garybradski

def rect_files(start,stop):
    startstop = range(start,stop)
    for i in startstop:
        openstr = "%09d.txt"%(i) #zero justify out to 9 places
        file = open(openstr,"w")
        file.write("[ 0, 0, 0, 0 ]")
        file.close()

garybradski

#!/bin/bash
# Useful stuff for scripts, Gary Bradski

#Python
## For python, you should run your files through yapf
sudo pip install yapf
yapf -i <path/file_to_be_yapfed>

##To convert python 2 to 3 in place: 
2to3 -w <path/file_to_be_converted>

garybradski

def skeletonize(img):
  '''
  Adapted from: 
    https://opencvpython.blogspot.com/2012/05/skeletonization-using-opencv-python.html
  '''
    assert len(img.shape) == 2  #make sure its single channel
    size = np.size(img)
    tenth_size = size/10
    skel = np.zeros(img.shape,np.uint8)

garybradski

import cv2
import numpy as np

def connected_components(thresh_img):
    thresh_img = mog_mask.copy()
    contours, hierarchy = cv2.findContours(thresh_img, cv2.RETR_TREE,
                                           cv2.CHAIN_APPROX_SIMPLE)
    fg_mask = np.zeros(frame.shape[:2], np.uint8)
    i = 0
    cs = []

garybradski

import cv2
import argparse
import os

parser = argparse.ArgumentParser()
parser.add_argument('--video', help='path to video to convert to images.')
parser.add_argument('--out', help='path to output directory.')
args = parser.parse_args()

if not os.path.exists(args.out):

garybradski

fdupes -rdN <dir>
# r is recursive
# d is keep the first file of the duplicated files
# N is run silent so you don't have to say yes or no

garybradski

# From https://gist.github.com/zacharybell/8d9b1b25749fe6494511f843361bb167
import numpy as np

def mean_iou(labels, predictions, n_classes):
    mean_iou = 0.0
    seen_classes = 0

    for c in range(n_classes):
        labels_c = (labels == c)
        pred_c = (predictions == c)

garybradski

import sys
import argparse
import cv2


MIN_PYTHON = (3, 3)
if sys.version_info < MIN_PYTHON:
    sys.exit("Python %s.%s or later is required.\n" % MIN_PYTHON)

garybradski

import numpy as np
import codecs, json 

#In order ot store an numpy array as a .json file
a = np.arange(10).reshape(2,5) # a 2 by 5 array
b = a.tolist() # nested lists with same data, indices
#    Obviously, if you already have list, you don't/can't .tolist() it
file_path = "/path.json" ## your path variable
json.dump(b, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4) ### this saves the array in .json format

garybradski

   def convert_bin_img_to_corners(img):
        '''
        Mark corners with bias being pixels origin as (x,y): (0.5, 0.5)
        :param img:     Binary image of field
        :return:        Image of corners marked with 255, all else is 0
        '''
        kernel = np.ones(
            (2, 2), np.float32)  # Make convolution kernel 2x2 of 0.25s
        ret, img_thresh = cv2.threshold(
            img, 1, 1, cv2.THRESH_BINARY)  # Make img into [0,1]