Sourcery refactored master branch

Tools工具包/OpenCV_Window_Management/opencv_windows_management.py

@@ -24,7 +24,7 @@ def __init__(self, name, image, weight=1):
 
 class opencv_windows_management:
     def __init__(self):
-        self.windows = dict()
+        self.windows = {}
 
         root = tk.Tk()
         screen_width = root.winfo_screenwidth()

ch04-图片/show_image.py

@@ -12,6 +12,7 @@
 
 """
 
+
 import numpy as np
 import cv2, sys
 
@@ -36,14 +37,9 @@
     cv2.imshow(title, temp)
 
     k = cv2.waitKey(10)
-    if k == 27 or k == ord('q'):
+    if k in [27, ord('q')]:
         break
-    #TODO 分辨率太大，需要缩放
     if k == ord('g'):
-        # t = temp == img
-        # if t.all():
-        # if t.any():
-        # if temp == img:
         if gray is False:
             temp = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
             gray = True

ch05-视频/5.VideoPlay.py

@@ -24,7 +24,7 @@
 frame_no = 121
 cap.set(1, frame_no)  # Where frame_no is the frame you want
 ret, frame = cap.read()  # Read the frame
-cv2.imshow('frame_no'+str(frame_no), frame)
+cv2.imshow(f'frame_no{frame_no}', frame)
 
 FRAME_NOW = cap.get(cv2.CAP_PROP_POS_FRAMES)
 print('当前帧数', FRAME_NOW)  # 当前帧数 122.0

ch05-视频/5.VideoWriter.py

@@ -18,17 +18,15 @@
 
 while cap.isOpened():
     ret, frame = cap.read()
-    if ret is True:
-
-        frame = cv2.resize(frame, (640, 480))
+    if ret is not True:
+        break
 
-        # write the flipped frame
-        out.write(frame)
+    frame = cv2.resize(frame, (640, 480))
 
-        cv2.imshow('frame', frame)
+    # write the flipped frame
+    out.write(frame)
 
-    else:
-        break
+    cv2.imshow('frame', frame)
 
     key = cv2.waitKey(1)
     if key == ord("q"):

ch05-视频/VideoCaptureOnePicture.py

@@ -29,7 +29,7 @@ def id_generator(size=6, chars=string.ascii_uppercase + string.digits):
     if key == ord("q"):
         break
     elif key == ord("s"):
-        cv2.imwrite(id_generator() + '.jpg', frame)
+        cv2.imwrite(f'{id_generator()}.jpg', frame)
 
 cap.release()
 cv2.destroyAllWindows()

ch06-绘图函数/画圆圈.py

@@ -32,7 +32,7 @@ def click_event(event, x, y, flags, param):
 canvas = np.zeros((300, 300, 3), dtype="uint8")
 while True:
     try:
-        for i in range(0, 25):
+        for _ in range(0, 25):
             radius = np.random.randint(5, high=200)
             color = np.random.randint(0, high=256, size=(3,)).tolist()
             pt = np.random.randint(0, high=300, size=(2,))

ch07-把鼠标当画笔/鼠标左右键回调函数.py

@@ -21,7 +21,7 @@ def click_event(event, x, y, flags, param):
         green = img[y, x, 1]
         print(red, green, blue)
 
-        strRGB = str(red) + "," + str(green) + "," + str(blue)
+        strRGB = f"{str(red)},{str(green)},{str(blue)}"
         font = cv2.FONT_HERSHEY_SIMPLEX
         cv2.putText(img, strRGB, (x, y), font, 1, (255, 255, 255), 2)
         cv2.imshow('original', img)

ch08-用滑动条做调色板/8.createTrackbar.py

@@ -30,11 +30,7 @@ def nothing(x):
     b = cv2.getTrackbarPos('B', 'image')
     s = cv2.getTrackbarPos(switch, 'image')  # 另外一个重要应用就是用作转换按钮
 
-    if s == 0:
-        img[:] = 0
-    else:
-        img[:] = [b, g, r]
-
+    img[:] = 0 if s == 0 else [b, g, r]
     cv2.imshow('image', img)
     k = cv2.waitKey(1)  # & 0xFF
     if k == ord("q"):

ch10-图像上的算术运算/图像相减2.py

@@ -8,6 +8,7 @@
 图像相减2.py:
 """
 
+
 import cv2
 import numpy as np
 import matplotlib.pyplot as plt
@@ -45,7 +46,7 @@
 
 image, contours, hierarchy = cv2.findContours(threshold, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
 
-areas = list()
+areas = []
 for i, cnt in enumerate(contours):
 
     areas.append((i, cv2.contourArea(cnt)))

ch10-图像上的算术运算/图像相减_camera.py

@@ -8,6 +8,7 @@
 图像相减_camera.py:
 """
 
+
 import cv2
 import numpy as np
 
@@ -49,7 +50,7 @@
 # cap.set(1, frame_no)#第10帧
 ret, bgimg0 = cap.read()  # 背景
 bgimg = cv2.cvtColor(bgimg0, cv2.COLOR_BGR2GRAY)
-cv2.imshow('bgimg' + str(frame_no), bgimg0)
+cv2.imshow(f'bgimg{frame_no}', bgimg0)
 # cv2.imwrite('desk_bgimg.jpg',bgimg)
 
 while cap.isOpened():

ch20-图像金字塔/20.Apple_orange.py

@@ -11,6 +11,7 @@
 4. 在拉普拉斯的每一层  图像 合 苹果的左 与橘子的右  合  5. 根据 合后的图像 字塔 建原始图像。
 '''
 
+
 import cv2
 import numpy as np, sys
 
@@ -20,14 +21,14 @@
 # generate Gaussian pyramid for A
 G = A.copy()
 gpA = [G]
-for i in range(6):
+for _ in range(6):
     G = cv2.pyrDown(G)
     gpA.append(G)
 
 # generate Gaussian pyramid for B
 G = B.copy()
 gpB = [G]
-for i in range(6):
+for _ in range(6):
     G = cv2.pyrDown(G)
     gpB.append(G)
 

ch200_Extra_modules/aruco/Camera Calibration using ChArUco and Python/aruco_test1.py

@@ -8,6 +8,7 @@
 aruco11.py:
 """
 
+
 import time, cv2
 # import cv2.aruco as A
 import numpy as np
@@ -28,8 +29,7 @@
 allCorners = []
 allIds = []
 decimator = 0
-for i in range(200):
-
+for _ in range(200):
     ret, frame = cap.read()
     gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
     res = cv2.aruco.detectMarkers(gray, dictionary)

ch200_Extra_modules/aruco/Camera Calibration using ChArUco and Python/calibrateCamera2.py

@@ -27,8 +27,7 @@ def get_image(camera):
 
 
 def make_grayscale(img):
-    ret = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-    return ret
+    return cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
 
 
 def main():

ch21-轮廓Contours/draw最大的轮廓.py

@@ -8,6 +8,7 @@
 draw最大的轮廓.py:
 """
 
+
 import cv2
 import numpy as np
 
@@ -22,10 +23,7 @@
 
 image, contours, hierarchy = cv2.findContours(threshold, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
 
-areas = list()
-for i, cnt in enumerate(contours):
-    areas.append((i, cv2.contourArea(cnt)))#面积大小
-
+areas = [(i, cv2.contourArea(cnt)) for i, cnt in enumerate(contours)]
 #
 a2 = sorted(areas, key=lambda d: d[1], reverse=True)#按面积大小，从大到小排序
 

ch22-直方图/22.3-2D直方图.py

@@ -22,6 +22,7 @@
 
 """
 
+
 import cv2
 import numpy as np
 
@@ -36,5 +37,3 @@
 h, s, v = cv2.split(hsv)
 
 hist, xbins, ybins = np.histogram2d(h.ravel(), s.ravel(), [180, 256], [[0, 180], [0, 256]])
-
-pass

ch22-直方图/hist.py

@@ -33,8 +33,7 @@ def hist_curve(im):
         hist = np.int32(np.around(hist_item))
         pts = np.int32(np.column_stack((bins, hist)))
         cv2.polylines(h, [pts], False, col)
-    y = np.flipud(h)
-    return y
+    return np.flipud(h)
 
 
 def hist_lines(im):

ch24-模板匹配/24.1-OpenCV中的模板匹配-matchTemplate.py

@@ -53,5 +53,5 @@
     plt.title('Matching Result'), plt.xticks([]), plt.yticks([])
     plt.subplot(122), plt.imshow(img, cmap='gray')
     plt.title('Detected Point'), plt.xticks([]), plt.yticks([])
-    plt.suptitle('method: ' + meth)
+    plt.suptitle(f'method: {meth}')
     plt.show()

ch26-Hough圆环变换/HoughCircles_camera.py

@@ -50,17 +50,16 @@ def draw_line_rectangle(frame, margin):
     if key == ord("q"):
         break
     if key == ord('s'):
-        cv2.imwrite(id_generator() + '.jpg', frame2)
+        cv2.imwrite(f'{id_generator()}.jpg', frame2)
 
     # Capture frame-by-frame
     ret, frame = cap.read()
     m = mse(cv2.cvtColor(temp, cv2.COLOR_BGR2GRAY), cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY))
     print('mse', m, '----\n')
-    if abs(m - tm) < 2:  # 静止画面，不用重复计算
+    if abs(m - tm) < 2:
         continue
-    else:
-        temp = frame.copy()
-        tm = m
+    temp = frame.copy()
+    tm = m
     #
     # print(margin,frame.shape[0] - margin, margin,frame.shape[1] - margin)#40 680 40 1240
     frame2 = frame[margin:frame.shape[0] - margin, margin:frame.shape[1] - margin]  # .copy()

ch26-Hough圆环变换/HoughCircles_chess.py

@@ -15,14 +15,12 @@
 
 
 def detect_weiqi(img):  # 检测棋子的颜色
-    txt = 'black'
     gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
     ret, threshold = cv2.threshold(gray, 100, 255, cv2.THRESH_BINARY)
 
     c = Counter(list(threshold.flatten()))
     print(c.most_common())
-    if c.most_common()[0][0] != 0:
-        txt = 'white'
+    txt = 'white' if c.most_common()[0][0] != 0 else 'black'
     return txt, threshold
 
 

ch37-特征匹配/37.4-SIFT_match.py

@@ -25,15 +25,7 @@
 bf = cv2.BFMatcher()
 matches = bf.knnMatch(des1, des2, k=2)
 
-# Apply ratio test
-# 比值测试，首先获取与 A距离最近的点 B （最近）和 C （次近），
-# 只有当 B/C 小于阀值时（0.75）才被认为是匹配，
-# 因为假设匹配是一一对应的，真正的匹配的理想距离为0
-good = []
-for m, n in matches:
-    if m.distance < 0.75 * n.distance:
-        good.append([m])
-
+good = [[m] for m, n in matches if m.distance < 0.75 * n.distance]
 # cv2.drawMatchesKnn expects list of lists as matches.
 # img3 = np.ndarray([2, 2])
 # img3 = cv2.drawMatchesKnn(img1, kp1, img2, kp2, good[:10], img3, flags=2)

ch37-特征匹配/37.5-FLANN匹配器.py

@@ -15,6 +15,7 @@
 
 
 
+
 import numpy as np
 import cv2
 from matplotlib import pyplot as plt
@@ -38,7 +39,7 @@
 flann = cv2.FlannBasedMatcher(index_params, search_params)
 matches = flann.knnMatch(des1, des2, k=2)
 # Need to draw only good matches, so create a mask
-matchesMask = [[0, 0] for i in range(len(matches))]
+matchesMask = [[0, 0] for _ in range(len(matches))]
 
 # ratio test as per Lowe's paper
 for i, (m, n) in enumerate(matches):

ch38-使用特征匹配和单应性查找对象/findHomography.py

@@ -8,6 +8,7 @@
 findHomography.py:联合使用特征提取和 calib3d 模块中的 findHomography 在复杂图像中查找已知对象
 """
 
+
 import numpy as np
 import cv2
 from matplotlib import pyplot as plt
@@ -28,11 +29,7 @@
 flann = cv2.FlannBasedMatcher(index_params, search_params)
 matches = flann.knnMatch(des1, des2, k=2)
 
-# store all the good matches as per Lowe's ratio test.
-good = []
-for m, n in matches:
-    if m.distance < 0.7 * n.distance:
-        good.append(m)
+good = [m for m, n in matches if m.distance < 0.7 * n.distance]
 '''
 现在我们 置只有存在 10 个以上匹 时才去查找目标 MIN_MATCH_COUNT=10   否则显示 告消息  现在匹 不   
 如果找到了 够的匹  我们 提取两幅图像中匹 点的坐标。把它们传 入到函数中 算  变换。一旦我们找到 3x3 的变换矩  就可以使用它将查  图像的四个 点 四个  变换到目标图像中去了。然后再绘制出来。

ch43-姿势估计/calib3d.py

@@ -72,7 +72,7 @@ def draw_cube(img, corners, imgpts):
         cv2.imshow('img', img)
         k = cv2.waitKey(0) & 0xFF
         if k == ord('s'):
-            cv2.imwrite(fname[:6] + '.png', img)
+            cv2.imwrite(f'{fname[:6]}.png', img)
 cv2.destroyAllWindows()
 
 #如果你对计算机图形学感兴趣的  为了增加图像的真实性 你可以使用 OpenGL 来渲染更复杂的图形。 下一个目标

ch44-对极几何/code.py

@@ -64,7 +64,7 @@ def drawlines(img1, img2, lines, pts1, pts2):
 pts1 = []
 pts2 = []
 # ratio test as per Lowe's paper
-for i, (m, n) in enumerate(matches):
+for m, n in matches:
     if m.distance < 0.8 * n.distance:
         good.append(m)
         pts2.append(kp2[m.trainIdx].pt)

ch47-支持向量机/47.2-使用SVM进行-手写数据OCR.py

@@ -36,8 +36,7 @@ def hog(img):
     bin_cells = bins[:10, :10], bins[10:, :10], bins[:10, 10:], bins[10:, 10:]
     mag_cells = mag[:10, :10], mag[10:, :10], mag[:10, 10:], mag[10:, 10:]
     hists = [np.bincount(b.ravel(), m.ravel(), bin_n) for b, m in zip(bin_cells, mag_cells)]
-    hist = np.hstack(hists)  # hist is a 64 bit vector
-    return hist
+    return np.hstack(hists)
 
 
 # 最后 和前 一样 我们将大图分割成小图。使用每个数字的前 250 个作 为训练数据

ch48-K值聚类/48.2.3_颜色量化2.py

@@ -8,6 +8,7 @@
 48.2.3_颜色量化.py:
 """
 
+
 #  色 化就是减少图片中 色数目的一个 程。为什么 减少图片中的  色呢 减少内存消耗 有些 备的 源有  只能显示很少的 色。在 种情 况下就     色 化。我们使用 K 值聚类的方法来   色 化。
 # 没有什么新的知   介绍了。现在有 3 个特征 R G B。所以我们   把图片数据变形成 Mx3 M 是图片中像素点的数目 的向 。聚类完成后  我们用聚类中心值替换与其同组的像素值  样结果图片就只含有指定数目的  色了。下 是代码
 # -*- coding: utf-8 -*-
@@ -39,7 +40,7 @@
             a1.append([0,0,0])
     a2=np.array(a1)
     a3=a2.reshape((img.shape))
-    cv2.imshow('res2'+str(y), a3)
+    cv2.imshow(f'res2{str(y)}', a3)
 
 #最大的色块
 

ch49-计算摄影学-图像去噪/2-fastNlMeansDenoisingMulti.py

@@ -8,13 +8,14 @@
 2-fastNlMeansDenoisingMulti.py:
 """
 
+
 import numpy as np
 import cv2
 from matplotlib import pyplot as plt
 
 cap = cv2.VideoCapture('../data/vtest.avi')
 # create a list of first 5 frames
-img = [cap.read()[1] for i in range(5)]
+img = [cap.read()[1] for _ in range(5)]
 # convert all to grayscale
 gray = [cv2.cvtColor(i, cv2.COLOR_BGR2GRAY) for i in img]
 # convert all to float64