076-080

Author: JimmyHHua

README.md

@@ -113,4 +113,9 @@ code_071 | [Hit and Miss](python/code_071/opencv_071.py)   | ✔️
 code_072 | [Defect Detecting-1](python/code_072)   | ✔️
 code_073 | [Defect Detecting-2](python/code_073/opencv_073.py)   | ✔️
 code_074 | [Extract the Maximum Contour and Coding Key Points](python/code_074)   | ✔️
-code_075 | [Image Inpainting](python/code_075/opencv_075.py)   | ✔️
+code_075 | [Image Inpainting](python/code_075/opencv_075.py)   | ✔️
+code_076 | [Perspective Transformation](python/code_076/opencv_076.py)   | ✏️
+code_077 | [Video Read, Write and Process](python/code_077/opencv_077.py)   | ✏️
+code_078 | [Identify and Track Specific Color Objects in Video](python/code_078)   | ✔️
+code_079 | [Video Analysis-Background/Foreground Extraction](python/code_079/opencv_079.py)   | ✔️
+code_080 | [Video Analysis–Background Subtraction and ROI Extraction of the Foreground](python/code_080)   | ✔️

README_CN.md

@@ -112,4 +112,9 @@ code_071 | [击中击不中](python/code_071/opencv_071.py)   | ✔️
 code_072 | [缺陷检测1](python/code_072)   | ✔️
 code_073 | [缺陷检测2](python/code_073/opencv_073.py)   | ✔️
 code_074 | [提取最大轮廓和编码关键点](python/code_074)   | ✔️
-code_075 | [图像修复](python/code_075/opencv_075.py)   | ✔️
+code_075 | [图像修复](python/code_075/opencv_075.py)   | ✔️
+code_076 | [图像透视变换应用](python/code_076/opencv_076.py)   | ✏️
+code_077 | [视频读写和处理](python/code_077/opencv_077.py)   | ✏️
+code_078 | [识别与跟踪视频中的特定颜色对象](python/code_078)   | ✔️
+code_079 | [视频分析-背景/前景 提取](python/code_079/opencv_079.py)   | ✔️
+code_080 | [视频分析–背景消除与前景ROI提取](python/code_080)   | ✔️

python/code_076/binary.png

@@ -0,0 +1,51 @@
+import cv2 as cv
+import numpy as np
+
+src = cv.imread("st_02.png")
+cv.namedWindow("input", cv.WINDOW_AUTOSIZE)
+cv.imshow("input", src)
+
+# 图像二值化
+gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
+ret, binary = cv.threshold(gray, 0, 255, cv.THRESH_BINARY_INV | cv.THRESH_OTSU)
+
+se = cv.getStructuringElement(cv.MORPH_RECT, (3, 3), (-1, -1))
+binary = cv.morphologyEx(binary, cv.MORPH_OPEN, se)
+cv.imshow("binary", binary)
+cv.imwrite("binary.png", binary)
+
+# 轮廓提取, 发现最大轮廓
+out, contours, hierarchy = cv.findContours(binary, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
+
+# 寻找最大面积轮廓
+cnt_maxArea = sorted(contours, key=cv.contourArea)[0]
+
+# 寻找最小外接矩形,返回最小外接矩形的（中心(x,y), (宽,高), 旋转角度）
+rect = cv.minAreaRect(cnt_maxArea)
+print(rect[2])
+print(rect[0])
+# trick
+height, width = rect[1]
+print(rect[1])
+box = cv.boxPoints(rect)
+src_pts = np.int0(box)
+print(src_pts)
+
+dst_pts = []
+dst_pts.append([width,height])
+dst_pts.append([0, height])
+dst_pts.append([0, 0])
+dst_pts.append([width, 0])
+
+# 透视变换
+M, status = cv.findHomography(src_pts, np.array(dst_pts))  #原图到透视后的图的四个点的转换矩阵
+result = cv.warpPerspective(src, M, (np.int32(width), np.int32(height)))
+
+if height < width:
+    result = cv.rotate(result, cv.ROTATE_90_CLOCKWISE)
+
+cv.imshow("result", result)
+cv.imwrite("result.png", result)
+
+cv.waitKey(0)
+cv.destroyAllWindows()

python/code_076/opencv_076.py

@@ -0,0 +1,39 @@
+import cv2 as cv
+
+capture = cv.VideoCapture(1)
+height = capture.get(cv.CAP_PROP_FRAME_HEIGHT)
+width = capture.get(cv.CAP_PROP_FRAME_WIDTH)
+count = capture.get(cv.CAP_PROP_FRAME_COUNT)
+fps = capture.get(cv.CAP_PROP_FPS)
+print(height, width, count, fps)
+
+
+def process(image, opt=1):
+    dst = None
+    if opt == 0:
+        dst = cv.bitwise_not(image)
+    if opt == 1:
+        dst = cv.GaussianBlur(image, (0, 0), 15)
+    if opt == 2:
+        dst = cv.Canny(image, 100, 200)
+    return dst
+
+
+index = 0
+while(True):
+    ret, frame = capture.read()
+    if ret is True:
+        cv.imshow("video-input", frame)
+        c = cv.waitKey(50)
+        if c >= 49:
+            index = c -49
+        result = process(frame, index)
+        cv.imshow("result", result)
+        #print(c)
+        if c == 27:  #ESC
+            break
+    else:
+        break
+cv.waitKey(0)
+cv.destroyAllWindows()
+

python/code_076/result.png

@@ -0,0 +1,45 @@
+#### 识别与跟踪视频中的特定颜色对象
+
+这个是其实图像处理与二值分析的视频版本，通过读取视频每一帧的图像，然后对图像二值分析，得到指定的色块区域，主要步骤如下：
+1.	色彩转换BGR2HSV
+2.	inRange提取颜色区域mask
+3.	对mask区域进行二值分析得到位置与轮廓信息
+4.	绘制外接椭圆与中心位置
+5.	显示结果
+
+其中涉及到的知识点主要包括图像处理、色彩空间转换、形态学、轮廓分析等。
+
+函数：
+```python
+def process(image, opt=1):
+
+    # BGR转换为HSV
+    hsv = cv.cvtColor(image, cv.COLOR_BGR2HSV)
+
+    line = cv.getStructuringElement(cv.MORPH_RECT, (15, 15), (-1, -1))
+
+    # Inrange
+    mask = cv.inRange(hsv, (0, 43, 46), (10, 255, 255))
+
+    # 开操作，去除吊小的噪点
+    mask = cv.morphologyEx(mask, cv.MORPH_OPEN, line)
+
+    # 轮廓提取, 发现最大轮廓
+    out, contours, hierarchy = cv.findContours(mask, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
+    index = -1
+    max = 0
+    for c in range(len(contours)):
+        area = cv.contourArea(contours[c])
+        if area > max:
+            max = area
+            index = c
+    # 绘制
+    if index >= 0:
+        rect = cv.minAreaRect(contours[index])
+        cv.ellipse(image, rect, (0, 255, 0), 2, 8)
+        cv.circle(image, (np.int32(rect[0][0]), np.int32(rect[0][1])), 2, (255, 0, 0), 2, 8, 0)
+    return image
+
+```
+
+<img src=../code_078/result.jpg width=300>

python/code_076/st_02.png

@@ -0,0 +1,52 @@
+import cv2 as cv
+import numpy as np
+
+capture = cv.VideoCapture("test.mp4")
+height = capture.get(cv.CAP_PROP_FRAME_HEIGHT)
+width = capture.get(cv.CAP_PROP_FRAME_WIDTH)
+count = capture.get(cv.CAP_PROP_FRAME_COUNT)
+fps = capture.get(cv.CAP_PROP_FPS)
+print(height, width, count, fps)
+
+
+def process(image, opt=1):
+    hsv = cv.cvtColor(image, cv.COLOR_BGR2HSV)
+    line = cv.getStructuringElement(cv.MORPH_RECT, (15, 15), (-1, -1))
+    #mask = cv.inRange(hsv, (0, 43, 46), (10, 255, 255))
+    cv.imshow("mask", mask)
+    mask = cv.morphologyEx(mask, cv.MORPH_OPEN, line)
+    #cv.imshow("masks", mask)
+
+    # 轮廓提取, 发现最大轮廓
+    out, contours, hierarchy = cv.findContours(mask, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
+    index = -1
+    max = 0
+    for c in range(len(contours)):
+        area = cv.contourArea(contours[c])
+        if area > max:
+            max = area
+            index = c
+    # 绘制
+    if index >= 0:
+        rect = cv.minAreaRect(contours[index])
+        cv.ellipse(image, rect, (0, 255, 0), 2, 8)
+        cv.circle(image, (np.int32(rect[0][0]), np.int32(rect[0][1])), 2, (255, 0, 0), 2, 8, 0)
+    return image
+
+
+while(True):
+    ret, frame = capture.read()
+    if ret is True:
+        cv.imshow("video-input", frame)
+        result = process(frame)
+        cv.imshow("result", result)
+        c = cv.waitKey(50)
+        #print(c)
+        if c == 27:  #ESC
+            cv.imwrite("result.jpg", result)
+            break
+    else:
+        break
+cv.waitKey(0)
+cv.destroyAllWindows()
+

python/code_077/opencv_077.py

@@ -0,0 +1,17 @@
+import numpy as np
+import cv2 as cv
+
+cap = cv.VideoCapture('color_object.mp4')
+fgbg = cv.createBackgroundSubtractorMOG2(history=500, varThreshold=1000, detectShadows=False)
+while True:
+    ret, frame = cap.read()
+    fgmask = fgbg.apply(frame)
+    background = fgbg.getBackgroundImage()
+    cv.imshow('input', frame)
+    cv.imshow('mask',fgmask)
+    cv.imshow('background', background)
+    k = cv.waitKey(10)&0xff
+    if k == 27:
+        break
+cap.release()
+cv.destroyAllWindows()

python/code_078/README.md

@@ -0,0 +1,59 @@
+#### 视频分析 – 背景消除与前景ROI提取
+
+通过视频中的背景进行建模，实现背景消除，生成mask图像，通过对mask二值图像分析实现对前景活动对象ROI区域的提取，是很多视频监控分析软件常用的手段之一，该方法很实时！整个步骤如下：
+1.	初始化背景建模对象GMM
+2.	读取视频一帧
+3.	使用背景建模消除生成mask
+4.	对mask进行轮廓分析提取ROI
+5.	绘制ROI对象
+
+函数：
+
+OpenCV中实现的背景模型提取算法有两种，一种是基于高斯混合模型GMM实现的背景提取，另外一种是基于最近邻KNN实现的。都有相关的API可以供开发者使用。
+**相关API：**
+```
+Ptr<BackgroundSubtractorMOG2> cv::createBackgroundSubtractorMOG2(
+int  history = 500,
+double 	varThreshold = 16,
+bool  detectShadows = true
+)
+```
+>参数解释如下：
+history表示过往帧数，500帧，选择history = 1就变成两帧差
+varThreshold表示像素与模型之间的马氏距离，值越大，只有那些最新的像素会被归到前景，值越小前景对光照越敏感。
+detectShadows 是否保留阴影检测，请选择False这样速度快点。
+
+创建
+Ptr<BackgroundSubtractor> pBackSub = createBackgroundSubtractorMOG2();
+Ptr<BackgroundSubtractor> pBackSub = createBackgroundSubtractorKNN();
+
+```python
+
+# 创建背景提取器
+fgbg = cv.createBackgroundSubtractorMOG2(
+    history=500, varThreshold=100, detectShadows=False)
+
+
+def process(image, opt=1):
+
+    # 提取前景mask
+    mask = fgbg.apply(frame)
+    line = cv.getStructuringElement(cv.MORPH_RECT, (1, 5), (-1, -1))
+    mask = cv.morphologyEx(mask, cv.MORPH_OPEN, line)
+    cv.imshow("mask", mask)
+    # 轮廓提取, 发现最大轮廓
+    out, contours, hierarchy = cv.findContours(mask, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
+    for c in range(len(contours)):
+        area = cv.contourArea(contours[c])
+        if area < 150:
+            continue
+        rect = cv.minAreaRect(contours[c])
+        cv.ellipse(image, rect, (0, 255, 0), 2, 8)
+        cv.circle(image, (np.int32(rect[0][0]), np.int32(rect[0][1])), 2, (255, 0, 0), 2, 8, 0)
+    return image, mask
+
+
+```
+<img src=../code_080/input.png width=300>
+<img src=../code_080/mask.png width=300>
+<img src=../code_080/result.png width=300>

python/code_078/opencv_078.py

@@ -0,0 +1,39 @@
+import numpy as np
+import cv2 as cv
+
+cap = cv.VideoCapture('vtest.avi')
+fgbg = cv.createBackgroundSubtractorMOG2(
+    history=500, varThreshold=100, detectShadows=False)
+
+
+def process(image, opt=1):
+    mask = fgbg.apply(frame)
+    line = cv.getStructuringElement(cv.MORPH_RECT, (1, 5), (-1, -1))
+    mask = cv.morphologyEx(mask, cv.MORPH_OPEN, line)
+    cv.imshow("mask", mask)
+    # 轮廓提取, 发现最大轮廓
+    out, contours, hierarchy = cv.findContours(mask, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
+    for c in range(len(contours)):
+        area = cv.contourArea(contours[c])
+        if area < 150:
+            continue
+        rect = cv.minAreaRect(contours[c])
+        cv.ellipse(image, rect, (0, 255, 0), 2, 8)
+        cv.circle(image, (np.int32(rect[0][0]), np.int32(rect[0][1])), 2, (255, 0, 0), 2, 8, 0)
+    return image, mask
+
+
+while True:
+    ret, frame = cap.read()
+    cv.imwrite("input.png", frame)
+    cv.imshow('input', frame)
+    result, m_ = process(frame)
+    cv.imshow('result', result)
+    k = cv.waitKey(50)&0xff
+    if k == 27:
+        cv.imwrite("result.png", result)
+        cv.imwrite("mask.png", m_)
+
+        break
+cap.release()
+cv.destroyAllWindows()