if img_path: img = cv2.imdecode(fromfile(img_path, dtype=uint8), cv2.IMREAD_COLOR)#彩色 #使用OpenCV库读取指定路径的图像文件,并解码为对应的NumPy数组格式 self.show(img, 400, oriImg) colors, lisencePlates = self.getROI(img) for m in range(len(lisencePlates)): self.show(lisencePlates[m], 40, ROIImg) letters = self.getLetters(lisencePlates[m], colors[m]) results = [] for letter in letters: feature = self.getFeature(letter) result = self.sort(feature, trainingMat, labels, 5) results.append(result) print(result) recogResult = ','.join(results) resultShow.configure(text=recogResult)
时间: 2024-02-14 14:35:40 浏览: 105
这段代码是一个 Python 类中的一个方法,用于处理车牌识别的流程。具体来说,它会读取指定路径的图像文件,解码为对应的 NumPy 数组格式,并进行一系列的处理,最终得到车牌识别的结果。
首先,代码会通过 cv2.imdecode() 函数读取指定路径的图像文件,并解码为彩色图片格式。接着,代码会调用 self.show() 方法,将读取到的图片显示在界面上。然后,代码会调用 self.getROI() 方法,对图片进行车牌的定位和提取,得到一个包含所有车牌的列表 lisencePlates。
接下来,代码会遍历 lisencePlates 中的所有车牌,依次调用 self.show() 方法将车牌显示在界面上,并调用 self.getLetters() 方法提取车牌中的字符。提取过程中,代码会使用 colors 列表记录车牌中每个字符的颜色信息。
对于每个字符,代码会调用 self.getFeature() 方法提取其特征,并调用 self.sort() 方法进行分类。分类过程中,代码会使用训练集的特征矩阵 trainingMat 和标签 labels 进行训练,并选取距离最近的 5 个样本进行分类。分类结果将被记录在 results 列表中。
最后,代码会将 results 中的分类结果拼接成一个字符串 recogResult,并将其显示在界面上。
相关问题
import pickle import numpy as np import os # from scipy.misc import imread def load_CIFAR_batch(filename): with open(filename, 'rb') as f: datadict = pickle.load(f, encoding='bytes') X = datadict[b'data'] Y = datadict[b'labels'] X = X.reshape(10000, 3, 32, 32).transpose(0, 2, 3, 1).astype("float") Y = np.array(Y) return X, Y def load_CIFAR10(ROOT): xs = [] ys = [] for b in range(1, 2): f = os.path.join(ROOT, 'data_batch_%d' % (b,)) X, Y = load_CIFAR_batch(f) xs.append(X) ys.append(Y) Xtr = np.concatenate(xs) Ytr = np.concatenate(ys) del X, Y Xte, Yte = load_CIFAR_batch(os.path.join(ROOT, 'test_batch')) return Xtr, Ytr, Xte, Yte def get_CIFAR10_data(num_training=5000, num_validation=500, num_test=500): cifar10_dir = r'D:\daima\cifar-10-python\cifar-10-batches-py' X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir) print(X_train.shape) mask = range(num_training, num_training + num_validation) X_val = X_train[mask] y_val = y_train[mask] mask = range(num_training) X_train = X_train[mask] y_train = y_train[mask] mask = range(num_test) X_test = X_test[mask] y_test = y_test[mask] mean_image = np.mean(X_train, axis=0) X_train -= mean_image X_val -= mean_image X_test -= mean_image X_train = X_train.transpose(0, 3, 1, 2).copy() X_val = X_val.transpose(0, 3, 1, 2).copy() X_test = X_test.transpose(0, 3, 1, 2).copy() return { 'X_train': X_train, 'y_train': y_train, 'X_val': X_val, 'y_val': y_val, 'X_test': X_test, 'y_test': y_test, } def load_models(models_dir): models = {} for model_file in os.listdir(models_dir): with open(os.path.join(models_dir, model_file), 'rb') as f: try: models[model_file] = pickle.load(f)['model'] except pickle.UnpicklingError: continue return models这是一个加载cifar10数据集的函数,如何修改使其能加载mnist数据集,不使用TensorFlow
可以使用以下代码加载MNIST数据集:
```
import gzip
import os
import pickle
import numpy as np
def load_mnist(path, kind='train'):
labels_path = os.path.join(path, '%s-labels-idx1-ubyte.gz' % kind)
images_path = os.path.join(path, '%s-images-idx3-ubyte.gz' % kind)
with gzip.open(labels_path, 'rb') as lbpath:
labels = np.frombuffer(lbpath.read(), dtype=np.uint8, offset=8)
with gzip.open(images_path, 'rb') as imgpath:
images = np.frombuffer(imgpath.read(), dtype=np.uint8, offset=16).reshape(len(labels), 784)
return images, labels
def get_mnist_data(num_training=5000, num_validation=500, num_test=500):
mnist_dir = r'D:\daima\mnist' # 修改为mnist数据集所在的目录
X_train, y_train = load_mnist(mnist_dir, kind='train')
X_test, y_test = load_mnist(mnist_dir, kind='t10k')
print(X_train.shape)
mask = range(num_training, num_training + num_validation)
X_val = X_train[mask]
y_val = y_train[mask]
mask = range(num_training)
X_train = X_train[mask]
y_train = y_train[mask]
mask = range(num_test)
X_test = X_test[mask]
y_test = y_test[mask]
X_train = X_train.astype('float32') / 255
X_val = X_val.astype('float32') / 255
X_test = X_test.astype('float32') / 255
return {
'X_train': X_train,
'y_train': y_train,
'X_val': X_val,
'y_val': y_val,
'X_test': X_test,
'y_test': y_test,
}
```
这个函数将会返回训练集、验证集和测试集的图像和标签。其中,图像是一个形如`(num_samples, 784)`的数组,标签是一个形如`(num_samples,)`的数组。
帮我分析一下下面代码有什么问题:#模型导入 import paddlehub as hub ocr = hub.Module(name="chinese_ocr_db_crnn_server") import cv2 import numpy as np from PIL import ImageFont,ImageDraw,Image def drawText(text, width, height, file): #创建一张全白的图片用来绘制中文 img = np.full((height, width, 3),fill_value=255,dtype=np.uint8) #文字大小 font_size = int(width/len(text)) - 5 #绘制中文 #cv2.putText(img, text ,(width - font_size/2, height - font_size/2),cv2.FONT_HERSHEY_SIMPLEX,1,(255,0,0),1) #导入字体文件 fontpath = "C:/Users/lenovo/Desktop/人工智能/chinese_cht.ttf" #设置字体的颜色 b,g,r,a = 0,0,0,0 #设置字体大小 font = ImageFont.truetype(fontpath, font_size) #将numpy array的图片格式转为PIL的图片格式 img_pil = Image.fromarray(img) #创建画板 draw = ImageDraw.Draw(img_pil) #在图片上绘制中文 draw.text((width/2 - int(len(text)*(font_size/2)), int(height/2 - font_size/2)), text, font=font, fill=(b,g,r,a)) #将图片转为numpy array的数据格式 img = np.array(img_pil) #保存图片 cv2.imwrite(f"txt/{file}",img) image_path = 'image.jpeg' # 读取测试文件夹test.txt中的照片路径 np_images =[cv2.imread(image_path)] results = ocr.recognize_text( images=np_images, # 图片数据,ndarray.shape 为 [H, W, C],BGR格式; use_gpu=False, # 是否使用 GPU;若使用GPU,请先设置CUDA_VISIBLE_DEVICES环境变量 output_dir='ocr_result', # 图片的保存路径,默认设为 ocr_result; visualization=False, # 是否将识别结果保存为图片文件; box_thresh=0.5, # 检测文本框置信度的阈值; text_thresh=0.5) # 识别中文文本置信度的阈值; image = cv2.imread(image_path) for result in results: data = result['data'] save_path = result['save_path'] for infomation in data: loca = infomation['text_box_position'] print(loca, infomation['text']) crop_img = image[loca[0][1]:loca[2][1], loca[0][0]:loca[2][0]] # 保存小图片 cv2.imwrite('txt/' + str(loca[0][1]) + str(loca[2][1]) + '.jpg', crop_img) text = infomation['text'] drawText(text, loca[2][1] - loca[0][1], loca[2][0] - loca[0][0], str(loca[0][1]) + str(loca[2][1]) + "M.jpg")
这段代码中存在两个潜在的问题:
1. OCR模型的使用问题:OCR模型需要预先安装并且需要联网下载模型文件,如果没有安装或者下载失败,会导致模型加载失败,从而无法进行文字识别。
2. 图片处理问题:在drawText函数中,width和height的顺序应该是(height, width, 3),和img的shape保持一致。此外,在使用cv2.imwrite保存图片时,目录'txt/'需要事先存在,否则会抛出FileNotFoundError异常。
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