A function that applies deep dream to an image using a pre-trained CNN trained on the ImageNet dataset.
Installation
pip install dreamify
Testing it
dreamify
Usage
To apply Dreamify to an image, use the following Python script:
from dreamify.deepdream import deepdream
image_path = "example.jpg"
deepdream(image_path)
You may customize the behavior of the dreamifyer by selecting a different pre-trained model, saving it as a video, etc.:
from dreamify.deepdream import deepdream
image_path = "example.jpg"
deepdream(
image_path,
output_path="deepdream.png",
model_name="inception_v3",
iterations=100,
learning_rate=0.01,
octaves=range(-2, 3),
octave_scale=1.3,
save_video=False,
save_gif=False,
duration=3,
vid_duration=3,
gif_duration=3,
mirror_video=False,
seed=None,
)
You may also use an object oriented approach for fine-grained behavior:
from dreamify.deepdream import DeepDream
# Default settings
image_path1 = "example1.jpg"
deepdream = DeepDream()
deepdream(image_path1)
deepdream.save_video(output_path=dream1.mp4, duration=42, mirror_video=False)
deepdream.save_gif(output_path=dream1.gif, duration=69, mirror_video=True)
##############################################################################
# Configured settings
image_path2 = "example2.jpg"
deepdream = DeepDream(iterations=50, learning_rate=0.1)
deepdream(image_path2)
deepdream.save_video(output_path=dream2.mp4, duration=42, mirror_video=False)
deepdream.save_gif(output_path=dream2.gif, duration=69, mirror_video=True)
Available Models
Dreamify supports the following models:
| Model Name | Enum Value |
|---|---|
| VGG19 | vgg19 |
| ConvNeXt-XL | convnext_xl |
| DenseNet121 | densenet121 |
| EfficientNet-V2L | efficientnet_v2l |
| Inception-ResNet-V2 | inception_resnet_v2 |
| Inception-V3 (Default) | inception_v3 |
| ResNet152V2 | resnet152v2 |
| Xception | xception |
| MobileNet-V2 | mobilenet_v2 |
Other Examples
DeepDream
Dream (shallow) -- See documentation of dream (shallow).
1 maintainer
Release files for dreamify 1.1.4
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| dreamify-1.1.4.tar.gz | 15.4 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| dreamify-1.1.4-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 30.9 MB
Release files / dreamify-1.1.4.tar.gz
| Download URL | dreamify-1.1.4.tar.gz |
|---|---|
| Size | 15.4 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
07f2617402a4b5e4852f550fd3ebaf61ed4310b727dd72ea3ff32b4a9a73238e
|
|
BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
poetry/2.1.1 CPython/3.10.13 Linux/6.1.43
|
Release files / dreamify-1.1.4-py3-none-any.whl
| Download URL | dreamify-1.1.4-py3-none-any.whl |
|---|---|
| Size | 15.4 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
364b63faf9fe28710b53ee6c46a383ad8cb6159f4f67efcafb028ce73917025d
|
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BLAKE2b-256 checksum How to use checksums |
7bb45fbd501fdaad2a2b741ed9706b4231241b41f4ee090de6cac3de03606681
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| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
poetry/2.1.1 CPython/3.10.13 Linux/6.1.43
|
