fix all

ch04-图片/4.1_imread_imshow.py

@@ -10,6 +10,8 @@
 # img = cv2.imread('messi5.jpg', cv2.IMREAD_GRAYSCALE)# Load an color image in grayscale 灰度
 img = cv2.imread('messi5.jpg',cv2.IMREAD_UNCHANGED)#包括图像的 alpha 通道
 
+img = cv2.resize(img, (640, 480))
+
 # img.I
 # AttributeError: 'numpy.ndarray' object has no attribute 'I'
 

ch21-轮廓Contours/21.4.3-形状匹配.py

@@ -20,9 +20,9 @@
 ret, thresh = cv2.threshold(img1, 127, 255, 0)
 ret, thresh2 = cv2.threshold(img2, 127, 255, 0)
 
-contours, hierarchy = cv2.findContours(thresh, 2, 1)
+image,contours, hierarchy = cv2.findContours(thresh, 2, 1)
 cnt1 = contours[0]
-contours, hierarchy = cv2.findContours(thresh2, 2, 1)
+image,contours, hierarchy = cv2.findContours(thresh2, 2, 1)
 cnt2 = contours[0]
 
 ret = cv2.matchShapes(cnt1, cnt2, 1, 0.0)

ch30-Harris角点检测/30.1-OpenCV-Harris角点检测-cornerHarris.py

@@ -13,11 +13,12 @@
 import cv2
 import numpy as np
 
-filename = '../data/chessboard.jpeg'
+filename = '../data/chessboard.png'
 # filename = '../data/chessboard-3.png'
 # filename = '../data/corner-detection.jpg'
 
 img = cv2.imread(filename)
+img = cv2.resize(img, (640, 480))
 gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
 gray = np.float32(gray)
 
@@ -28,7 +29,7 @@
 # Threshold for an optimal value, it may vary depending on the image.
 img[dst > 0.01 * dst.max()] = [0, 0, 255]
 
+cv2.namedWindow('dst', cv2.WINDOW_NORMAL)
 cv2.imshow('dst', img)
-
 cv2.waitKey(0)
 cv2.destroyAllWindows()

ch31-Shi-Tomasi角点检测-适合于跟踪的图像特征/goodFeaturesToTrack.py

@@ -18,7 +18,8 @@
 import cv2
 from matplotlib import pyplot as plt
 
-filename = '../data/corner-detection.jpg'
+# filename = '../data/corner-detection.jpg'
+filename = '../data/blox.jpg'
 img = cv2.imread(filename)
 gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
 

ch33-介绍SURF/surf.py

@@ -13,7 +13,7 @@
 import cv2
 import matplotlib.pyplot as plt
 
-img = cv2.imread('fly.png', 0)
+img = cv2.imread('../data/butterfly.jpg', 0)
 # Create SURF object. You can specify params here or later. # Here I set Hessian Threshold to 400
 # surf = cv2.SURF(400)
 surf = cv2.xfeatures2d.SURF_create(400)
@@ -47,7 +47,8 @@
 print(surf.getExtended())
 # False
 # So we make it to True to get 128-dim descriptors.
-surf.extended = True
+# surf.extended = True
+surf.setExtended(True)
 kp, des = surf.detectAndCompute(img, None)
 print(surf.descriptorSize())
 # 128

ch34-角点检测的FAST算法/fast.py

@@ -18,7 +18,7 @@
 import cv2
 from matplotlib import pyplot as plt
 
-img = cv2.imread('simple.jpg', 0)
+img = cv2.imread('../data/blox.jpg', 0)
 
 # Initiate FAST object with default values
 fast = cv2.FastFeatureDetector_create()

ch35-BRIEF/brief.py

@@ -19,7 +19,7 @@
 import cv2
 from matplotlib import pyplot as plt
 
-img = cv2.imread('simple.jpg', 0)
+img = cv2.imread('../data/blox.jpg', 0)
 
 # Initiate FAST detector
 star = cv2.xfeatures2d.StarDetector_create()

ch36-ORB/orb.py

@@ -28,7 +28,7 @@
 import cv2
 from matplotlib import pyplot as plt
 
-img = cv2.imread('simple.jpg', 0)
+img = cv2.imread('../data/blox.jpg', 0)
 
 # Initiate ORB detector
 orb = cv2.ORB_create()

ch37-特征匹配/37.2-对ORB描述符进行蛮力匹配.py

@@ -19,8 +19,8 @@
 import cv2
 import matplotlib.pyplot as plt
 
-img1 = cv2.imread('box.png', 0)  # queryImage
-img2 = cv2.imread('box_in_scene.png', 0)  # trainImage
+img1 = cv2.imread('../data/box.png', 0)  # queryImage
+img2 = cv2.imread('../data/box_in_scene.png', 0)  # trainImage
 
 # Initiate ORB detector
 orb = cv2.ORB_create()
@@ -36,6 +36,7 @@
 # Sort them in the order of their distance.
 matches = sorted(matches, key=lambda x: x.distance)
 # Draw first 10 matches.
-img3 = cv2.drawMatches(img1, kp1, img2, kp2, matches[:10], flags=2)  # 前10个匹配
+# img3 = cv2.drawMatches(img1, kp1, img2, kp2, matches[:10], flags=2)  # 前10个匹配
+img3 = cv2.drawMatches(img1, kp1, img2, kp2, matches[:10], None,flags=2)  # 前10个匹配
 
 plt.imshow(img3), plt.show()

ch37-特征匹配/37.4-SIFT_match.py

@@ -39,6 +39,6 @@
 # img3 = cv2.drawMatchesKnn(img1, kp1, img2, kp2, good[:10], img3, flags=2)
 
 # cv2.drawMatchesKnn expects list of lists as matches.
-img3 = cv2.drawMatchesKnn(img1,kp1,img2,kp2,good,flags=2)
+img3 = cv2.drawMatchesKnn(img1,kp1,img2,kp2,good,None,flags=2)
 
 plt.imshow(img3), plt.show()

ch37-特征匹配/37.5-FLANN匹配器.py

@@ -19,8 +19,8 @@
 import cv2
 from matplotlib import pyplot as plt
 
-img1 = cv2.imread('box.png', 0)  # queryImage
-img2 = cv2.imread('box_in_scene.png', 0)  # trainImage
+img1 = cv2.imread('../data/box.png', 0)  # queryImage
+img2 = cv2.imread('../data/box_in_scene.png', 0)  # trainImage
 
 # Initiate SIFT detector
 # sift = cv2.SIFT()

ch38-使用特征匹配和单应性查找对象/findHomography.py

@@ -13,8 +13,8 @@
 from matplotlib import pyplot as plt
 
 MIN_MATCH_COUNT = 10
-img1 = cv2.imread('box.png', 0)  # queryImage
-img2 = cv2.imread('box_in_scene.png', 0)  # trainImage
+img1 = cv2.imread('../data/box.png', 0)  # queryImage
+img2 = cv2.imread('../data/box_in_scene.png', 0)  # trainImage
 
 # Initiate SIFT detector
 sift = cv2.xfeatures2d.SIFT_create()
@@ -51,7 +51,7 @@
     M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)
     matchesMask = mask.ravel().tolist()
     # 获得原图像的高和宽
-    h, w, d = img1.shape
+    h, w = img1.shape
     # 使用得到的变换矩 对原图像的四个   变换 获得在目标图像上对应的坐标
     pts = np.float32([[0, 0], [0, h - 1], [w - 1, h - 1], [w - 1, 0]]).reshape(-1, 1, 2)
     dst = cv2.perspectiveTransform(pts, M)
@@ -61,8 +61,7 @@
     print("Not enough matches are found - %d/%d" % (len(good), MIN_MATCH_COUNT))
     matchesMask = None
 
-
-#最后我再绘制 inliers 如果能成功的找到目标图像的话  或者匹配的关  点 如果失败。
+# 最后我再绘制 inliers 如果能成功的找到目标图像的话  或者匹配的关  点 如果失败。
 draw_params = dict(matchColor=(0, 255, 0),  # draw matches in green color
                    singlePointColor=None,
                    matchesMask=matchesMask,  # draw only inliers
@@ -71,4 +70,4 @@
 img3 = cv2.drawMatches(img1, kp1, img2, kp2, good, None, **draw_params)
 
 plt.imshow(img3, 'gray'), plt.show()
-#复杂图像中被找到的目标图像被标记成白色
+# 复杂图像中被找到的目标图像被标记成白色

ch39-视频分析-Meanshift和Camshift/Camshift.py

@@ -37,13 +37,15 @@
         # Draw it on image
         pts = cv2.boxPoints(ret)
         pts = np.int0(pts)
-        img2 = cv2.polylines(frame, [pts], True, 255, 2)
+        print('len pts:', len(pts),pts)
+        img2 = cv2.polylines(frame, [pts], True, (255, 0, 0), 2)
+
         cv2.imshow('img2', img2)
-        k = cv2.waitKey(60) & 0xff
+        k = cv2.waitKey(1)  # & 0xff
         if k == 27:
             break
-        else:
-            cv2.imwrite(chr(k) + ".jpg", img2)
+            # else:
+            #     cv2.imwrite(chr(k) + ".jpg", img2)
     else:
         break
 cv2.destroyAllWindows()

ch39-视频分析-Meanshift和Camshift/Meanshift.py

@@ -11,12 +11,13 @@
 而汽车由远及近 在视觉上 是一个 渐变大的过程
 固定的窗口是不 合适的。所以我们需要根据目标的大小和角度来对窗口的大小和角度进行修订
 
+http://docs.opencv.org/3.2.0/db/df8/tutorial_py_meanshift.html
 """
 
 import numpy as np
 import cv2
 
-cap = cv2.VideoCapture('slow.flv')
+cap = cv2.VideoCapture('../data/slow.flv')
 
 # take first frame of the video
 ret, frame = cap.read()
@@ -26,7 +27,7 @@
 # set up the ROI for tracking
 roi = frame[r:r + h, c:c + w]
 hsv_roi = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
-#将低亮度的值忽略掉
+# 将低亮度的值忽略掉
 mask = cv2.inRange(hsv_roi, np.array((0., 60., 32.)), np.array((180., 255., 255.)))
 roi_hist = cv2.calcHist([hsv_roi], [0], mask, [180], [0, 180])
 
@@ -37,22 +38,26 @@
 
 while True:
     ret, frame = cap.read()
-    if ret == True:
+    if ret is True:
         hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
         dst = cv2.calcBackProject([hsv], [0], roi_hist, [0, 180], 1)
         # apply meanshift to get the new location
         ret, track_window = cv2.meanShift(dst, track_window, term_crit)
+
         # Draw it on image
         x, y, w, h = track_window
+        print(track_window)
         img2 = cv2.rectangle(frame, (x, y), (x + w, y + h), 255, 2)
         cv2.imshow('img2', img2)
-        k = cv2.waitKey(60) & 0xff
+
+
+        k = cv2.waitKey(60)  # & 0xff
         if k == 27:
             break
-        else:
-            cv2.imwrite(chr(k) + ".jpg", img2)
+            # else:
+            #     cv2.imwrite(chr(k) + ".jpg", img2)
     else:
         break
 cv2.destroyAllWindows()
 cap.release()
-
+#不正常

ch40-光流/calcOpticalFlowPyrLK.py → ch40-光流/40.3-Lucas-Kanade-光流calcOpticalFlowPyrLK.py

@@ -17,7 +17,7 @@
 import numpy as np
 import cv2
 
-cap = cv2.VideoCapture('slow.flv')
+cap = cv2.VideoCapture('../data/slow.flv')
 
 # params for ShiTomasi corner detection
 feature_params = dict(maxCorners=100,
@@ -55,7 +55,8 @@
         frame = cv2.circle(frame, (a, b), 5, color[i].tolist(), -1)
     img = cv2.add(frame, mask)
     cv2.imshow('frame', img)
-    k = cv2.waitKey(30) & 0xff
+
+    k = cv2.waitKey(30) #& 0xff
     if k == 27:
         break
     # Now update the previous frame and previous points

ch40-光流/40.4-OpenCV中的稠密光流.py

@@ -12,6 +12,7 @@
 import numpy as np
 
 cap = cv2.VideoCapture("../data/vtest.avi")
+# cap = cv2.VideoCapture("../data/slow.flv")
 ret, frame1 = cap.read()
 
 prvs = cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY)
@@ -26,8 +27,10 @@
     hsv[..., 0] = ang * 180 / np.pi / 2
     hsv[..., 2] = cv2.normalize(mag, None, 0, 255, cv2.NORM_MINMAX)
     bgr = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
-    cv2.imshow('frame2', bgr)
-    k = cv2.waitKey(30) & 0xff
+
+    cv2.imshow('frame2', frame2)
+    cv2.imshow('flow', bgr)
+    k = cv2.waitKey(1) & 0xff
     if k == 27:
         break
     elif k == ord('s'):

ch41-背景减除/41.2-BackgroundSubtractorMOG.py

@@ -16,15 +16,15 @@
 import cv2
 
 cap = cv2.VideoCapture('../data/vtest.avi')
-fgbg = cv2.bgsegm.createBackgroundSubtractorMOG() #TODO
+fgbg = cv2.bgsegm.createBackgroundSubtractorMOG()
 # 可选参数 比如 进行建模场景的时间长度 高斯混合成分的数量-阈值等
 
 while True:
     ret, frame = cap.read()
     fgmask = fgbg.apply(frame)
 
     cv2.imshow('frame', fgmask)
-    k = cv2.waitKey(30) & 0xff
+    k = cv2.waitKey(1) #& 0xff
     if k == 27:
         break
 cap.release()

ch41-背景减除/41.3-BackgroundSubtractorMOG2.py

@@ -16,7 +16,7 @@
 import numpy as np
 import cv2
 
-cap = cv2.VideoCapture('vtest.avi')
+cap = cv2.VideoCapture('../data/vtest.avi')
 fgbg = cv2.createBackgroundSubtractorMOG2()
 while True:
     ret, frame = cap.read()

ch41-背景减除/41.4-BackgroundSubtractorGMG.py

@@ -14,16 +14,16 @@
 import numpy as np
 import cv2
 
-cap = cv2.VideoCapture('vtest.avi')
+cap = cv2.VideoCapture('../data/vtest.avi')
 kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
-fgbg = cv2.createBackgroundSubtractorGMG()
+fgbg = cv2.bgsegm.createBackgroundSubtractorGMG()
 while True:
     ret, frame = cap.read()
     fgmask = fgbg.apply(frame)
     fgmask = cv2.morphologyEx(fgmask, cv2.MORPH_OPEN, kernel)
 
     cv2.imshow('frame', fgmask)
-    k = cv2.waitKey(30) & 0xff
+    k = cv2.waitKey(1)  # & 0xff
     if k == 27:
         break
 cap.release()

ch42-摄像机标定/42.2.1-设置-findChessboardCorners.py

@@ -31,7 +31,8 @@
 # Arrays to store object points and image points from all the images.
 objpoints = []  # 3d point in real world space
 imgpoints = []  # 2d points in image plane.
-images = glob.glob('*.jpg')
+images = glob.glob('../data/left*.jpg')
+images += glob.glob('../data/right*.jpg')
 for fname in images:
     img = cv2.imread(fname)
     gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

ch43-姿势估计/calib3d.py

@@ -58,7 +58,7 @@ def draw_cube(img, corners, imgpts):
 '''
 很 常一样我们  加 图像。搜寻 7x6 的格子 如果发现 我们就把它 优化到亚像素级。然后使用函数:cv2.solvePnPRansac() 来 算旋 和变 换。但我们有了变换矩 之后 我们就可以利用它们将 些坐标 点映射到图 像平 中去。简单来  我们在图像平 上找到了与 3D 空 中的点 3,0,0  ,(0,3,0),(0,0,3) 相对应的点。然后我们就可以使用我们的函数 draw() 从图像 上的第一个 点开始绘制 接 些点的直线了。搞定   
 '''
-for fname in glob.glob('left*.jpg'):
+for fname in glob.glob('../data/left*.jpg'):
     img = cv2.imread(fname)
     gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
     ret, corners = cv2.findChessboardCorners(gray, (7, 6), None)