quantombone

function top = nms(boxes, overlap)
% top = nms_fast(boxes, overlap)
% Non-maximum suppression. (FAST VERSION)
% Greedily select high-scoring detections and skip detections
% that are significantly covered by a previously selected
% detection.
% NOTE: This is adapted from Pedro Felzenszwalb's version (nms.m),
% but an inner loop has been eliminated to significantly speed it
% up in the case of a large number of boxes
% Tomasz Malisiewicz (tomasz@cmu.edu)

iwatake2222

# -*- coding: utf-8 -*-
import cv2
import tensorflow as tf
import numpy as np

# https://www.tensorflow.org/lite/guide/hosted_models
# http://storage.googleapis.com/download.tensorflow.org/models/tflite/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip


def detect_from_camera():

akaanirban

Edit 04/11/2021: This gist is quite old now. The current version of PyTorch is 1.8.1, which is miles ahead of version 1.0.1 that I was trying to install when I wrote this gist. Therefore some of the instructions may not apply, or some dependencies may have changed or bugs taken care of. I do not currently have a Raspberry Pi to verify unfortunately. Please proceed with caution. Further, there are may others who have shared their fixes, and direct links to their wheels down in the comments. Cheers !

Install the prerequisites (the last one for numpy):

sudo apt install libopenblas-dev libblas-dev m4 cmake cython python3-yaml libatlas-base-dev

Increase the swap size:

• Stop the swap : sudo dphys-swapfile swapoff
• Modify the size of the swap by editing as root the following file : /etc/dphys-swapfile. Modify the valiable CONF_SWAPSIZE and change its value to CONF_SWAPSIZE=2048

tworuler

This script is used to convert BlazeFace tflite model to pb.

you need to get below files first.

• model: https://github.com/google/mediapipe/blob/master/mediapipe/models/face_detection_front.tflite
• flatc: https://github.com/tensorflow/tensorflow/blob/v1.14.0/tensorflow/lite/tools/visualize.py#L38
• shema: https://github.com/tensorflow/tensorflow/blob/v1.14.0/tensorflow/lite/tools/visualize.py#L37

NobuoTsukamoto

bmabir17

import tensorflow as tf
import numpy as np
import mrcnn.model as modellib # https://github.com/matterport/Mask_RCNN/
from mrcnn.config import Config
import keras.backend as keras

PATH_TO_SAVE_FROZEN_PB ="./"
FROZEN_NAME ="saved_model.pb"

tomoaki0705

はじめに

• エヌビディアの手島でございます
• この記事はOpenCVの画像処理をGPU(CUDA)で高速化する - Qiita(以降「元記事」)を読んで、最後に書かれているリクエストを検証したものです。

OpenCVでの処理(リサイズなど)を、簡単にマルチコア化する方法をどなたかご存知でしたら教えて頂けないでしょうか? TBBを使ってスレッドを作る方法は色々見つかったのですが、単にcv::resize()をマルチコア動作で高速化させたいです。

TL;DR

• Python バインディングが遅い
• OpenCV の resize はずっと昔からマルチコア対応している

NobuoTsukamoto

kounoike

ARG PYTORCH="1.5"
ARG CUDA="10.1"
ARG CUDNN="7"

FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel

ENV TORCH_CUDA_ARCH_LIST="6.0 6.1 7.0+PTX"
ENV TORCH_NVCC_FLAGS="-Xfatbin -compress-all"
ENV CMAKE_PREFIX_PATH="$(dirname $(which conda))/../"

UnaNancyOwen

OpenCV ObjDetect Module Face Recognition (SFace) Sample

• 1. Generate Aligned Faces

  python generate_aligned_faces.py image.jpg
  

  • Input