get_new_img_size(image_shape[0], image_shape[1])
时间: 2023-03-30 19:04:51 浏览: 90
这个问题我可以回答。get_new_img_size(image_shape[], image_shape[1])是一个函数调用,它的作用是根据输入的图片大小,返回一个新的图片大小,具体实现需要看函数的定义和参数。
相关问题
opencv find_shape_model
OpenCV provides a function called `cv2.findContours()` that can be used to find the contours of a shape in an image.
To find a specific shape model, you can first create a template of the shape you want to find. This can be done by drawing the shape on a blank image and saving it.
Then, you can use the `cv2.matchTemplate()` function to match the template to the image and find the location of the shape.
Here is an example code snippet for finding a circle shape model in an image:
```python
import cv2
# Load the image
img = cv2.imread('image.jpg')
# Create a template of the circle shape
template = cv2.imread('circle_template.jpg', cv2.IMREAD_GRAYSCALE)
# Find the contours of the image
_, contours, _ = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# Loop through the contours and match the template
for contour in contours:
# Get the bounding box of the contour
x, y, w, h = cv2.boundingRect(contour)
# Crop the image to the bounding box
roi = img[y:y+h, x:x+w]
# Match the template to the ROI
result = cv2.matchTemplate(roi, template, cv2.TM_CCOEFF_NORMED)
# Set a threshold for the match result
threshold = 0.7
# Find the location of the shape
locations = np.where(result >= threshold)
# Draw a rectangle around the shape
for loc in zip(*locations[::-1]):
cv2.rectangle(img, (x+loc[0], y+loc[1]), (x+loc[0]+w, y+loc[1]+h), (0, 0, 255), 2)
# Display the image with the detected shape
cv2.imshow('Detected shape', img)
cv2.waitKey(0)
cv2.destroyAllWindows()
```
Note that this is just one way to find a shape model in an image using OpenCV. Depending on the complexity of the shape, you may need to use more advanced techniques such as feature detection or machine learning.
bm_image_to_tensor 详解
`bm_image_to_tensor`是百度PaddleX中的一个函数,用于将图像转换为Tensor格式。其详细解释如下:
函数原型:`bm_image_to_tensor(image, order='hwc')`
参数说明:
- `image`:输入的图像数据,可以是numpy.ndarray或者PIL.Image.Image格式。
- `order`:图像数据的通道顺序,可以是'hwc'(默认)或'chw'。
返回值:返回Tensor格式的图像数据。
函数作用:将输入的图像数据转换为Tensor格式,方便输入到模型中进行预测。
具体实现细节:
- 对于numpy.ndarray格式的图像数据,`bm_image_to_tensor`函数会将其转换为Tensor格式,并将像素值归一化至[0, 1]之间。
- 对于PIL.Image.Image格式的图像数据,`bm_image_to_tensor`函数会先将其转换为numpy.ndarray格式,然后再按照上述方式进行处理。
- `order`参数用于指定图像数据的通道顺序,如果是'hwc'(默认),则表示通道顺序为(height, width, channel),如果是'chw',则表示通道顺序为(channel, height, width)。
示例代码:
```python
import paddle
from paddle.vision.transforms import Compose, Normalize
from paddlex.paddleseg.cvlibs import manager
from paddlex.paddleseg.transforms import *
from paddlex.paddleseg.utils import get_encoding, get_palette
from paddlex import transforms as T
from paddlex.paddleseg.datasets import Dataset
from paddlex.paddleseg.datasets import transforms
# 定义数据增强方式
train_transforms = [
T.RandomPaddingCrop(crop_size=512),
T.RandomHorizontalFlip(),
T.Normalize(),
T.ToTensor(),
]
# 定义数据集
train_dataset = Dataset(
data_dir='data/seg_dataset/train',
file_list='data/seg_dataset/train_list.txt',
label_list='data/seg_dataset/labels.txt',
transforms=train_transforms,
shuffle=True)
# 定义数据加载器
train_loader = paddle.io.DataLoader(
train_dataset,
batch_size=4,
shuffle=True,
drop_last=True,
num_workers=2,
use_shared_memory=True)
# 加载数据
for data in train_loader:
img = data[0][0]
label = data[1][0]
img = transforms.bm_image_to_tensor(img, order='hwc')
label = transforms.bm_image_to_tensor(label, order='hwc')
print('img shape: ', img.shape)
print('label shape: ', label.shape)
break
```
在以上示例代码中,我们使用`bm_image_to_tensor`函数将输入的图像数据转换为Tensor格式,并打印出其形状。
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