)
     cv2.imshow('mask', mask)
     cv2.imshow('res', res)
-    k = cv2.waitKey(0)  # & 0xFF
-    if k == 27:
+
+    k = cv2.waitKey(1)  # & 0xFF
+    if k == ord('q'):
         break
 # 关闭窗口
 cv2.destroyAllWindows()
-

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

@@ -20,14 +20,11 @@
 
 # OR
 # 我们直接设置输出图像的尺寸 所以不用设置缩放因子
-height, width = img.shape[:2]
-res = cv2.resize(img, (2 * width, 2 * height), interpolation=cv2.INTER_CUBIC)
+# height, width = img.shape[:2]
+# res = cv2.resize(img, (2 * width, 2 * height), interpolation=cv2.INTER_CUBIC)
 
-while True:
-    cv2.imshow('resize', res)
-    cv2.imshow('src img', img)
+cv2.imshow('resize', res)
+cv2.imshow('src img', img)
 
-    key = cv2.waitKey(1)
-    if key == ord("q"):
-        break
+cv2.waitKey(0)
 cv2.destroyAllWindows()

ch14-几何变换/14.2平移.py

@@ -13,6 +13,8 @@
 import numpy as np
 
 cap = cv2.VideoCapture(0)
+ret = cap.set(3, 640)
+ret = cap.set(4, 480)
 while True:
     # 获取每一帧
     ret, frame = cap.read()
@@ -32,7 +34,7 @@
     cv2.imshow('mask', mask)
     cv2.imshow('res', res)
 
-    k = cv2.waitKey(0)  # & 0xFF
+    k = cv2.waitKey(1)  # & 0xFF
     if k == ord('q'):
         break
 # 关 窗口

ch16-图像平衡/图像模糊-平均.py

@@ -19,7 +19,7 @@
 '''
 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 。
 '''
-# 0 是指根据窗口大小 5,5 来计算高斯函数标准差
+# 0 是指根据窗口大小 (5,5) 来计算高斯函数标准差
 blur = cv2.GaussianBlur(img, (5, 5), 0)  # 高斯模糊
 
 '''
@@ -43,10 +43,11 @@
   因为边界处的灰度值变化比较大。
 '''
 
+#16.4 双边滤波
 # cv2.bilateralFilter(src, d, sigmaColor, sigmaSpace)
 # d – Diameter of each pixel neighborhood that is used during filtering. # If it is non-positive, it is computed from sigmaSpace
 # 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
-blur = cv2.bilateralFilter(img, 9, 75, 75)
+# blur = cv2.bilateralFilter(img, 9, 75, 75)
 
 plt.subplot(121), plt.imshow(img), plt.title('Original')
 plt.xticks([]), plt.yticks([])

ch18-图像梯度/一个重要的事.py

@@ -22,7 +22,7 @@
 import numpy as np
 from matplotlib import pyplot as plt
 
-img = cv2.imread('../data/box.png', 0)
+img = cv2.imread('../data/box.jpg', 0)
 
 # Output dtype = cv2.CV_8U
 sobelx8u = cv2.Sobel(img, cv2.CV_8U, 1, 0, ksize=5)

ch19-Canny边缘检测/19.Canny.py

@@ -29,7 +29,7 @@
 import numpy as np
 from matplotlib import pyplot as plt
 
-img = cv2.imread('../data/messi5.jpg')
+img = cv2.imread('../data/messi5.jpg',0)
 edges = cv2.Canny(img, 100, 200)
 
 plt.subplot(121), plt.imshow(img, cmap='gray')

ch20-图像金字塔/20.Apple_orange.py

@@ -37,6 +37,8 @@
     GE = cv2.pyrUp(gpA[i])
     L = cv2.subtract(gpA[i - 1], GE)#TODO error
     lpA.append(L)
+    #cv2.error: /Users/play/Temp/opencv/modules/core/src/arithm.cpp:659: error: (-209) The operation is neither 'array op array' (where arrays have the same size and the same number of channels), nor 'array op scalar', nor 'scalar op array' in function arithm_op
+
 
 # generate Laplacian Pyramid for B
 lpB = [gpB[5]]