# 基于Opencv图像识别实现答题卡识别示例详解

## 2.项目实验

```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, hierarchy = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cv2.drawContours(contours_img,cnts,-1,(0,0,255),3)
cv_show("contours_img",contours_img)
docCnt = None
```

```# 轮廓检测
cnts, hierarchy = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cv2.drawContours(contours_img,cnts,-1,(0,0,255),3)
cv_show("contours_img",contours_img)
```
```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
```
```# 执行透视变换

warped = four_point_transform(gray, docCnt.reshape(4, 2))
cv_show("warped",warped)
```

```# 找到每一个圆圈轮廓
cnts, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
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
```

```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
```

```# 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]     #从左到右排列，保持顺序：A B C D E
bubbled = None

# 遍历每一个结果
for (j, c) in enumerate(cnts):
cv2.drawContours(mask, [c], -1, 255, -1) #-1表示填充
# 通过计算非零点数量来算是否选择这个答案

# 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)

# 判断正确
if k == bubbled[1]:
color = (0, 255, 0)
correct += 1
# 绘图
cv2.drawContours(warped, [cnts[k]], -1, color, 3)
```

## 3.项目结果

```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("Exam", warped)
cv2.waitKey(0)```
```Connected to pydev debugger (build 201.6668.115)
[INFO] score: 100.00%

Process finished with exit code 0
```

## 总结

1. 图像的形态学操作，处理的每一步都应该预先思考，选择最合适的处理方式，如：未采用霍夫变换而使用了二次轮廓检测。