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