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https://gitlab.fdmci.hva.nl/technische-informatica-sm3/ti-projectten/rooziinuubii79.git
synced 2025-08-03 20:04:58 +00:00
attempt to fix broken code
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@@ -9,15 +9,13 @@ app = Flask(__name__)
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# Load a model
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model = YOLO("yolo11n.pt") # pretrained YOLO11n model
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#from: https://medium.com/@Mert.A/how-to-segment-objects-with-yolov11-68593eb49fa8
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yolo_classes = list(model.names.values())
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classes_ids = [yolo_classes.index(clas) for clas in yolo_classes]
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kobuki_message = ""
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latest_image = None
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yolo_results = []
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# https://medium.com/@Mert.A/how-to-segment-objects-with-yolov11-68593eb49fa8
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yolo_classes = list(model.names.values())
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def on_message(client, userdata, message):
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global kobuki_message, latest_image, yolo_results
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if message.topic == "kobuki/data":
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@@ -30,15 +28,17 @@ def on_message(client, userdata, message):
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yolo_results = []
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for result in results:
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for box in result.boxes:
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class_id = int(box.cls.item()) # Convert to integer
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class_name = yolo_classes[class_id]
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yolo_results.append({
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"class": yolo_classes[box.cls.item()],
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"class": class_name,
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"confidence": box.conf.item(),
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"bbox": box.xyxy.tolist()
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})
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# Draw bounding box on the image
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x1, y1, x2, y2 = map(int, box.xyxy[0])
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x1, y1, x2, y2 = map(int, box.xyxy)
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cv2.rectangle(latest_image, (x1, y1), (x2, y2), (0, 255, 0), 2)
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cv2.putText(latest_image, f"{yolo_classes[box.cls.item()]} {box.conf.item():.2f}", (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
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cv2.putText(latest_image, f"{class_name} {box.conf.item():.2f}", (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
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# Create an MQTT client instance
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mqtt_client = mqtt.Client()
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