我佛了！用 KNN 实现验证码识别，又 Get 到一招

#定义函数get_rect_box，目的在于获得切割图片字符位置和宽度
def?get_rect_box(contours):
????print("获取字符轮廓。。。")
????#定义ws和valid_contours数组，用来存放图片宽度和训练数据集中的图片。如果分割错误的话需要重新分割
????ws?=?[]
????valid_contours?=?[]
????for?contour?in?contours:
????????#画矩形用来框住单个字符，x,y,w,h四个参数分别是该框子的x,y坐标和长宽。因
????????x,?y,?w,?h?=?cv2.boundingRect(contour)
????????if?w?<?7:
????????????continue
????????valid_contours.append(contour)
????????ws.append(w)
#w_min是二值化白色区域最小宽度，目的用来分割。
????w_min?=?min(ws)
#?w_max是最大宽度
????w_max?=?max(ws)
????result?=?[]
????#如果切割出有4个字符。说明没啥问题
????if?len(valid_contours)?==?4:
????????for?contour?in?valid_contours:
????????????x,?y,?w,?h?=?cv2.boundingRect(contour)
????????????box?=?np.int0([[x,y],?[x+w,y],?[x+w,y+h],?[x,y+h]])
????????????result.append(box)
????#?如果切割出有3个字符。参照文章，中间分割
????elif?len(valid_contours)?==?3:
????????for?contour?in?valid_contours:
????????????x,?y,?w,?h?=?cv2.boundingRect(contour)
????????????if?w?==?w_max:
????????????????box_left?=?np.int0([[x,y],?[x+w/2,y],?[x+w/2,y+h],?[x,y+h]])
????????????????box_right?=?np.int0([[x+w/2,y],?[x+w,y],?[x+w,y+h],?[x+w/2,y+h]])
????????????????result.append(box_left)
????????????????result.append(box_right)
????????????else:
????????????????box?=?np.int0([[x,y],?[x+w,y],?[x+w,y+h],?[x,y+h]])
????????????????result.append(box)
????#?如果切割出有3个字符。参照文章，将包含了3个字符的轮廓在水平方向上三等分
????elif?len(valid_contours)?==?2:
????????for?contour?in?valid_contours:
????????????x,?y,?w,?h?=?cv2.boundingRect(contour)
????????????if?w?==?w_max?and?w_max?>=?w_min?*?2:
????????????????box_left?=?np.int0([[x,y],?[x+w/3,y],?[x+w/3,y+h],?[x,y+h]])
????????????????box_mid?=?np.int0([[x+w/3,y],?[x+w*2/3,y],?[x+w*2/3,y+h],?[x+w/3,y+h]])
????????????????box_right?=?np.int0([[x+w*2/3,y],?[x+w,y],?[x+w,y+h],?[x+w*2/3,y+h]])
????????????????result.append(box_left)
????????????????result.append(box_mid)
????????????????result.append(box_right)
????????????elif?w_max?<?w_min?*?2:
????????????????box_left?=?np.int0([[x,y],?[x+w/2,y],?[x+w/2,y+h],?[x,y+h]])
????????????????box_right?=?np.int0([[x+w/2,y],?[x+w,y],?[x+w,y+h],?[x+w/2,y+h]])
????????????????result.append(box_left)
????????????????result.append(box_right)
????????????else:
????????????????box?=?np.int0([[x,y],?[x+w,y],?[x+w,y+h],?[x,y+h]])
????????????????result.append(box)
????#?如果切割出有3个字符。参照文章，对轮廓在水平方向上做4等分
????elif?len(valid_contours)?==?1:
????????contour?=?valid_contours[0]
????????x,?y,?w,?h?=?cv2.boundingRect(contour)
????????box0?=?np.int0([[x,y],?[x+w/4,y],?[x+w/4,y+h],?[x,y+h]])
????????box1?=?np.int0([[x+w/4,y],?[x+w*2/4,y],?[x+w*2/4,y+h],?[x+w/4,y+h]])
????????box2?=?np.int0([[x+w*2/4,y],?[x+w*3/4,y],?[x+w*3/4,y+h],?[x+w*2/4,y+h]])
????????box3?=?np.int0([[x+w*3/4,y],?[x+w,y],?[x+w,y+h],?[x+w*3/4,y+h]])
????????result.extend([box0,?box1,?box2,?box3])
????elif?len(valid_contours)?>?4:
????????for?contour?in?valid_contours:
????????????x,?y,?w,?h?=?cv2.boundingRect(contour)
????????????box?=?np.int0([[x,y],?[x+w,y],?[x+w,y+h],?[x,y+h]])
????????????result.append(box)
????result?=?sorted(result,?key=lambda?x:?x[0][0])
????return?result

def?process_im(im):
????rows,?cols,?ch?=?im.shape
????#转为灰度图
????im_gray?=?cv2.cvtColor(im,?cv2.COLOR_BGR2GRAY)
????#二值化，就是黑白图。字符变成白色的，背景为黑色
????ret,?im_inv?=?cv2.threshold(im_gray,127,255,cv2.THRESH_BINARY_INV)
????#应用高斯模糊对图片进行降噪。高斯模糊的本质是用高斯核和图像做卷积。就是去除一些斑斑点点的。因为二值化难免不够完美，去燥使得二值化结果更好
????kernel?=?1/16*np.array([[1,2,1],?[2,4,2],?[1,2,1]])
????im_blur?=?cv2.filter2D(im_inv,-1,kernel)
????#再进行一次二值化。
????ret,?im_res?=?cv2.threshold(im_blur,127,255,cv2.THRESH_BINARY)
????return?im_res

#借助第一个函数获得待切割位置和长宽后就可以切割了
def?split_code(filepath):
????#获取图片名
????filename?=?filepath.split("/")[-1]
????#图片名即为标签
????filename_ts?=?filename.split(".")[0]
????im?=?cv2.imread(filepath)
????im_res?=?process_im(im)
????im2,?contours,?hierarchy?=?cv2.findContours(im_res,?cv2.RETR_EXTERNAL,?cv2.CHAIN_APPROX_SIMPLE)
#这里就是用的第一个函数，获得待切割位置和长宽
????boxes?=?get_rect_box(contours)
#如果没有区分出四个字符，就不切割这个图片
????if?len(boxes)?!=?4:
????????print(filepath)
#?如果区分出了四个字符，说明切割正确，就可以切割这个图片。将切割后的图片保存在char文件夹下
????for?box?in?boxes:
????????cv2.drawContours(im,?[box],?0,?(0,0,255),2)
????????roi?=?im_res[box[0][1]:box[3][1],?box[0][0]:box[1][0]]
????????roistd?=?cv2.resize(roi,?(30,?30))
????????timestamp?=?int(time.time()?*?1e6)
????????filename?=?"{}.jpg".format(timestamp)
????????filepath?=?os.path.join("char",?filename)
????????cv2.imwrite(filepath,?roistd)
????#cv2.imshow("image",?im)
????#cv2.waitKey(0)
????#cv2.destroyAllWindows()
#?split?all?captacha?codes?in?training?set
#调用上面的split_code进行切割即可。
def?split_all():
????files?=?os.listdir(TRAIN_DIR)
????for?filename?in?files:
????????filename_ts?=?filename.split(".")[0]
????????patt?=?"label/{}_*".format(filename_ts)
????????saved_chars?=?glob.glob(patt)
????????if?len(saved_chars)?==?4:
????????????print("{}?done".format(filepath))
????????????continue
????????filepath?=?os.path.join(TRAIN_DIR,?filename)
????????split_code(filepath)

#用来标注单个字符图片，在label文件夹下，很明显可以看到_后面的就是标签。比如图片里是数字6，_后面就是6

def?label_data():

????files?=?os.listdir("char")

????for?filename?in?files:

????????filename_ts?=?filename.split(".")[0]

????????patt?=?"label/{}_*".format(filename_ts)

????????saved_num?=?len(glob.glob(patt))

????????if?saved_num?==?1:

????????????print("{}?done".format(patt))

????????????continue

????????filepath?=?os.path.join("char",?filename)

????????im?=?cv2.imread(filepath)

????????cv2.imshow("image",?im)

????????key?=?cv2.waitKey(0)

????????if?key?==?27:

????????????sys.exit()

????????if?key?==?13:

????????????continue

????????char?=?chr(key)

????????filename_ts?=?filename.split(".")[0]

????????outfile?=?"{}_{}.jpg".format(filename_ts,?char)

????????outpath?=?os.path.join("label",?outfile)

????????cv2.imwrite(outpath,?im)

#和标注字符图反过来，我们需要让电脑知道这个字符叫啥名字，即让电脑知道_后面的就是他字符的名字

def?analyze_label():

????print("识别数据标签中。。。")

????files?=?os.listdir("label")

????label_count?=?{}

????for?filename?in?files:

????????label?=?filename.split(".")[0].split("_")[1]

????????label_count.setdefault(label,?0)

????????label_count[label]?+=?1

print(label_count)

#训练模型，用的是k相邻算法

def?get_code(im):

????#将读取图片和标签

??? print("读取数据集和标签中。。。。")

????[samples,?label_ids,?id_label_map]?=?load_data()

????#k相邻算法

????print("初始化中...")

????model?=?cv2.ml.KNearest_create()

????#开始训练

??? print("训练模型中，请等待！")

????model.train(samples,?cv2.ml.ROW_SAMPLE,?label_ids)

????#处理图片。即二值化和降噪

????im_res?=?process_im(im)

????#提取轮廓

????im2,?contours,?hierarchy?=?cv2.findContours(im_res,?cv2.RETR_EXTERNAL,?cv2.CHAIN_APPROX_SIMPLE)

????#获取各切割区域位置和长宽

????boxes?=?get_rect_box(contours)

????#判断有没有识别出4个字符，如果没有识别出来，就不往下运行，直接结束了

????if?len(boxes)?!=?4:

????????print("cannot?get?code")

????result?=?[]

????#如果正确分割出了4个字符，下面调用训练好的模型进行识别。

????for?box?in?boxes:

????????#获取字符长宽

????????roi?=?im_res[box[0][1]:box[3][1],?box[0][0]:box[1][0]]

????????#重新设长宽。

????????roistd?=?cv2.resize(roi,?(30,?30))

????????#将图片转成像素矩阵

????????sample?=?roistd.reshape((1,?900)).astype(np.float32)

????????#调用训练好的模型识别

????????ret,?results,?neighbours,?distances?=?model.findNearest(sample,?k?=?3)

????????#获取对应标签id

????????label_id?=?int(results[0,0])

????????#根据id得到识别出的结果

????????label?=?id_label_map[label_id]

????????#存放识别结果

????????result.append(label)

????return?result

if?__name__?==?"__main__":

????file=os.listdir("test")

????filepath="test/"+file[4]

????im?=?cv2.imread(filepath)

????preds?=?get_code(im)

????preds="识别结果为："+preds[0]+preds[1]+preds[2]+preds[3]

????print(preds)

????canny0?=?im

????img_PIL?=?Image.fromarray(cv2.cvtColor(canny0,?cv2.COLOR_BGR2RGB))

????myfont?=?ImageFont.truetype(r'simfang.ttf',?18)

????draw?=?ImageDraw.Draw(img_PIL)

????draw.text((20,?5),?str(preds),?font=myfont,?fill=(255,?23,?140))

????img_OpenCV?=?cv2.cvtColor(np.asarray(img_PIL),?cv2.COLOR_RGB2BGR)

????cv2.imshow("frame",?img_OpenCV)



????key?=?cv2.waitKey(0)

????print(filepath)