Datasets:
Updating README.
Browse files- tutorials/images.md +3 -8
- tutorials/metadata.md +4 -9
tutorials/images.md
CHANGED
@@ -4,14 +4,14 @@ Once you have the URLs or S3 file keys from the metadata files, you can download
|
|
4 |
#### cURL
|
5 |
Download an image from a url to a local image file with the name `image.png`:
|
6 |
```bash
|
7 |
-
curl -O image.png https://pd12m.s3.us-west-2.amazonaws.com/image.png
|
8 |
```
|
9 |
#### Python
|
10 |
Download an image from a url to a local image file with the name `image.png`:
|
11 |
```python
|
12 |
import requests
|
13 |
|
14 |
-
url = "https://pd12m.s3.us-west-2.amazonaws.com/image.png"
|
15 |
response = requests.get(url)
|
16 |
with open('image.png', 'wb') as f:
|
17 |
f.write(response.content)
|
@@ -19,11 +19,6 @@ with open('image.png', 'wb') as f:
|
|
19 |
#### img2dataset
|
20 |
You can also use the `img2dataset` tool to quickly download images from a metadata file. The tool is available [here](https://github.com/rom1504/img2dataset). The example below will download all the images to a local `images` directory.
|
21 |
```bash
|
22 |
-
img2dataset download --url_list pd12m
|
23 |
```
|
24 |
|
25 |
-
#### S3 CLI
|
26 |
-
Download an image from an S3 bucket to an image with the name `image.png`:
|
27 |
-
```bash
|
28 |
-
aws s3 cp s3://pd12m/image.png image.png
|
29 |
-
```
|
|
|
4 |
#### cURL
|
5 |
Download an image from a url to a local image file with the name `image.png`:
|
6 |
```bash
|
7 |
+
curl -O image.png https://pd12m.s3.us-west-2.amazonaws.com/images/image.png
|
8 |
```
|
9 |
#### Python
|
10 |
Download an image from a url to a local image file with the name `image.png`:
|
11 |
```python
|
12 |
import requests
|
13 |
|
14 |
+
url = "https://pd12m.s3.us-west-2.amazonaws.com/images/image.png"
|
15 |
response = requests.get(url)
|
16 |
with open('image.png', 'wb') as f:
|
17 |
f.write(response.content)
|
|
|
19 |
#### img2dataset
|
20 |
You can also use the `img2dataset` tool to quickly download images from a metadata file. The tool is available [here](https://github.com/rom1504/img2dataset). The example below will download all the images to a local `images` directory.
|
21 |
```bash
|
22 |
+
img2dataset download --url_list pd12m.01.parquet --input_format parquet --url_col url --caption_col caption --output-dir images/
|
23 |
```
|
24 |
|
|
|
|
|
|
|
|
|
|
tutorials/metadata.md
CHANGED
@@ -4,17 +4,17 @@ The metadata files are in parquet format, and contain the following attributes:
|
|
4 |
- `url`: The URL of the image.
|
5 |
- `s3_key`: The S3 file key of the image.
|
6 |
- `caption`: A caption for the image.
|
7 |
-
- `
|
8 |
-
- `mime_type`: The MIME type of the image file.
|
9 |
- `width`: The width of the image in pixels.
|
10 |
- `height`: The height of the image in pixels.
|
11 |
-
- `
|
|
|
12 |
|
13 |
#### Open a metadata file
|
14 |
The files are in parquet format, and can be opened with a tool like `pandas` in Python.
|
15 |
```python
|
16 |
import pandas as pd
|
17 |
-
df = pd.read_parquet('
|
18 |
```
|
19 |
|
20 |
#### Get URLs from metadata
|
@@ -23,8 +23,3 @@ Once you have opened a maetadata file with pandas, you can get the URLs of the i
|
|
23 |
urls = df['url']
|
24 |
```
|
25 |
|
26 |
-
#### Get S3 File Keys from metadata
|
27 |
-
You can also get the S3 file keys, which can be used to download the images using the S3 CLI:
|
28 |
-
```python
|
29 |
-
s3_keys = df['s3_key']
|
30 |
-
```
|
|
|
4 |
- `url`: The URL of the image.
|
5 |
- `s3_key`: The S3 file key of the image.
|
6 |
- `caption`: A caption for the image.
|
7 |
+
- `hash`: The MD5 hash of the image file.
|
|
|
8 |
- `width`: The width of the image in pixels.
|
9 |
- `height`: The height of the image in pixels.
|
10 |
+
- `mime_type`: The MIME type of the image file.
|
11 |
+
- `license`: The URL of the license.
|
12 |
|
13 |
#### Open a metadata file
|
14 |
The files are in parquet format, and can be opened with a tool like `pandas` in Python.
|
15 |
```python
|
16 |
import pandas as pd
|
17 |
+
df = pd.read_parquet('pd12m.01.parquet')
|
18 |
```
|
19 |
|
20 |
#### Get URLs from metadata
|
|
|
23 |
urls = df['url']
|
24 |
```
|
25 |
|
|
|
|
|
|
|
|
|
|