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from huggingface_hub import hf_hub_download
from typing import Dict, List, Any
from ultralytics import YOLO
import json
import urllib.request
import cv2
from io import BytesIO
import numpy as np
class EndpointHandler():
def __init__(self, path=""):
hf_hub_download(repo_id="Drazcat-AI/cecinas", filename="cecinas_v3-16/runs/detect/train/weights/best.pt")
self.model = YOLO(hf_hub_download(repo_id="Drazcat-AI/cecinas", filename="cecinas_v3-16/runs/detect/train/weights/best.pt", local_files_only=True))
def predict_objects(self, image_path, image_size_m):
results = self.model(image_path, imgsz=[1280, 960])
predictions = []
for box in results[0].boxes:
class_id = results[0].names[box.cls[0].item()]
cords = box.xywh[0].tolist()
conf = box.conf[0].item()
prediction = {
"x": round(cords[0]*image_size_m[0]),
"y": round(cords[1]*image_size_m[1]),
"width": round(cords[2]*image_size_m[0]),
"height": round(cords[3]*image_size_m[1]),
"confidence": conf,
"class": class_id
}
predictions.append(prediction)
predictions_array = {"predictions": predictions}
return predictions_array
def __call__(self, event):
if "inputs" not in event:
return {
"statusCode": 400,
"body": json.dumps("Error: Please provide an 'inputs' parameter."),
}
image_path = event["inputs"]
try:
with urllib.request.urlopen(image_path) as response:
image_content = np.asarray(bytearray(response.read()), dtype=np.uint8)
image = cv2.imdecode(image_content, cv2.IMREAD_COLOR)
predictions = self.predict_objects(image, (1,1))
return {
"statusCode": 200,
"body": json.dumps(predictions),
}
except Exception as e:
return {
"statusCode": 500,
"body": json.dumps(f"Error: {str(e)}"),
} |