tacv 1.1.1

A mini package for daily tasks

TACV - A mini package for daily tasks

Installation

pip install tacv

Examples

2D Object Detection

For now, CenterNet supported. However, use it as prototype purpose only, there is no official benchmark on accuracy.

• First, create a config file for training/model config, see full config at tacv/detection/sample_config.yml.

input_size: &input_size [ 224,448 ]
max_object: &max_obj 16
num_classes: &num_classes 5
train_config:
  gpus: 0 # 0 means CPU, N means using N available GPU(s) for training
  epoch: 600
  batch_size: 32
  shuffle: True
  num_workers: 4
  learning_rate: 0.0001
  lr_decay_milestones: [ 80,160 ]
  lr_decay_gamma: 0.5
  weight_decay: 0.01
  checkpoint_frequency: 1
  amp: True
  unfreeze_bbone_epoch: 200
  initial_denom_lr: 5
  loss_hm_reg_offset_weights: [ 1, 1, 0.1 ]
  callback:
    monitor: "val_loss"
    dirpath: "logs/exp_name_1"
    save_top_k: 20
    mode: "min"
val_config:
  batch_size: 1
  checkpoint: ""
model:
  num_classes: *num_classes
  backbone_layers: 18
  head_conv_channel: 64
  max_object: *max_obj
  input_shape: *input_size

• Second, create your own Dataset class that returns data as described in the __getitem__() method, see following example:

from torch.utils.data import Dataset
import torch

class MockDataset(Dataset):
    def __init__(self, max_objs):
        self.max_objs = max_objs

    def __getitem__(self, item):
        image = torch.rand(3, 224, 448)  # Shape = (3, H, W)
        annos = torch.rand(self.max_objs, 5)  # Shape = (MaxObjs x 5) , each row presents for (x,y,w,h,class_id)
        masks = torch.zeros(
            self.max_objs)  # Shape = (MaxObjs,)  each value is False or True (1 indicates having object)
        masks[0:3] = True
        return {"image": image, "annos": annos, "masks": masks}

    def __len__(self):
        return 1000

• Init CenterNetTrainer and here we go

from tacv.detection import CenterNetTrainer
from torch.utils.data import random_split

config_path = "tacv/detection/sample_config.yml"
dataset = MockDataset(max_objs=16)  # Replace with your custom dataset
train_set, val_set = random_split(dataset, [len(dataset)*0.9, len(dataset)-len(dataset)*0.9])

trainer = CenterNetTrainer(train_set, val_set, config_path)
trainer.train()

• Finally, do inference (TO BE UPDATED after coffee time).

File utils

Get all file paths from a directory

from tacv.fileUtils import get_all_files
file_paths = get_all_files("dir_name")

Returns a list of file absolute paths, for example

['./venvCondaTest/x86_64-conda_cos6-linux-gnu/bin/ld', './venvCondaTest/conda-meta/_libgcc_mutex-0.1-main.json', './venvCondaTest/conda-meta/xz-5.2.5-h7b6447c_0.json', './venvCondaTest/conda-meta/wheel-0.37.1-pyhd3eb1b0_0.json', './venvCondaTest/conda-meta/setuptools-58.0.4-py36h06a4308_0.json', './venvCondaTest/conda-meta/ca-certificates-2021.10.26-h06a4308_2.json', './venvCondaTest/conda-meta/readline-8.1.2-h7f8727e_1.json', './venvCondaTest/conda-meta/sqlite-3.37.2-hc218d9a_0.json', './venvCondaTest/conda-meta/libgcc-ng-9.3.0-h5101ec6_17.json', './venvCondaTest/conda-meta/ncurses-6.3-h7f8727e_2.json']

Save/load json data to/from file

from tacv.fileUtils import save_json,load_json

json_file = "myfile.json"
json_data = {"name":"Ta","age":100}
# save json
save_json(json_file,json_data)
# load json
json_data = load_json(json_file)

Visual

Draw 2D points onto an image

import cv2
from tacv.visual import draw_points
image = cv2.imread("myimage.jpg")
points = [(18,19),(55,55),(102,22),(66,22)]
draw_points(image,points,circular=True,color=(0,255,0),thickness=2)
cv2.imwrite("new_image.jpg",image)

Video

Synthesize a video from images

from tacv.video import images2video
image_dir = "my_images" #directory containing images in the same format
video_path = "tacv_test.mp4" #path to save the synthesized video
# common use case
images2video(image_dir,video_path,fps=24, image_ext = None, sort=False)

Parameters:

• fps: default = 24
• image_ext: a string, specify image extension to synthesize the video, for example (jpg, png,...). If it is None. All images will be grabbed. Default is None.
• sort: True or False. Indicate if the images should be sorted by name before synthesizing the video. Default is True.

Extract images from a video

from tacv.video import video2images
video_path = "tacv_test.mp4" #path to video to be extracted to images
image_dir = "my_images" #directory to save the extracted images
video2images(video_path,image_dir,exist_ok=False, image_ext="jpg", verbose=True)

Parameters:

• exist_ok: default is False. If image_dir already contains images and this flag is False. The process will be cancel, otherwise it continues.
• image_ext: a string, specify image extension, for example (jpg, png,...). If it is None. All images will be grabbed. Default is None.
• verbose: True or False. Set it to True to view the extracting process. Default is True.

Geometry

Calculate 2D IOU of two polygons

from tacv.geometry import iou_2d
polygon_1 = [[0,0],[10,10],[0,10]]
polygon_2 = [[0, 20], [10, 10], [0, 0]]
print(iou_2d(polygon_1,polygon_2))

Command Line Interface

Synthesize a video from images

tacv_i2v image_dir video_path [optional: fps image_ext]

Extract images from a video

tacv_v2i video_path image_dir

For more

• Visit args description in source code
• Visit test.py file