init repo

01-选择ROI区域/select_roi.py

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+# -*- coding: utf-8 -*- 
+'''
+选取ROI区域
+回车或者空格确认选择
+c键 撤销选择
+'''
+import numpy as np
+import cv2
+import sys
+
+
+# 文件路径
+# img_path = 'blue-color-block.png'
+img_path = sys.argv[1]
+
+# 读入图片
+img = cv2.imread(img_path)
+# 创建一个窗口
+cv2.namedWindow("image", flags= cv2.WINDOW_NORMAL | cv2.WINDOW_FREERATIO)
+cv2.imshow("image", img)
+# 是否显示网格 
+showCrosshair = True
+
+# 如果为Ture的话 , 则鼠标的其实位置就作为了roi的中心
+# False: 从左上角到右下角选中区域
+fromCenter = False
+# Select ROI
+rect = cv2.selectROI("image", img, showCrosshair, fromCenter)
+
+print("选中矩形区域")
+(x, y, w, h) = rect
+
+# Crop image
+imCrop = img[y : y+h, x:x+w]
+
+# Display cropped image
+cv2.imshow("image_roi", imCrop)
+cv2.imwrite("image_roi.png", imCrop)
+cv2.waitKey(0)

02-图像颜色统计HSV/img_hsv_stat.py

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+# -*- coding: utf-8 -*- 
+
+'''
+绘制彩图在HSV颜色空间下的统计直方图
+'''
+
+from matplotlib import pyplot as plt
+import numpy as np
+import cv2
+import sys
+
+# 读入图片
+img_path = sys.argv[1]
+img = cv2.imread(img_path)
+# img = cv2.imread('little_chess.png')
+if img is None:
+    print("图片读入失败, 请检查图片路径及文件名")
+    exit()
+
+
+# 将图片转换为HSV格式
+img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
+# 创建画布
+fig, ax = plt.subplots()
+# Matplotlib预设的颜色字符
+hsvColor = ('y', 'g', 'k')
+# 统计窗口间隔 , 设置小了锯齿状较为明显 最小为1 最好可以被256整除
+bin_win  = 3
+# 设定统计窗口bins的总数
+bin_num = int(256/bin_win)
+# 控制画布的窗口x坐标的稀疏程度. 最密集就设定xticks_win=1
+xticks_win = 2
+# 设置标题
+ax.set_title('HSV Color Space')
+lines = []
+for cidx, color in enumerate(hsvColor):
+    # cidx channel 序号
+    # color r / g / b
+    cHist = cv2.calcHist([img], [cidx], None, [bin_num], [0, 256])
+    # 绘制折线图
+    line, = ax.plot(cHist, color=color,linewidth=8)
+    lines.append(line)  
+
+# 标签
+labels = [cname +' Channel' for cname in 'HSV']
+# 添加channel 
+plt.legend(lines,labels, loc='upper right')
+# 设定画布的范围
+ax.set_xlim([0, bin_num])
+# 设定x轴方向标注的位置
+ax.set_xticks(np.arange(0, bin_num, xticks_win))
+# 设定x轴方向标注的内容
+ax.set_xticklabels(list(range(0, 256, bin_win*xticks_win)),rotation=45)
+
+# 显示画面
+plt.show()

03-HSV颜色阈值动态调节工具/ThresholdEditorGUIHsv.py

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+# -*- coding: utf-8 -*- 
+'''
+    可视化颜色阈值调参软件
+'''
+
+import cv2
+import numpy as np
+import sys
+
+# 更新MASK图像，并且刷新windows
+def updateMask():
+    global img
+    global lowerb
+    global upperb
+    global mask
+    # 计算MASK
+    mask = cv2.inRange(img_hsv, lowerb, upperb)
+
+    cv2.imshow('mask', mask)
+
+# 更新阈值
+def updateThreshold(x):
+
+    global lowerb
+    global upperb
+
+    minH = cv2.getTrackbarPos('minH','image')
+    maxH = cv2.getTrackbarPos('maxH','image')
+    minS = cv2.getTrackbarPos('minS','image')
+    maxS = cv2.getTrackbarPos('maxS', 'image')
+    minV = cv2.getTrackbarPos('minV', 'image')
+    maxV = cv2.getTrackbarPos('maxV', 'image')
+
+    lowerb = np.int32([minH, minS, minV])
+    upperb = np.int32([maxH, maxS, maxV])
+
+    print('更新阈值')
+    print(lowerb)
+    print(upperb)
+    updateMask()
+
+def main(img):
+    global img_hsv
+    global upperb
+    global lowerb
+    global mask
+    # 将图片转换为HSV格式
+    img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
+
+    # 颜色阈值 Upper
+    upperb = None
+    # 颜色阈值 Lower
+    lowerb = None
+
+    mask = None
+
+    cv2.namedWindow('image', flags= cv2.WINDOW_NORMAL | cv2.WINDOW_FREERATIO)
+    # cv2.namedWindow('image')
+    cv2.imshow('image', img)
+
+    # cv2.namedWindow('mask')
+    cv2.namedWindow('mask', flags= cv2.WINDOW_NORMAL | cv2.WINDOW_FREERATIO)
+
+    # 红色阈值 Bar
+    ## 红色阈值下界
+    cv2.createTrackbar('minH','image',0,255,updateThreshold)
+    ## 红色阈值上界
+    cv2.createTrackbar('maxH','image',0,255,updateThreshold)
+    ## 设定红色阈值上界滑条的值为255
+    cv2.setTrackbarPos('maxH', 'image', 255)
+    cv2.setTrackbarPos('minH', 'image', 0)
+    # 绿色阈值 Bar
+    cv2.createTrackbar('minS','image',0,255,updateThreshold)
+    cv2.createTrackbar('maxS','image',0,255,updateThreshold)
+    cv2.setTrackbarPos('maxS', 'image', 255)
+    cv2.setTrackbarPos('minS', 'image', 0)
+    # 蓝色阈值 Bar
+    cv2.createTrackbar('minV','image',0,255,updateThreshold)
+    cv2.createTrackbar('maxV','image',0,255,updateThreshold)
+    cv2.setTrackbarPos('maxV', 'image', 255)
+    cv2.setTrackbarPos('minV', 'image', 0)
+
+    # 首次初始化窗口的色块
+    # 后面的更新 都是由getTrackbarPos产生变化而触发
+    updateThreshold(None)
+
+    print("调试棋子的颜色阈值, 键盘摁e退出程序")
+    while cv2.waitKey(0) != ord('e'):
+        continue
+
+    cv2.imwrite('tmp_bin.png', mask)
+    cv2.destroyAllWindows()
+
+if __name__ == "__main__":
+    # 样例图片 (从命令行中填入)
+    image_path = sys.argv[1]
+    # 样例图片 (在代码中填入)
+    # img = cv2.imread('cfs_samples.jpg')
+    img = cv2.imread(image_path)
+    if img is None:
+        print("Error: 文件路径错误，没有此图片 {}".format(image_path))
+        exit(1)
+
+    main(img)

04-色块识别/color_feature.py

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+# -*- coding: utf-8 -*- 
+'''
+颜色特征识别
+'''
+import numpy as np
+import cv2
+
+
+def color_block_finder(img, lowerb, upperb, 
+                        min_w=0, max_w=None, min_h=0, max_h=None):
+    '''
+    色块识别 返回矩形信息
+    '''
+    # 转换色彩空间 HSV
+    img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
+    # 根据颜色阈值转换为二值化图像
+    img_bin = cv2.inRange(img_hsv, lowerb, upperb)
+
+    # 寻找轮廓（只寻找最外侧的色块）
+    bimg, contours, hier = cv2.findContours(img_bin, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    # 声明画布 拷贝自img
+    canvas = np.copy(img)
+    # 外接矩形区域集合
+    rects = []
+
+    if max_w is None:
+        # 如果最大宽度没有设定，就设定为图像的宽度
+        max_w = img.shape[1]
+    if max_h is None:
+        # 如果最大高度没有设定，就设定为图像的高度
+        max_h = img.shape[0]
+
+    # 遍历所有的边缘轮廓集合
+    for cidx,cnt in enumerate(contours):
+        # 获取联通域的外界矩形
+        (x, y, w, h) = cv2.boundingRect(cnt)
+
+        if w >= min_w and w <= max_w and h >= min_h and h <= max_h:
+            # 将矩形的信息(tuple)添加到rects中
+            rects.append((x, y, w, h))
+    return rects
+
+def draw_color_block_rect(img, rects,color=(0, 0, 255)):
+    '''
+    绘制色块的矩形区域
+    '''
+    # 声明画布(canvas) 拷贝自img
+    canvas = np.copy(img)
+    # 遍历矩形区域
+    for rect in rects:
+        (x, y, w, h) = rect
+        # 在画布上绘制矩形区域（红框）
+        cv2.rectangle(canvas, pt1=(x, y), pt2=(x+w, y+h),color=color, thickness=3)
+
+    return canvas

04-色块识别/test_color_feature.py

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+# -*- coding: utf-8 -*- 
+'''
+颜色特征识别测试代码
+'''
+import numpy as np
+import cv2
+from color_feature import color_block_finder,draw_color_block_rect
+
+def test_color_block_finder_01():
+    '''
+    色块识别测试样例1 从图片中读取并且识别
+    '''
+    # 图片路径
+    img_path = "demo-pic.png"
+    # 颜色阈值下界(HSV) lower boudnary
+    lowerb = (96, 210, 85) 
+    # 颜色阈值上界(HSV) upper boundary
+    upperb = (114, 255, 231)
+
+    # 读入素材图片 BGR
+    img = cv2.imread(img_path, cv2.IMREAD_COLOR)
+    # 检查图片是否读取成功
+    if img is None:
+        print("Error: 请检查图片文件路径")
+        exit(1)
+
+    # 识别色块 获取矩形区域数组
+    rects = color_block_finder(img, lowerb, upperb)
+    # 绘制色块的矩形区域
+    canvas = draw_color_block_rect(img, rects)
+    # 在HighGUI窗口 展示最终结果
+    cv2.namedWindow('result', flags=cv2.WINDOW_NORMAL | cv2.WINDOW_FREERATIO)
+    cv2.imshow('result', canvas)
+
+    # 等待任意按键按下
+    cv2.waitKey(0)
+    # 关闭其他窗口
+    cv2.destroyAllWindows()
+
+def test_color_block_finder_02():
+    '''
+    色块识别测试样例2 从视频流中读取并且识别
+    '''
+    # 视频路径
+    video_path = 'demo-video.mkv'
+    # 颜色阈值下界(HSV) lower boudnary
+    lowerb = (96, 210, 85) 
+    # 颜色阈值上界(HSV) upper boundary
+    upperb = (114, 255, 231)
+
+
+    # 读入视频流
+    cap = cv2.VideoCapture(video_path)
+    # 色块识别结果展示
+    cv2.namedWindow('result', flags=cv2.WINDOW_NORMAL | cv2.WINDOW_FREERATIO)
+
+    while(True):
+        # 逐帧获取画面
+        # ret ？ 画面是否获取成功
+        ret, frame = cap.read()
+
+        if ret:
+            img = frame
+            # 识别色块 获取矩形区域数组
+            # 同时设定最小高度还有宽度，过滤噪声
+            rects = color_block_finder(img, lowerb, upperb,min_w=10,min_h=10)
+            # 绘制色块的矩形区域
+            canvas = draw_color_block_rect(img, rects)
+            # 在HighGUI窗口 展示最终结果 更新画面
+            cv2.imshow('result', canvas)
+
+        else:
+            print("视频读取完毕或者视频路径异常")
+            break
+
+        # 这里做一下适当的延迟，每帧延时0.1s钟
+        if cv2.waitKey(50) & 0xFF == ord('q'):
+            break
+
+    # 释放资源
+    cap.release()
+    cv2.destroyAllWindows()
+
+
+if __name__ == "__main__":
+    # 测试图片色块识别
+    # test_color_block_finder_01()
+    # 测试视频流色块识别
+    test_color_block_finder_02()

README.md

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+# OpenCV色块识别-例程演示
+
+