opencv-python实战

（一）信用卡数字识别

• 使用模板匹配进行数字识别

• 将信用卡里取取来的数字与模板里的每一个数字进行匹配，得分高者即是该数字

#python3 ocr_template_match.py -i images/credit_card_01.png -t images/ocr_a_reference.png

# 导入工具包
from imutils import contours
import numpy as np
import argparse
import cv2
import myutils

# 设置参数
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True,
	help="path to input image")
ap.add_argument("-t", "--template", required=True,
	help="path to template OCR-A image")
args = vars(ap.parse_args())

# 指定信用卡类型
FIRST_NUMBER = {
	"3": "American Express",
	"4": "Visa",
	"5": "MasterCard",
	"6": "Discover Card"
}
# 绘图展示
def cv_show(name,img):
	cv2.imshow(name, img)
	cv2.waitKey(0)
	cv2.destroyAllWindows()
# 读取一个模板图像
img = cv2.imread(args["template"])
cv_show('img',img)
# 灰度图
ref = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cv_show('ref',ref)
# 二值图像
ref = cv2.threshold(ref, 10, 255, cv2.THRESH_BINARY_INV)[1]
cv_show('ref2',ref)

# 计算轮廓
#cv2.findContours()函数接受的参数为二值图，即黑白的（不是灰度图）,cv2.RETR_EXTERNAL只检测外轮廓，cv2.CHAIN_APPROX_SIMPLE只保留终点坐标
#返回的list中每个元素都是图像中的一个轮廓

_,refCnts, hierarchy = cv2.findContours(ref.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)

cv2.drawContours(img,refCnts,-1,(0,0,255),3) 
cv_show('img',img)
print (np.array(refCnts).shape)
refCnts = myutils.sort_contours(refCnts, method="left-to-right")[0] #排序，从左到右，从上到下
digits = {}

# 遍历每一个轮廓
for (i, c) in enumerate(refCnts):
	# 计算外接矩形并且resize成合适大小
	(x, y, w, h) = cv2.boundingRect(c)
	roi = ref[y:y + h, x:x + w]
	roi = cv2.resize(roi, (57, 88))

	# 每一个数字对应每一个模板
	digits[i] = roi

# 初始化卷积核
rectKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (9, 3))
sqKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))

#读取输入图像，预处理
image = cv2.imread(args["image"])
cv_show('image',image)
image = myutils.resize(image, width=300)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv_show('gray',gray)

#礼帽操作，突出更明亮的区域
tophat = cv2.morphologyEx(gray, cv2.MORPH_TOPHAT, rectKernel) 
cv_show('tophat',tophat) 
# 
gradX = cv2.Sobel(tophat, ddepth=cv2.CV_32F, dx=1, dy=0, #ksize=-1相当于用3*3的
	ksize=-1)

gradX = np.absolute(gradX)
(minVal, maxVal) = (np.min(gradX), np.max(gradX))
gradX = (255 * ((gradX - minVal) / (maxVal - minVal)))
gradX = gradX.astype("uint8")

print (np.array(gradX).shape)
cv_show('gradX',gradX)

#通过闭操作（先膨胀，再腐蚀）将数字连在一起
gradX = cv2.morphologyEx(gradX, cv2.MORPH_CLOSE, rectKernel) 
cv_show('gradX',gradX)
#THRESH_OTSU会自动寻找合适的阈值，适合双峰，需把阈值参数设置为0
thresh = cv2.threshold(gradX, 0, 255,
	cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1] 
cv_show('thresh',thresh)

#再来一个闭操作
thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, sqKernel) #再来一个闭操作
cv_show('thresh',thresh)

# 计算轮廓
_,threshCnts, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
	cv2.CHAIN_APPROX_SIMPLE)
cnts = threshCnts
cur_img = image.copy()
cv2.drawContours(cur_img,cnts,-1,(0,0,255),3) 
cv_show('img2',cur_img)
locs = []

# 遍历轮廓
for (i, c) in enumerate(cnts):
	# 计算矩形
	(x, y, w, h) = cv2.boundingRect(c)
	ar = w / float(h)

	# 选择合适的区域，根据实际任务来，这里的基本都是四个数字一组
	if ar > 2.5 and ar < 4.0:

		if (w > 40 and w < 55) and (h > 10 and h < 20):
			#符合的留下来
			locs.append((x, y, w, h))

# 将符合的轮廓从左到右排序
locs = sorted(locs, key=lambda x:x[0])
output = []

# 遍历每一个轮廓中的数字
for (i, (gX, gY, gW, gH)) in enumerate(locs):
	# initialize the list of group digits
	groupOutput = []

	# 根据坐标提取每一个组
	group = gray[gY - 5:gY + gH + 5, gX - 5:gX + gW + 5]
	cv_show('group',group)
	# 预处理
	group = cv2.threshold(group, 0, 255,
		cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
	cv_show('group',group)
	# 计算每一组的轮廓
	_,digitCnts,hierarchy = cv2.findContours(group.copy(), cv2.RETR_EXTERNAL,
		cv2.CHAIN_APPROX_SIMPLE)
	digitCnts = contours.sort_contours(digitCnts,
		method="left-to-right")[0]

	# 计算每一组中的每一个数值
	for c in digitCnts:
		# 找到当前数值的轮廓，resize成合适的的大小
		(x, y, w, h) = cv2.boundingRect(c)
		roi = group[y:y + h, x:x + w]
		roi = cv2.resize(roi, (57, 88))
		cv_show('roi',roi)

		# 计算匹配得分
		scores = []

		# 在模板中计算每一个得分
		for (digit, digitROI) in digits.items():
			# 模板匹配
			result = cv2.matchTemplate(roi, digitROI,
				cv2.TM_CCOEFF)
			(_, score, _, _) = cv2.minMaxLoc(result)
			scores.append(score)
		# 得到最合适的数字
		groupOutput.append(str(np.argmax(scores)))
		
	# 画出来
	cv2.rectangle(image, (gX - 5, gY - 5),
		(gX + gW + 5, gY + gH + 5), (0, 0, 255), 1)
	cv2.putText(image, "".join(groupOutput), (gX, gY - 15),
		cv2.FONT_HERSHEY_SIMPLEX, 0.65, (0, 0, 255), 2)

	# 得到结果
	output.extend(groupOutput)
# 打印结果
print("Credit Card Type: {}".format(FIRST_NUMBER[output[0]]))
print("Credit Card #: {}".format("".join(output)))
cv2.imshow("Image", image)
cv2.waitKey(0)

 

（二）答题卡识别

• 找到选项的轮廓，从上到下，从左到右依次扫描，根据阈值判断是否选中
   

  #python3 get_answer.py -i images/example_test.png
  #导入工具包
  import numpy as np
  import argparse
  import imutils
  import cv2
  
  # 设置参数
  ap = argparse.ArgumentParser()
  ap.add_argument("-i", "--image", required=True,
  	help="path to the input image")
  args = vars(ap.parse_args())
  
  # 正确答案
  ANSWER_KEY = {0: 1, 1: 4, 2: 0, 3: 3, 4: 1}
  
  def order_points(pts):
  	# 一共4个坐标点
  	rect = np.zeros((4, 2), dtype = "float32")
  
  	# 按顺序找到对应坐标0123分别是 左上，右上，右下，左下
  	# 计算左上，右下
  	s = pts.sum(axis = 1)
  	rect[0] = pts[np.argmin(s)]
  	rect[2] = pts[np.argmax(s)]
  
  	# 计算右上和左下
  	diff = np.diff(pts, axis = 1)
  	rect[1] = pts[np.argmin(diff)]
  	rect[3] = pts[np.argmax(diff)]
  
  	return rect
  
  def four_point_transform(image, pts):
  	# 获取输入坐标点
  	rect = order_points(pts)
  	(tl, tr, br, bl) = rect
  
  	# 计算输入的w和h值
  	widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
  	widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
  	maxWidth = max(int(widthA), int(widthB))
  
  	heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
  	heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
  	maxHeight = max(int(heightA), int(heightB))
  
  	# 变换后对应坐标位置
  	dst = np.array([
  		[0, 0],
  		[maxWidth - 1, 0],
  		[maxWidth - 1, maxHeight - 1],
  		[0, maxHeight - 1]], dtype = "float32")
  
  	# 计算变换矩阵
  	M = cv2.getPerspectiveTransform(rect, dst)
  	warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))
  
  	# 返回变换后结果
  	return warped
  def sort_contours(cnts, method="left-to-right"):
      reverse = False
      i = 0
      if method == "right-to-left" or method == "bottom-to-top":
          reverse = True
      if method == "top-to-bottom" or method == "bottom-to-top":
          i = 1
      boundingBoxes = [cv2.boundingRect(c) for c in cnts]
      (cnts, boundingBoxes) = zip(*sorted(zip(cnts, boundingBoxes),
                                          key=lambda b: b[1][i], reverse=reverse))
      return cnts, boundingBoxes
  def cv_show(name,img):
          cv2.imshow(name, img)
          cv2.waitKey(0)
          cv2.destroyAllWindows()  
  
  # 预处理
  image = cv2.imread(args["image"])
  contours_img = image.copy()
  gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
  blurred = cv2.GaussianBlur(gray, (5, 5), 0)
  cv_show('blurred',blurred)
  edged = cv2.Canny(blurred, 75, 200)
  cv_show('edged',edged)
  
  # 轮廓检测
  cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,
  	cv2.CHAIN_APPROX_SIMPLE)[1]
  cv2.drawContours(contours_img,cnts,-1,(0,0,255),3) 
  cv_show('contours_img',contours_img)
  docCnt = None
  
  # 确保检测到了
  if len(cnts) > 0:
  	# 根据轮廓大小进行排序
  	cnts = sorted(cnts, key=cv2.contourArea, reverse=True)
  
  	# 遍历每一个轮廓
  	for c in cnts:
  		# 近似
  		peri = cv2.arcLength(c, True)
  		approx = cv2.approxPolyDP(c, 0.02 * peri, True)
  
  		# 准备做透视变换
  		if len(approx) == 4:
  			docCnt = approx
  			break
  
  # 执行透视变换
  
  warped = four_point_transform(gray, docCnt.reshape(4, 2))
  cv_show('warped',warped)
  # Otsu's 阈值处理
  thresh = cv2.threshold(warped, 0, 255,
  	cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1] 
  cv_show('thresh',thresh)
  thresh_Contours = thresh.copy()
  # 找到每一个圆圈轮廓
  cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
  	cv2.CHAIN_APPROX_SIMPLE)[1]
  cv2.drawContours(thresh_Contours,cnts,-1,(0,0,255),3) 
  cv_show('thresh_Contours',thresh_Contours)
  questionCnts = []
  
  # 遍历
  for c in cnts:
  	# 计算比例和大小
  	(x, y, w, h) = cv2.boundingRect(c)
  	ar = w / float(h)
  
  	# 根据实际情况指定标准
  	if w >= 20 and h >= 20 and ar >= 0.9 and ar <= 1.1:
  		questionCnts.append(c)
  
  # 按照从上到下进行排序
  questionCnts = sort_contours(questionCnts,
  	method="top-to-bottom")[0]
  correct = 0
  
  # 每排有5个选项
  for (q, i) in enumerate(np.arange(0, len(questionCnts), 5)):
  	# 排序
  	cnts = sort_contours(questionCnts[i:i + 5])[0]
  	bubbled = None
  
  	# 遍历每一个结果
  	for (j, c) in enumerate(cnts):
  		# 使用mask来判断结果
  		mask = np.zeros(thresh.shape, dtype="uint8")
  		cv2.drawContours(mask, [c], -1, 255, -1) #-1表示填充
  		cv_show('mask',mask)
  		# 通过计算非零点数量来算是否选择这个答案
  		mask = cv2.bitwise_and(thresh, thresh, mask=mask)
  		total = cv2.countNonZero(mask)
  
  		# 通过阈值判断
  		if bubbled is None or total > bubbled[0]:
  			bubbled = (total, j)
  
  	# 对比正确答案
  	color = (0, 0, 255)
  	k = ANSWER_KEY[q]
  
  	# 判断正确
  	if k == bubbled[1]:
  		color = (0, 255, 0)
  		correct += 1
  
  	# 绘图
  	cv2.drawContours(warped, [cnts[k]], -1, color, 3)
  
  
  score = (correct / 5.0) * 100
  print("[INFO] score: {:.2f}%".format(score))
  cv2.putText(warped, "{:.2f}%".format(score), (10, 30),
  	cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)
  cv2.imshow("Original", image)
  cv2.imshow("Exam", warped)
  cv2.waitKey(0)

  

（三）车道线检测

• 利用掩膜抠出车道线区域，再利用霍夫变换找出直线，再进行左右直线的拟合

  import cv2
  import numpy as np
  from moviepy.editor import VideoFileClip
  
  # 高斯滤波核大小
  blur_ksize = 5
  
  # Canny边缘检测高低阈值
  canny_lth = 50
  canny_hth = 150
  
  # 霍夫变换参数
  rho = 1
  theta = np.pi / 180
  threshold = 15
  min_line_len = 40
  max_line_gap = 20
  
  
  def process_an_image(img):
      # 1. 灰度化、滤波和Canny
      gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
      blur_gray = cv2.GaussianBlur(gray, (blur_ksize, blur_ksize), 1)
      edges = cv2.Canny(blur_gray, canny_lth, canny_hth)
  
      # 2. 标记四个坐标点用于ROI截取
      rows, cols = edges.shape
      points = np.array([[(0, rows), (460, 325), (520, 325), (cols, rows)]])
      # [[[0 540], [460 325], [520 325], [960 540]]]
      roi_edges = roi_mask(edges, points)
  
      # 3. 霍夫直线提取
      drawing, lines = hough_lines(roi_edges, rho, theta,
                                   threshold, min_line_len, max_line_gap)
  
      # 4. 车道拟合计算
      draw_lanes(drawing, lines)
  
      # 5. 最终将结果合在原图上
      result = cv2.addWeighted(img, 0.9, drawing, 0.2, 0)
  
      return result
  
  
  def roi_mask(img, corner_points):
      # 创建掩膜
      mask = np.zeros_like(img)
      cv2.fillPoly(mask, corner_points, 255)
  
      masked_img = cv2.bitwise_and(img, mask)
      return masked_img
  
  
  def hough_lines(img, rho, theta, threshold, min_line_len, max_line_gap):
      # 统计概率霍夫直线变换
      lines = cv2.HoughLinesP(img, rho, theta, threshold,
                              minLineLength=min_line_len, maxLineGap=max_line_gap)
  
      # 新建一副空白画布
      drawing = np.zeros((img.shape[0], img.shape[1], 3), dtype=np.uint8)
      # 画出直线检测结果
      # draw_lines(drawing, lines)
  
      return drawing, lines
  
  
  def draw_lines(img, lines, color=[0, 0, 255], thickness=1):
      for line in lines:
          for x1, y1, x2, y2 in line:
              cv2.line(img, (x1, y1), (x2, y2), color, thickness)
  
  
  def draw_lanes(img, lines, color=[255, 0, 0], thickness=8):
      # a. 划分左右车道
      left_lines, right_lines = [], []
      for line in lines:
          for x1, y1, x2, y2 in line:
              k = (y2 - y1) / (x2 - x1)
              if k < 0:
                  left_lines.append(line)
              else:
                  right_lines.append(line)
  
      if (len(left_lines) <= 0 or len(right_lines) <= 0):
          return
  
      # b. 清理异常数据
      clean_lines(left_lines, 0.1)
      clean_lines(right_lines, 0.1)
  
      # c. 得到左右车道线点的集合，拟合直线
      left_points = [(x1, y1) for line in left_lines for x1, y1, x2, y2 in line]
      left_points = left_points + [(x2, y2)
                                   for line in left_lines for x1, y1, x2, y2 in line]
  
      right_points = [(x1, y1)
                      for line in right_lines for x1, y1, x2, y2 in line]
      right_points = right_points + \
          [(x2, y2) for line in right_lines for x1, y1, x2, y2 in line]
  
      left_results = least_squares_fit(left_points, 325, img.shape[0])
      right_results = least_squares_fit(right_points, 325, img.shape[0])
  
      # 注意这里点的顺序
      vtxs = np.array(
          [[left_results[1], left_results[0], right_results[0], right_results[1]]])
      # d.填充车道区域
      cv2.fillPoly(img, vtxs, (0, 255, 0))
  
      # 或者只画车道线
      # cv2.line(img, left_results[0], left_results[1], (0, 255, 0), thickness)
      # cv2.line(img, right_results[0], right_results[1], (0, 255, 0), thickness)
  
  
  def clean_lines(lines, threshold):
      # 迭代计算斜率均值，排除掉与差值差异较大的数据
      slope = [(y2 - y1) / (x2 - x1)
               for line in lines for x1, y1, x2, y2 in line]
      while len(lines) > 0:
          mean = np.mean(slope)
          diff = [abs(s - mean) for s in slope]
          idx = np.argmax(diff)
          if diff[idx] > threshold:
              slope.pop(idx)
              lines.pop(idx)
          else:
              break
  
  
  def least_squares_fit(point_list, ymin, ymax):
      # 最小二乘法拟合
      x = [p[0] for p in point_list]
      y = [p[1] for p in point_list]
  
      # polyfit第三个参数为拟合多项式的阶数，所以1代表线性
      fit = np.polyfit(y, x, 1)
      fit_fn = np.poly1d(fit)  # 获取拟合的结果
  
      xmin = int(fit_fn(ymin))
      xmax = int(fit_fn(ymax))
  
      return [(xmin, ymin), (xmax, ymax)]
  
  
  if __name__ == "__main__":
      output = 'test_videos/yellow_lane_mark.mp4'
      clip = VideoFileClip("test_videos/yellow_lane.mp4")
      out_clip = clip.fl_image(process_an_image)
      out_clip.write_videofile(output, audio=False)