import cv2 import random import numpy as np img=cv2.imread("D:/CV_data/test/resize/1.jpg") percent =rand_point_percent=0.03 num = int(percent * img.shape[0] * img.shape[1]) for i in range(num): rand_x = random.randint(0, img.shape[0] - 1) rand_y = random.randint(0, img.shape[1] - 1) if random.randint(0, 1) == 0: img[rand_x, rand_y] = 0 else: img[rand_x, rand_y] = 255 cv2.imshow("output",img) cv2.waitKey() cv2.destroyAllWindows()
时间: 2024-04-18 19:23:45 浏览: 10
这段代码使用了OpenCV库进行图像处理。首先,通过cv2.imread函数读取了一张图片。然后,定义了一个变量percent表示随机点的比例,rand_point_percent表示随机点的比例。接着,计算了需要生成的随机点的数量num。然后,通过循环生成num个随机点,其中rand_x和rand_y分别表示随机点的横坐标和纵坐标。通过random.randint函数生成0到img.shape[0]-1和0到img.shape[1]-1之间的随机整数作为坐标值。如果random.randint(0, 1)的结果为0,则将对应位置的像素值设置为0,否则设置为255。最后,通过cv2.imshow函数显示处理后的图像,并通过cv2.waitKey和cv2.destroyAllWindows函数等待并关闭窗口。
请注意确保图像路径正确,并且已经安装了cv2和numpy库。如果有任何问题,请及时提出。
相关问题
用python实现编码:结果: 实现老鼠和鸭的分类 过程: 1. 自己上网寻找老鼠和鸭的图像 2. 自己完成图像的预处理(形成64x64 的3通道图像) 3. 自己对数据集进行分类 4. 自己依据分类后的数据集进行训练 5. 根据自己的训练结果随意找一张图像预测输出对应的标签
好的,下面是用 Python 实现老鼠和鸭的分类的代码:
1. 导入必要的库
```python
import os
import numpy as np
import cv2
from sklearn.model_selection import train_test_split
from keras.utils import to_categorical
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
```
2. 定义数据集路径和图像大小
```python
DATASET_PATH = 'dataset'
IMG_SIZE = 64
```
3. 加载数据集并进行预处理
```python
def load_dataset():
X, y = [], []
for label, name in enumerate(['duck', 'mouse']):
folder_path = os.path.join(DATASET_PATH, name)
for filename in os.listdir(folder_path):
img_path = os.path.join(folder_path, filename)
img = cv2.imread(img_path, cv2.IMREAD_COLOR)
img = cv2.resize(img, (IMG_SIZE, IMG_SIZE))
X.append(img)
y.append(label)
X = np.array(X)
y = to_categorical(y, num_classes=2)
return X, y
X, y = load_dataset()
```
4. 划分训练集和测试集
```python
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
```
5. 定义模型
```python
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(IMG_SIZE, IMG_SIZE, 3)))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(2, activation='softmax'))
```
6. 编译模型
```python
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
```
7. 训练模型
```python
model.fit(X_train, y_train, batch_size=32, epochs=10, validation_data=(X_test, y_test))
```
8. 预测新图像
```python
def predict_image(filename):
img_path = os.path.join(DATASET_PATH, filename)
img = cv2.imread(img_path, cv2.IMREAD_COLOR)
img = cv2.resize(img, (IMG_SIZE, IMG_SIZE))
img = np.array(img).reshape(-1, IMG_SIZE, IMG_SIZE, 3)
pred = model.predict(img)
return np.argmax(pred)
print(predict_image('duck.jpg')) # 输出 0
print(predict_image('mouse.jpg')) # 输出 1
```
这样就完成了老鼠和鸭的分类任务。
分别使用计算机视觉完成车牌检测,然后利用svm对车牌进行识别代码
好的,我可以为你提供一个使用计算机视觉完成车牌检测,然后利用SVM对车牌进行识别的代码示例。以下是一个基于OpenCV和scikit-learn的示例代码:
```python
import cv2
import numpy as np
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
# 读取车牌图像和标签
def load_data():
images = []
labels = []
for i in range(1, 201):
img = cv2.imread("plates/plate{}.jpg".format(i))
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img_gray = cv2.resize(img_gray, (80, 20))
images.append(img_gray)
with open("labels/plate{}.txt".format(i), "r") as f:
label = f.read().strip()
labels.append(label)
return images, labels
# 提取车牌特征
def extract_features(images):
features = []
for img in images:
hist = cv2.calcHist([img], [0], None, [256], [0, 256])
hist = np.reshape(hist, (-1,))
features.append(hist)
features = np.array(features)
return features
# 训练SVM模型
def train_model(X_train, y_train):
svm = SVC(kernel="linear", C=1)
svm.fit(X_train, y_train)
return svm
# 测试SVM模型
def test_model(svm, X_test, y_test):
y_pred = svm.predict(X_test)
acc = accuracy_score(y_test, y_pred)
return acc
# 加载数据
images, labels = load_data()
# 提取特征
features = extract_features(images)
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2, random_state=42)
# 训练模型
svm = train_model(X_train, y_train)
# 测试模型
acc = test_model(svm, X_test, y_test)
print("模型准确率:{:.2f}%".format(acc * 100))
```
上述代码中,需要将车牌图像保存在"plates"文件夹中,标签保存在"labels"文件夹中。运行代码后,将会输出模型的准确率。
注意,上述代码只是一个简单的示例,实际上在车牌识别中还需要进行很多优化和处理,例如车牌定位、字符分割、字符识别等。如果需要更加完整和精确的车牌识别算法,建议使用深度学习算法,例如基于CNN的字符识别网络。