Student: Xavier Weber
Mentors: Vladimir Tyan & Yida Wang
Student on the same project: Fanny Monori
Link to accomplished work:
- PR in the opencv_contrib repository: opencv_contrib/pull/2231
user=juergenhoetzel | |
while curl -s "https://api.github.com/users/$user/starred?per_page=100&page=${page:-1}" \ | |
|jq -r -e '.[].full_name' && [[ ${PIPESTATUS[1]} != 4 ]]; do | |
let page++ | |
done |
# ================================================================== | |
# module list | |
# ------------------------------------------------------------------ | |
# darknet latest (git) | |
# torch latest (git) | |
# python 3.8 (apt) | |
# pytorch latest (pip) | |
# onnx latest (pip) | |
# theano latest (git) | |
# tensorflow latest (pip) |
import streamlit as st | |
import os | |
import sys | |
import importlib.util | |
# Parse command-line arguments. | |
if len(sys.argv) > 1: | |
folder = os.path.abspath(sys.argv[1]) | |
else: | |
folder = os.path.abspath(os.getcwd()) |
""" | |
Example of a Streamlit app for an interactive Prodigy dataset viewer that also lets you | |
run simple training experiments for NER and text classification. | |
Requires the Prodigy annotation tool to be installed: https://prodi.gy | |
See here for details on Streamlit: https://streamlit.io. | |
""" | |
import streamlit as st | |
from prodigy.components.db import connect | |
from prodigy.models.ner import EntityRecognizer, merge_spans, guess_batch_size |
Student: Xavier Weber
Mentors: Vladimir Tyan & Yida Wang
Student on the same project: Fanny Monori
Link to accomplished work:
import base64 | |
import numpy as np | |
from pycocotools import _mask as coco_mask | |
import typing as t | |
import zlib | |
def encode_binary_mask(mask: np.ndarray) -> t.Text: | |
"""Converts a binary mask into OID challenge encoding ascii text.""" | |
# check input mask -- | |
if mask.dtype != np.bool: |
def frames_to_TC (frames): | |
h = int(frames / 86400) | |
m = int(frames / 1440) % 60 | |
s = int((frames % 1440)/24) | |
f = frames % 1440 % 24 | |
return ( "%02d:%02d:%02d:%02d" % ( h, m, s, f)) | |
# Breakdown of the steps above: | |
# Hours: Divide frames by 86400 (# of frames in an hour at 24fps). Round down to nearest integer. |
import warnings | |
from skimage.measure import compare_ssim | |
from skimage.transform import resize | |
from scipy.stats import wasserstein_distance | |
from scipy.misc import imsave | |
from scipy.ndimage import imread | |
import numpy as np | |
import cv2 | |
## |
import os | |
# convert all image like `<Any_name>_2x.png` to `<Any_name>@2x.png` | |
[os.rename(f, f.replace('_2x', '@2x')) for f in os.listdir('.') if not f.startswith('.')] |