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import numpy as np |
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import time |
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import onnxruntime as ort |
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import json |
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import os |
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import logging |
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import ast |
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import cv2.dnn |
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# Environment variables |
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MODEL_PATH = os.environ.get("MODEL_PATH", "model.onnx") |
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INPUT_IMAGE_PATH = os.environ.get("IMAGE_PATH", "input.jpg") |
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# Onnxruntime providers |
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PROVIDERS = ast.literal_eval(os.environ.get("ONNXRUNTIME_PROVIDERS", '["CUDAExecutionProvider"]')) |
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CLASSES = { |
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0: "SpeedLimit", |
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1: "DangerAhead" |
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} |
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colors = np.random.uniform(0, 255, size=(len(CLASSES), 3)) |
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MIN_CONF_THRESHOLD = float(os.environ.get("MIN_CONF_THRESHOLD", 0.8)) |
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def preprocess(original_image): |
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# original_image: np.ndarray = cv2.imread(image_path) |
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[height, width, _] = original_image.shape |
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# Prepare a square image for inference |
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length = max((height, width)) |
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image = np.zeros((length, length, 3), np.uint8) |
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image[0:height, 0:width] = original_image |
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# Calculate scale factor |
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scale = length / 640 |
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# Preprocess the image and prepare blob for model |
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blob = cv2.dnn.blobFromImage(image, scalefactor=1 / 255, size=(640, 640), swapRB=True) |
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return blob, scale, original_image |
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def postprocess(response): |
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outputs = np.array([cv2.transpose(response[0])]) |
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rows = outputs.shape[1] |
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boxes = [] |
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scores = [] |
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class_ids = [] |
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# Iterate through output to collect bounding boxes, confidence scores, and class IDs |
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for i in range(rows): |
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classes_scores = outputs[0][i][4:] |
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(minScore, maxScore, minClassLoc, (x, maxClassIndex)) = cv2.minMaxLoc(classes_scores) |
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if maxScore >= 0.25: |
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box = [ |
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outputs[0][i][0] - (0.5 * outputs[0][i][2]), outputs[0][i][1] - (0.5 * outputs[0][i][3]), |
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outputs[0][i][2], outputs[0][i][3]] |
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boxes.append(box) |
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scores.append(maxScore) |
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class_ids.append(maxClassIndex) |
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detections = [] |
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result_boxes = cv2.dnn.NMSBoxes(boxes, scores, 0.25, 0.45, 0.5) |
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# Iterate through NMS results to draw bounding boxes and labels |
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for i in range(len(result_boxes)): |
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index = result_boxes[i] |
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box = boxes[index] |
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if scores[index] > MIN_CONF_THRESHOLD: |
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detection = { |
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'class_id': class_ids[index], |
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'class_name': CLASSES[class_ids[index]], |
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'confidence': f"{scores[index]:.2f}", |
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'box': [f"{c:.2f}" for c in box]} |
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detections.append(detection) |
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return detections |
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if __name__ == "__main__": |
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# Logger |
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logging.basicConfig( |
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format='%(asctime)s %(levelname)-8s %(message)s', |
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level=logging.INFO, |
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datefmt='%Y-%m-%d %H:%M:%S' |
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) |
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logger = logging.getLogger(__name__) |
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ort_sess = ort.InferenceSession(MODEL_PATH, providers=PROVIDERS) |
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nparr = np.fromfile(INPUT_IMAGE_PATH, np.uint8) |
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nparr = cv2.imdecode(nparr, cv2.IMREAD_COLOR) |
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preprocessed, scale, original_image = preprocess(nparr) |
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outputs = ort_sess.run(None, {'images': preprocessed}) |
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detections = postprocess(outputs[0]) |
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logger.info(f"Processed image {INPUT_IMAGE_PATH}") |
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logger.info(json.dumps(detections)) |
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logger.info(f"Now starting mass inference...") |
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start = time.time() |
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count = 0 |
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while True: |
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now = time.time() |
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if now - start > 1: |
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logger.info(f"Performed {count} inferences in {now-start:.2f}s") |
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start = time.time() |
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count = 0 |
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outputs = ort_sess.run(None, {'images': preprocessed}) |
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count+=1 |
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