Jojozzc

import json
class NumpyEncoder(json.JSONEncoder):
    """ Special json encoder for numpy types """
    def default(self, obj):
        if isinstance(obj, (np.int_, np.intc, np.intp, np.int8,
            np.int16, np.int32, np.int64, np.uint8,
            np.uint16, np.uint32, np.uint64)):
            return int(obj)
        elif isinstance(obj, (np.float_, np.float16, np.float32,
            np.float64)):

Jojozzc

import numpy as np

def resize_contours(cnts, cnts_origin_shape_WxH:tuple, to_shape_WxH:tuple):
    w_resize_rate = to_shape_WxH[0] / cnts_origin_shape_WxH[0]
    h_resize_rate = to_shape_WxH[1] / cnts_origin_shape_WxH[1]
    rate = (w_resize_rate, h_resize_rate)
    for i in range(len(cnts)):
        cnt = cnts[i] * rate
        cnts[i] = np.array(cnt, dtype='int')
    return cnts

Jojozzc

import numpy as np
import cv2
import os

def cv2_imread(filename, mode=-1):
    # 0:gray
    # 1:rgb
    # -1 any, read as the mode of source itself.
    # like cv2.imread
    cv_img = cv2.imdecode(np.fromfile(filename, dtype=np.uint8), mode)

Jojozzc

def color_kinds(img):
    color_set = set()
    if np.ndim(img) == 2:
        channel_num = 1
    elif np.ndim(img) >= 3:
        channel_num = np.size(img, np.ndim(img) - 1)
    else:
        print('unknow img format')
        return color_set

Jojozzc

clc, clear, close all;
x = -10:0.1:10;
y = 1 ./ (1 + exp(-x)); %sigmoid
plot(x, y);
set(gca,'XAxisLocation','origin','YAxisLocation','origin');

Jojozzc

{
    "files.autoGuessEncoding": false,
    "matlab.linterEncoding": "gb2312",
    "[matlab]": {
        "files.encoding": "gb2312"
    }
}