purrsong
Installation
pip install purrsongStart
import purrsong as ps
ps.__version__
>>> 0.1.6Create Anaconda Environment(Optional)
conda create -n purrsong python=3.6
conda activate purrsongRequirments
- tensorflow
- opencv-python
- tqdm
- requests
- pandas
- matplotlib
Auto-download-extract-load of datasets, modelsets
Below function automatically download data or models and save locally If data is already exists, returns data directory or model filepath
import purrsong as ps
model_list = ps.list_models() # or ps.list_models(fresh=True)
print(model_list)
ps.load_model('bbs') # or ps.load_model('bbs', fresh=True)
dataset_list = ps.list_datasets() # or ps.list_datasets(fresh=True)
print(dataset_list)
ps.load_dataset('cat') # or ps.load_dataset('cat', fresh=True)Manipulating cats dataset
You can play with auto downloaded cats dataset by below example.
Try changing factor arg, which will return different size of bounding boxes.
load cats dataset
import purrsong as ps
import matplotlib.pyplot as plt
cats = ps.load_cats()showing cat image
img = cats[0]['image']
plt.imshow(img)
plt.show()showing cat image with landmark
img = cats[0]['image']
lmk = cats[0]['landmark']
x, y = lmk.T
plt.imshow(img)
plt.scatter(x, y)
plt.show()showing cat face image
img = cats.face_img(44) # or img = cats.face_img(idx=44, factor=1.7)
plt.imshow(img)
plt.show()showing cat left eye image
img = cats.left_eye_img(44) # or img = cats.face_img(idx=44, factor=1.7)
plt.imshow(img)
plt.show()available data features
import purrsong as ps
cats = ps.load_cats()
cat = cats(0) # or cats(idx=0, factor=1.7)
cat['image'] # cats.image(0)
cat['landmark'] # cats.landmark(0)
cat['face'] # cats.face(0, factor=1.7)
cat['face_bb'] # cats.face_bb(0, factor=1.7)
cat['face_img'] # cats.face_img(0, factor=1.7)
cat['face_lmk'] # cats.face_lmk(0, factor=1.7)
cat['eye'] # cats.eye(0, factor=1.7)
cat['left_eye_bb'] # cats.left_eye_bb(0, factor=1.7)
cat['left_eye_img'] # cats.left_eye_img(0, factor=1.7)
cat['right_eye_bb'] # cats.right_eye_bb(0, factor=1.7)
cat['right_eye_img'] # cats.right_eye_img(0, factor=1.7)
cat['nose'] # cats.nose(0, factor=1.7)
cat['nose_bb'] # cats.nose_bb(0, factor=1.7)
cat['nose_img'] # cats.nose_img(0, factor=1.7)
cat['ear'] # cats.ear(0, factor=1.7)
cat['left_ear_bb'] # cats.left_ear_bb(0, factor=1.7)
cat['left_ear_img'] # cats.left_ear_img(0, factor=1.7)
cat['right_ear_bb'] # cats.right_ear_bb(0, factor=1.7)
cat['right_ear_img'] # cats.right_ear_img(0, factor=1.7)left dict form is much more intuitive and good
when you have to handle many of features at the same time.
right method way is good when you access specific feature.
