How to gently close Chrome using CMD or VBS?
时间: 2024-05-29 20:13:42 浏览: 20
You can use the following command in CMD to gently close Chrome:
taskkill /f /im chrome.exe
In VBS, you can use the following script:
Set objWMIService = GetObject("winmgmts:\\.\root\cimv2")
Set colProcesses = objWMIService.ExecQuery ("SELECT * FROM Win32_Process WHERE Name = 'chrome.exe'")
For Each objProcess in colProcesses
objProcess.Terminate()
Next
Both of these methods will close Chrome gracefully without any data loss or corruption.
相关问题
详细解释一下这段代码,每一句都要进行注解:for dataset in datasets: print(dataset) if dataset not in out_results: out_results[dataset] = {} for scene in data_dict[dataset]: print(scene) # Fail gently if the notebook has not been submitted and the test data is not populated. # You may want to run this on the training data in that case? img_dir = f'{src}/test/{dataset}/{scene}/images' if not os.path.exists(img_dir): continue # Wrap the meaty part in a try-except block. try: out_results[dataset][scene] = {} img_fnames = [f'{src}/test/{x}' for x in data_dict[dataset][scene]] print (f"Got {len(img_fnames)} images") feature_dir = f'featureout/{dataset}{scene}' if not os.path.isdir(feature_dir): os.makedirs(feature_dir, exist_ok=True) t=time() index_pairs = get_image_pairs_shortlist(img_fnames, sim_th = 0.5644583, # should be strict min_pairs = 33, # we select at least min_pairs PER IMAGE with biggest similarity exhaustive_if_less = 20, device=device) t=time() -t timings['shortlisting'].append(t) print (f'{len(index_pairs)}, pairs to match, {t:.4f} sec') gc.collect() t=time() if LOCAL_FEATURE != 'LoFTR': detect_features(img_fnames, 2048, feature_dir=feature_dir, upright=True, device=device, resize_small_edge_to=600 ) gc.collect() t=time() -t timings['feature_detection'].append(t) print(f'Features detected in {t:.4f} sec') t=time() match_features(img_fnames, index_pairs, feature_dir=feature_dir,device=device) else: match_loftr(img_fnames, index_pairs, feature_dir=feature_dir, device=device, resize_to=(600, 800)) t=time() -t timings['feature_matching'].append(t) print(f'Features matched in {t:.4f} sec') database_path = f'{feature_dir}/colmap.db' if os.path.isfile(database_path): os.remove(database_path) gc.collect() import_into_colmap(img_dir, feature_dir=feature_dir,database_path=database_path) output_path = f'{feature_dir}/colmap_rec_{LOCAL_FEATURE}' t=time() pycolmap.match_exhaustive(database_path) t=time() - t timings['RANSAC'].append(t) print(f'RANSAC in {t:.4f} sec')
这段代码是Python代码,用于图像匹配和三维重建。以下是每行代码的注释:
```python
for dataset in datasets: # 遍历数据集列表
print(dataset) # 输出当前数据集
if dataset not in out_results: # 如果当前数据集不在输出结果中
out_results[dataset] = {} # 添加一个空字典作为该数据集的输出结果
for scene in data_dict[dataset]: # 遍历当前数据集中的场景
print(scene) # 输出当前场景
# 如果在测试集中,但该场景的图像数据未被提供,则跳过
img_dir = f'{src}/test/{dataset}/{scene}/images'
if not os.path.exists(img_dir):
continue
try: # 尝试匹配图像并执行三维重建
out_results[dataset][scene] = {} # 添加一个空字典作为该场景的输出结果
img_fnames = [f'{src}/test/{x}' for x in data_dict[dataset][scene]] # 获取当前场景中的图像文件名列表
print(f"Got {len(img_fnames)} images")
feature_dir = f'featureout/{dataset}{scene}' # 设置特征输出目录
if not os.path.isdir(feature_dir): # 如果特征输出目录不存在,则创建该目录
os.makedirs(feature_dir, exist_ok=True)
t = time()
# 获取图像对的候选列表
index_pairs = get_image_pairs_shortlist(img_fnames, sim_th=0.5644583, min_pairs=33,
exhaustive_if_less=20, device=device)
t = time() - t
timings['shortlisting'].append(t)
print(f'{len(index_pairs)}, pairs to match, {t:.4f} sec')
gc.collect() # 执行垃圾回收以释放内存
t = time()
# 如果不使用LoFTR,则检测图像中的特征点
if LOCAL_FEATURE != 'LoFTR':
detect_features(img_fnames, 2048, feature_dir=feature_dir, upright=True,
device=device, resize_small_edge_to=600)
gc.collect()
t = time() - t
timings['feature_detection'].append(t)
print(f'Features detected in {t:.4f} sec')
# 匹配图像中的特征点
match_features(img_fnames, index_pairs, feature_dir=feature_dir, device=device)
# 如果使用LoFTR,则使用LoFTR进行特征匹配
else:
match_loftr(img_fnames, index_pairs, feature_dir=feature_dir, device=device,
resize_to=(600, 800))
t = time() - t
timings['feature_matching'].append(t)
print(f'Features matched in {t:.4f} sec')
database_path = f'{feature_dir}/colmap.db'
if os.path.isfile(database_path):
os.remove(database_path)
gc.collect()
# 将特征匹配结果导入到COLMAP数据库中
import_into_colmap(img_dir, feature_dir=feature_dir, database_path=database_path)
output_path = f'{feature_dir}/colmap_rec_{LOCAL_FEATURE}'
t = time()
# 使用COLMAP执行RANSAC算法进行三维重建
pycolmap.match_exhaustive(database_path)
t = time() - t
timings['RANSAC'].append(t)
print(f'RANSAC in {t:.4f} sec')
except Exception as e: # 捕获任何异常
print(f'Scene {scene} failed. Error: {e}')
```
此代码的主要目的是使用图像匹配和三维重建技术来重建场景。在此过程中,它使用了许多库和函数,例如os、time、gc、pycolmap等。
python 利用继承和多态概念实现球的半径、颜色,性质、生成蓝球、水球、足球、铁球的球类
在Python中,我们可以使用继承和多态的概念来创建一个通用的"球"类,然后派生出具体的子类(如篮球、水球、足球和铁球)来表示不同类型的球,每个子类可以有自己的特性和行为。这里是一个简单的例子:
```python
# 基类球(Ball)
class Ball:
def __init__(self, radius, color):
self.radius = radius
self.color = color
# 定义抽象方法,用于展示多态性
def bounce(self):
raise NotImplementedError("Subclasses must implement this method.")
# 简单的描述方法
def describe(self):
return f"{self.color} ball with a radius of {self.radius}."
# 子类篮球(Basketball),继承自Ball
class Basketball(Ball):
def bounce(self):
return f"{self.color} basketball bounces!"
# 子类水球(WaterBall)
class WaterBall(Ball):
def bounce(self):
return f"{self.color} water ball gently floats."
# 其他子类...
class Football(Ball):
def bounce(self):
return f"{self.color} football kicks off with a thud."
class IronBall(Ball):
def bounce(self):
return f"{self.color} iron ball makes a metallic sound when dropped."
# 使用示例
blue_ball = Basketball(7, "blue")
print(blue_ball.describe())
print(blue_ball.bounce()) # 输出: blue ball with a radius of 7.
water_ball = WaterBall(6, "clear")
print(water_ball.describe())
print(water_ball.bounce()) # 输出: clear water ball gently floats.
```
在这个例子中,`Ball`是父类,`Basketball`, `WaterBall`, `Football`, 和 `IronBall`是子类。`bounce()`方法被声明为抽象方法,子类需要实现自己的版本以体现多态性。每个子类可以有自己的颜色和可能的行为。
相关推荐
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![rar](https://img-home.csdnimg.cn/images/20210720083606.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![rar](https://img-home.csdnimg.cn/images/20210720083606.png)
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![pdf](https://img-home.csdnimg.cn/images/20210720083512.png)