realphongha

# https://www.johnpapa.net/node-and-npm-without-sudo/ 
VERSION=v18.18.0
DISTRO=linux-x64

wget https://nodejs.org/dist/$VERSION/node-$VERSION-$DISTRO.tar.xz
tar -xvf node-$VERSION-$DISTRO.tar.xz
rm node-$VERSION-$DISTRO.tar.xz
mkdir ~/.nodejs
mv node-$VERSION-$DISTRO ~/.nodejs

realphongha

import cv2
import ffmpeg


def get_fps_fast(video_path):
    # using only opencv-python package, fast but can be inaccurate
    cap = cv2.VideoCapture(video_path)
    if not cap.isOpened():
        raise Exception(f"Cannot open {video_path}")
    fps = cap.get(cv2.CAP_PROP_FPS)

realphongha

### Basic configs
# source file
unbind r
bind r source-file ~/.tmux.conf

# new prefix
# unbind C-b
# set -g prefix C-a

# act like vim

realphongha

import numpy as np


def warp_affine(img, M, fill_value=127):
    new_img = np.full_like(img, fill_value, dtype=img.dtype)
    h, w = img.shape[:2]
    y, x = np.indices((h, w))
    x = x.flatten()
    y = y.flatten()
    ones = np.ones((h * w, 1))

realphongha

import numpy as np
import cv2


def img_horizontal_concat(img1, img2, img_size=(240, 480)):
    img1_resized = cv2.resize(img1, img_size)
    img2_resized = cv2.resize(img2, img_size)
    return np.concatenate((img1_resized, img2_resized), axis=1)

realphongha

def iou_calc(boxes1, boxes2):
    boxes1_area = (boxes1[2] - boxes1[0]) * (boxes1[3] - boxes1[1])
    boxes2_area = (boxes2[2] - boxes2[0]) * (boxes2[3] - boxes2[1])

    left_up = np.maximum(boxes1[:2], boxes2[:2])
    right_down = np.minimum(boxes1[2:-2], boxes2[2:-2])

    inter_section = np.maximum(right_down - left_up, 0.0)
    inter_area = inter_section[0] * inter_section[1]

realphongha

import cv2


def bbox_percentage_to_pixel(bbox, h, w):
    x1, y1, x2, y2 = bbox
    return x1 * w, y1 * h, x2 * w, y2 * h
    
    
def draw_bboxes_on_image(img, bboxes, show=True, percentage=True):
    h, w = img.shape[:2]

realphongha

import cv2
import numpy as np


def horizontal_concat(img1, img2, img_size):
    img1_resized = cv2.resize(img1, img_size)
    img2_resized = cv2.resize(img2, img_size)
    return np.concatenate((img1_resized, img2_resized), axis=1)

realphongha

import numpy as np


class PerfMonitorMixin:
    PERF_MONITOR = {}

    def update_perf(self, name, latency, ignore_classwise=False):
        if not ignore_classwise:
            name = self.__class__.__name__ + "_" + name
        if name not in PerfMonitorMixin.PERF_MONITOR: