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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): # 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) # Decode bytes to string
###########################################################################
return jpg_as_text
###########################################################################
# data = {
# "inputs": {
# "isurl": True,
# "path": "http://192.168.10.20/cam-hi.jpg",
# "key": "iJuYzEzNEFSaQq4e0hfE",
# }
# }
# # test run
# Model = EndpointHandler()
# print(Model(data))
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