问题

以下是您要翻译的内容：

"My project aims to detect object labels and coordinates and then convert them into a string which is converted into voice using gTTS but I keep getting an attribute error in the prediction labels. I am new to this framework, any help will be appreciated."

"Code:"

import cv2
from gtts import gTTS
import os
from ultralytics import YOLO

def convert_labels_to_text(labels):
    text = ", ".join(labels)
    return text

class YOLOWithLabels(YOLO):
    def __call__(self, frame):
        results = super().__call__(frame)
        labels = results.pred[0].get_field("labels").tolist()
        annotated_frame = results.render()
        return annotated_frame, labels

cap = cv2.VideoCapture(0)
model = YOLOWithLabels('yolov8n.pt')

while cap.isOpened():
    success, frame = cap.read()

    if success:
        annotated_frame, labels = model(frame)

        message = convert_labels_to_text(labels)

        tts_engine = gTTS(text=message)  # Initialize gTTS with the message

        tts_engine.save("output.mp3")
        os.system("output.mp3")

        cv2.putText(annotated_frame, message, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
        cv2.imshow("YOLOv8 Inference", annotated_frame)

        if cv2.waitKey(1) & 0xFF == ord("q"):
            break

    else:
        break

cap.release()
cv2.destroyAllWindows()

"Error"

File "C:\Users\alien\Desktop\YOLOv8 project files\gtts service\testservice.py", line 13, in __call__
    labels = results.pred[0].get_field("labels").tolist()
             ^^^^^^^^^^^^
AttributeError: 'list' object has no attribute 'pred'

print(results)
~~~
orig_shape: (480, 640)
path: 'image0.jpg'
probs: None
save_dir: None
speed: {'preprocess': 3.1604766845703125, 'inference': 307.905912399292, 'postprocess': 2.8924942016601562}]
0: 480x640 1 person, 272.4ms
Speed: 3.0ms preprocess, 272.4ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 640)
[ultralytics.yolo.engine.results.Results object with attributes:
    boxes: ultralytics.yolo.engine.results.Boxes object
    keypoints: None
    keys: ['boxes']
    masks: None
    names: {0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus', 6: 'train', 7: 'truck', 8: 'boat', 9: 'traffic light', 10: 'fire hydrant', 11: 'stop sign', 12: 'parking meter', 13: 'bench', 14: 'bird', 15: 'cat', 16: 'dog', 17: 'horse', 18: 'sheep', 19: 'cow', 20: 'elephant', 21: 'bear', 22: 'zebra', 23: 'giraffe', 24: 'backpack', 25: 'umbrella', 26: 'handbag', 27: 'tie', 28: 'suitcase', 29: 'frisbee', 30: 'skis', 31: 'snowboard', 32: 'sports ball', 33: 'kite', 34: 'baseball bat', 35: 'baseball glove', 36: 'skateboard', 37: 'surfboard', 38: 'tennis racket', 39: 'bottle', 40: 'wine glass', 41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon', 45: 'bowl', 46: 'banana', 47: 'apple', 48: 'sandwich', 49: 'orange', 50: 'broccoli', 51: 'carrot', 52: 'hot dog', 53: 'pizza', 54: 'donut', 55: 'cake', 56: 'chair', 57: 'couch', 58: 'potted plant', 59: 'bed', 60: 'dining table', 61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse', 65: 'remote', 66: 'keyboard', 67: 'cell phone', 68: 'microwave', 69: 'oven', 70: 'toaster', 71: 'sink', 72: 'refrigerator', 73: 'book', 74: 'clock', 75: 'vase', 76: 'scissors', 77: 'teddy bear', 78: 'hair drier', 79: 'toothbrush.'}
    orig_img: array([[[168, 167, 166],
            [165, 165, 165],
            [165, 166, 167],
            ...,
            [183, 186, 178],
            [183, 186, 178],
            [184, 187, 179]],

           [[168, 167, 165],
            [166, 165, 165],
            [166, 167, 166],
            ...,
            [184, 187, 179],
            [183, 186, 178],
            [184, 187, 179]],

           [[168, 167, 164],
            [167, 167, 164],
            [167, 167, 165],
            ...,
            [184, 187, 178],
            [184, 187, 179],
            [183, 186, 178]],

           ...,

           [[196, 192, 185],
            [196, 192, 185],
            [196, 192, 185],
            ...,
            [ 25,  29,  38],
            [ 22,  25,  35],
            [ 20,  24,  34]],

           [[199, 195, 187],
            [197, 193, 186],
            [197, 193, 186],
            ...,
            [ 23,  26,  35],
            [ 22,  25,  35],
            [ 22,  25,  35]],

           [[199, 195, 187],
            [199, 195, 187],
            [199, 195, 187],
            ...,
            [ 20,  24,  33],
            [ 19

<details>
<summary>英文:</summary>

My project aims to detect object labels and coordinates and then convert them into a string which is converted into voice using gTTS but I keep getting an attribute error in the prediction labels. I am new to this framework, any help will be appreciated.


Code:

    import cv2
    from gtts import gTTS
    import os
    from ultralytics import YOLO
    
    def convert_labels_to_text(labels):
        text = &quot;, &quot;.join(labels)
        return text
    
    class YOLOWithLabels(YOLO):
        def __call__(self, frame):
            results = super().__call__(frame)
            labels = results.pred[0].get_field(&quot;labels&quot;).tolist()
            annotated_frame = results.render()
            return annotated_frame, labels
    
    cap = cv2.VideoCapture(0)
    model = YOLOWithLabels(&#39;yolov8n.pt&#39;)
    
    while cap.isOpened():
        success, frame = cap.read()
    
        if success:
            annotated_frame, labels = model(frame)
    
            message = convert_labels_to_text(labels)
    
            tts_engine = gTTS(text=message)  # Initialize gTTS with the message
    
            tts_engine.save(&quot;output.mp3&quot;)
            os.system(&quot;output.mp3&quot;)
    
            cv2.putText(annotated_frame, message, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
            cv2.imshow(&quot;YOLOv8 Inference&quot;, annotated_frame)
    
            if cv2.waitKey(1) &amp; 0xFF == ord(&quot;q&quot;):
                break
    
        else:
            break
    
    cap.release()
    cv2.destroyAllWindows()

Error
 

    File &quot;C:\Users\alien\Desktop\YOLOv8 project files\gtts service\testservice.py&quot;, line 13, in __call__
        labels = results.pred[0].get_field(&quot;labels&quot;).tolist()
                 ^^^^^^^^^^^^
    AttributeError: &#39;list&#39; object has no attribute &#39;pred&#39;

print(results)
~~~
orig_shape: (480, 640)
path: &#39;image0.jpg&#39;
probs: None
save_dir: None
speed: {&#39;preprocess&#39;: 3.1604766845703125, &#39;inference&#39;: 307.905912399292, &#39;postprocess&#39;: 2.8924942016601562}]
0: 480x640 1 person, 272.4ms
Speed: 3.0ms preprocess, 272.4ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 640)
[ultralytics.yolo.engine.results.Results object with attributes:
    boxes: ultralytics.yolo.engine.results.Boxes object
    keypoints: None
    keys: [&#39;boxes&#39;]
    masks: None
    names: {0: &#39;person&#39;, 1: &#39;bicycle&#39;, 2: &#39;car&#39;, 3: &#39;motorcycle&#39;, 4: &#39;airplane&#39;, 5: &#39;bus&#39;, 6: &#39;train&#39;, 7: &#39;truck&#39;, 8: &#39;boat&#39;, 9: &#39;traffic light&#39;, 10: &#39;fire hydrant&#39;, 11: &#39;stop sign&#39;, 12: &#39;parking meter&#39;, 13: &#39;bench&#39;, 14: &#39;bird&#39;, 15: &#39;cat&#39;, 16: &#39;dog&#39;, 17: &#39;horse&#39;, 18: &#39;sheep&#39;, 19: &#39;cow&#39;, 20: &#39;elephant&#39;, 21: &#39;bear&#39;, 22: &#39;zebra&#39;, 23: &#39;giraffe&#39;, 24: &#39;backpack&#39;, 25: &#39;umbrella&#39;, 26: &#39;handbag&#39;, 27: &#39;tie&#39;, 28: &#39;suitcase&#39;, 29: &#39;frisbee&#39;, 30: &#39;skis&#39;, 31: &#39;snowboard&#39;, 32: &#39;sports ball&#39;, 33: &#39;kite&#39;, 34: &#39;baseball bat&#39;, 35: &#39;baseball glove&#39;, 36: &#39;skateboard&#39;, 37: &#39;surfboard&#39;, 38: &#39;tennis racket&#39;, 39: &#39;bottle&#39;, 40: &#39;wine glass&#39;, 41: &#39;cup&#39;, 42: &#39;fork&#39;, 43: &#39;knife&#39;, 44: &#39;spoon&#39;, 45: &#39;bowl&#39;, 46: &#39;banana&#39;, 47: &#39;apple&#39;, 48: &#39;sandwich&#39;, 49: &#39;orange&#39;, 50: &#39;broccoli&#39;, 51: &#39;carrot&#39;, 52: &#39;hot dog&#39;, 53: &#39;pizza&#39;, 54: &#39;donut&#39;, 55: &#39;cake&#39;, 56: &#39;chair&#39;, 57: &#39;couch&#39;, 58: &#39;potted plant&#39;, 59: &#39;bed&#39;, 60: &#39;dining table&#39;, 61: &#39;toilet&#39;, 62: &#39;tv&#39;, 63: &#39;laptop&#39;, 64: &#39;mouse&#39;, 65: &#39;remote&#39;, 66: &#39;keyboard&#39;, 67: &#39;cell phone&#39;, 68: &#39;microwave&#39;, 69: &#39;oven&#39;, 70: &#39;toaster&#39;, 71: &#39;sink&#39;, 72: &#39;refrigerator&#39;, 73: &#39;book&#39;, 74: &#39;clock&#39;, 75: &#39;vase&#39;, 76: &#39;scissors&#39;, 77: &#39;teddy bear&#39;, 78: &#39;hair drier&#39;, 79: &#39;toothbrush&#39;}
    orig_img: array([[[168, 167, 166],
            [165, 165, 165],
            [165, 166, 167],
            ...,
            [183, 186, 178],
            [183, 186, 178],
            [184, 187, 179]],

           [[168, 167, 165],
            [166, 165, 165],
            [166, 167, 166],
            ...,
            [184, 187, 179],
            [183, 186, 178],
            [184, 187, 179]],

           [[168, 167, 164],
            [167, 167, 164],
            [167, 167, 165],
            ...,
            [184, 187, 178],
            [184, 187, 179],
            [183, 186, 178]],

           ...,

           [[196, 192, 185],
            [196, 192, 185],
            [196, 192, 185],
            ...,
            [ 25,  29,  38],
            [ 22,  25,  35],
            [ 20,  24,  34]],

           [[199, 195, 187],
            [197, 193, 186],
            [197, 193, 186],
            ...,
            [ 23,  26,  35],
            [ 22,  25,  35],
            [ 22,  25,  35]],

           [[199, 195, 187],
            [199, 195, 187],
            [199, 195, 187],
            ...,
            [ 20,  24,  33],
            [ 19,  23,  33],
            [ 19,  23,  33]]], dtype=uint8)
~~~


</details>


# 答案1
**得分**: 0

愿上帝怜悯那位编写Ultralytics文档的人...以下是如何仅打印标签的方法：

```python
from ultralytics import YOLO

model = YOLO('yolov8n.pt')
results = model('http://images.cocodataset.org/val2017/000000397133.jpg')

print(model.names)

for result in results:
    boxes = result.boxes.cpu().numpy()
    for box in boxes:
        print(model.names[box.cls[0]])

model.names 包含所有可预测的类别。每个 box 都有一个 cls（缩写为 class）属性，它是一个整数值列表。您可以在 model.names 字典中查找该类别。

附注：每个 box 都有一个整数列表，表示模型可以为一个边界框返回多个类别。此示例仅获取 box.cls 列表中的第一个类别。

英文:

May God have mercy on whoever wrote Ultralytics's docs... Here's how you can print only the labels:

from ultralytics import YOLO
model = YOLO(&#39;yolov8n.pt&#39;)
results = model(&#39;http://images.cocodataset.org/val2017/000000397133.jpg&#39;)
print(model.names)
for result in results:
boxes = result.boxes.cpu().numpy()
for box in boxes:
print(model.names[box.cls[0]])

model.names contains all the classes that can be predicted. Each box has a cls (class for short) attribute which is a list of int values. You can search for that class in the model.names dictionary.

PS: Each box has a list of ints which indicates that the model can return multiple classes for one bounding box. This example only takes first of the classes in the box.cls list.

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