PyPI 官网下载 | salesvision-0.1.6.tar.gz

# Salesvision API Salesvision API provides accurate, reliable and scalable fashion image analysis by endpoints. It includes free public methods and python API client # Fashion analysis Fashion analysis includes recognition of items: - Category - Tags/attributes - Mask - Color ## Category detection The following solution will detect 27 categories:  ## Tag/attribute recognition The solution will specify tags from 7 main categories and styles such as described below:  On the real world images will have such output for different fashion items:  ## Color extraction Here is example of how color is been extracted from the fashion item mask covered area:  # Public methods ### Curl HTTP Request > Public endpoint for fashion analysis with time measurement ```shell `IMG_URL=https%3A%2F%2Fis4.revolveassets.com%2Fimages%2Fp4%2Fn%2Fd%2FCAME-WD114_V1.jpg; time curl -X GET "http://87.117.25.190:5015/fashion_analysis/?url=$IMG_URL"` ``` > From office local network ```shell `IMG_URL=https%3A%2F%2Fis4.revolveassets.com%2Fimages%2Fp4%2Fn%2Fd%2FCAME-WD114_V1.jpg; time curl -X GET "http://192.168.0.125:5015/fashion_analysis/?url=$IMG_URL"` ``` ## Try it yourself at: [office local network access link](http://192.168.0.125:5015/docs#/default/analysis_fashion_analysis__get) [global network access link](http://87.117.25.190:5015/docs#/default/analysis_fashion_analysis__get) Parameter | Default | Description --------- | ------- | ----------- url | true | fashion image url. *The `json` output will contain result list where each object is a recognized fashion item* Each object will include: - 1 of 27 fashion classes - Tags description in 7 main categories - Mask of the recognized item - 5 main colors extracted from item mask covered area - Color embedding which can be used later on with fashion item search by color ## Visual analysis method > The above command returns JSON structured like this: ```json { "result": [ { "category": "pants", "description": { "tags": { "length": "maxi (length)", "nickname": "jeans", "opening type": "fly (opening)", "silhouette": "regular (fit)", "textile finishing, manufacturing techniques": "washed", "textile pattern": "plain (pattern)", "waistline": "low waist" }, "colors": [ "172839", ... ] }, "color_embedding": [ -0.45759817957878113, ... ] }, { "category": "top, t-shirt, sweatshirt", "description": { "tags": { "length": "above-the-hip (length)", "nickname": "classic (t-shirt)", "opening type": "no opening", "silhouette": "symmetrical", "textile finishing, manufacturing techniques": "printed", "textile pattern": "plain (pattern)", "waistline": "no waistline" }, "colors": [ "321d1a", ... ] }, "color_embedding": [ -0.5404209494590759, ... ] }, { "category": "shoe", "description": { "colors": [ "161615", ... ] }, "color_embedding": [ -0.5041476488113403, ... ] }, { "category": "headband, head covering, hair accessory", "description": { "colors": [ "35261b", ... ] }, "color_embedding": [ -0.5759932398796082, ... ] } ] } ``` That method allows us to check accuracy of given masks and bounding boxes recognition by the given image:  ### Provided attributes for the recognized `"category": "pants"`: Attribute | Predicted tag --------- | ------------- length | maxi nickname | jeans opening type | fly silhouette | regular (fit) textile finishing, manufacturing techniques | washed textile pattern | plain waistline | low waist ## Try it yourself at: [office local network access link](http://192.168.0.125:5015/docs#/default/analysis_visual_fashion_analysis__get) [global network access link](http://192.168.0.125:5015/docs#/default/analysis_visual_fashion_analysis__get) # Client API ## Setup You can easily setup our SDK with python 3.x language > Install pip package (under development) ```shell pip install salesvision ``` ## Authentication > Example of authentification process: > will be replaced with fastapi oauth2 ```python from salesvision import Salesvision # connect to Salesvision module to work with its API api = Salesvision(api_url='https://salesvision.com/api', api_key='Your_API_key', secret) ``` > Make sure to replace `v` with your API key. Salesvision API will be probably using oAuth2 for authentification process `Authorization: Your_API_key` <aside class="notice"> You must replace <code>Your_API_key</code> with your personal API key. </aside> ## Fashion analysis ```python from salesvision import Salesvision # connect to Salesvision module to work with its API api = Salesvision(api_url='https://salesvision.com/api', api_key='Your_API_key', secret) # image can be rather url or local stored file results = api.fashion_analysis(image) ``` > The above command returns JSON structured like this: ```json { "result": [ { "category": "pants", "description": { "tags": { "length": "maxi (length)", "nickname": "jeans", "opening type": "fly (opening)", "silhouette": "regular (fit)", "textile finishing, manufacturing techniques": "washed", "textile pattern": "plain (pattern)", "waistline": "low waist" }, "colors": [ "172839", ... ] }, "color_embedding": [ -0.45759817957878113, ... ] }, { "category": "top, t-shirt, sweatshirt", "description": { "tags": { "length": "above-the-hip (length)", "nickname": "classic (t-shirt)", "opening type": "no opening", "silhouette": "symmetrical", "textile finishing, manufacturing techniques": "printed", "textile pattern": "plain (pattern)", "waistline": "no waistline" }, "colors": [ "321d1a", ... ] }, "color_embedding": [ -0.5404209494590759, ... ] }, { "category": "shoe", "description": { "colors": [ "161615", ... ] }, "color_embedding": [ -0.5041476488113403, ... ] }, { "category": "headband, head covering, hair accessory", "description": { "colors": [ "35261b", ... ] }, "color_embedding": [ -0.5759932398796082, ... ] } ] } ``` Under the hood POST request is used ### Query Parameters Parameter | Default | Description --------- | ------- | ----------- file: | false | file in binary format. *The output of this method will contain result list where each object is a recognized fashion item* Each object will include: - 1 of 27 fashion classes - Tags description in 7 main categories - Mask of the recognized item - 5 main colors extracted from item mask covered area - Color embedding which can be used later on with fashion item search by color <aside class="success"> This method used with client API handles image file paths and urls </aside>