from typing import Dict, List, Any import supervision as sv import urllib.request import numpy as np import cv2 import base64 from inference_sdk import InferenceHTTPClient class EndpointHandler(): def __init__(self, key): #pass api key to model pass def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]: inputs = data.get("inputs") isurl = inputs.get("isurl") path = inputs.get("path") key = inputs.get("key") ########################### Load Image ################################# CLIENT = InferenceHTTPClient( api_url="https://detect.roboflow.com", api_key=key ) if(isurl): # for url set isurl = 1 req = urllib.request.urlopen(path) arr = np.asarray(bytearray(req.read()), dtype=np.uint8) img = cv2.imdecode(arr, -1) # 'Load it as it is' else: # for image file img = cv2.imread(path) ########################################################################### ########################### Model Detection ################################# # change model_id to use a different model # can try: # clothing-segmentation-dataset/1 # t-shirts-detector/1 # mainmodel/2 result = CLIENT.infer(path, model_id="mainmodel/2") detections = sv.Detections.from_inference(result) # print(detections) ########################################################################### ########################### Data proccessing ################################# # only pass the first detection # change 1 -> to len(detections.xyxy) to pass all photos if(detections.confidence.size == 0): return "Not Found" else: x1, y1, x2, y2 = int(detections.xyxy[0][0]), int(detections.xyxy[0][1]), int(detections.xyxy[0][2]), int(detections.xyxy[0][3]) clothes = img[y1: y2, x1: x2] retval , buffer = cv2.imencode('.jpg', clothes) # create base 64 object jpg_as_text = base64.b64encode(buffer) ########################################################################### return jpg_as_text ########################################################################### # test run # Model = Image_detect("api key") # print(Model("test_images/test5.jpg", 0))