Loading repository data…
pooya-mohammadi / repository
This is a minimal implementation of face-detection models using flask, gunicorn, nginx, docker, and docker-compose
A transparent discovery signal based on current public GitHub metadata.
This score does not audit code, security, maintainers, documentation quality, or suitability. Verify the repository and its current documentation before adoption.
This is a simple implementation of dockerized face-detection restful-API implemented with flask, Nginx, and scaled up with Gunicorn. This web service takes in an image and returns face-box coordinates.
workers in the docker-compose.yml file.FACE_DETECTION_MODEL in the docker-compose.yml file.app/base_app.py file.~/.deep_utils
directory and place it inside app/weights directory. This will save you tons of time while working with models with
heavy weights.tensorflow, comment the pytorch installation section in app/Dockerfile and
uncomment the tensorflow installation lines.To run the API, install docker and docker-compose, execute the following command:
docker-compose up --build
sudo docker-compose up --build
To send an image and get back the boxes run the following commands:
curl --request POST ip:port/endpoint -F image=@img-add
Selected from shared topics, language and repository description—not editorial ratings.
immobiliare /
This project is a versatile load testing tool designed to evaluate the performance and resilience of web services and APIs.
46/100 healthtwirapp /
This is a simple server that uses Silero models to convert text to audio files over HTTP
59/100 healthIf you run the service on your local system the following request shall work perfectly:
curl --request POST http://127.0.0.1:8000/face -F image=@./sample-images/movie-stars.jpg
The output will be as follows:
{
"face_1":[269,505,571,726],
"face_10":[73,719,186,809],
"face_11":[52,829,172,931],
"face_2":[57,460,187,550],
"face_3":[69,15,291,186],
"face_4":[49,181,185,279],
"face_5":[53,318,205,424],
"face_6":[18,597,144,716],
"face_7":[251,294,474,444],
"face_8":[217,177,403,315],
"face_9":[175,765,373,917]
}
If you find something missing, please open an issue or kindly create a pull request.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at: http://www.apache.org/licenses/LICENSE-2.0.
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and limitations under the License.
Kianoush-h /
This project is a simple web application built with Python and Flask that allows users to upload a picture of a product, such as a pen, and then performs a Google image search to find stores that sell the product. The app then extracts and presents a list of stores along with their prices for the identified product.
satyarth12 /
This API is built using django rest framework and has all the features (Vendor and Customer) of a food delivery app, say Zomato.
67/100 healthAiGptCode /
This Python script is a command-line interface (CLI) for interacting with OpenAI's GPT-3.5 Turbo model. It allows you to have conversations with the model, generate commands, and more.
42/100 healtheduardo-da-silva /
The main purpose of this project is to create a mock API Rest with Flask, to be used in the development of the frontend of the mobile project.
34/100 health