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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)) |