opencv “DetectionResult”没有属性“detections”

uajslkp6  于 2023-08-06  发布在  其他
关注(0)|答案(1)|浏览(108)

我是新来的mediapipe图书馆。我正在执行here的代码。一切工作正常，没有尝试使用visualize函数;这意味着我正确地获得了以下检测结果，摄像机打开。
当我使用visualize函数时，我得到以下错误：

2023-07-18 15:11:18.865101: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
2023-07-18 15:11:19.003048: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
2023-07-18 15:11:22.230024: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
INFO: Created TensorFlow Lite XNNPACK delegate for CPU.
detection result: DetectionResult(detections=[Detection(bounding_box=BoundingBox(origin_x=3, origin_y=2, width=633, height=474), categories=[Category(index=None, score=0.7890625, display_name=None, category_name='person')], keypoints=[]), Detection(bounding_box=BoundingBox(origin_x=0, origin_y=200, width=323, height=271), categories=[Category(index=None, score=0.36328125, display_name=None, category_name='couch')], keypoints=[]), Detection(bounding_box=BoundingBox(origin_x=490, origin_y=92, width=146, height=266), categories=[Category(index=None, score=0.1328125, display_name=None, category_name='couch')], keypoints=[]), Detection(bounding_box=BoundingBox(origin_x=1, origin_y=59, width=43, height=85), categories=[Category(index=None, score=0.10546875, display_name=None, category_name='book')], keypoints=[]), Detection(bounding_box=BoundingBox(origin_x=4, origin_y=1, width=232, height=369), categories=[Category(index=None, score=0.08203125, display_name=None, category_name='person')], keypoints=[])])
Traceback (most recent call last):
  File "/home/simon/Public/face_detection/dog/main.py", line 76, in <module>
    annotated_image = visualize(mp_image, DetectionResult)
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/simon/Public/face_detection/dog/main.py", line 23, in visualize
    for detection in detection_result.detections:
                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: type object 'DetectionResult' has no attribute 'detections'

字符串
下面是我的代码：

import mediapipe as mp
import cv2
import numpy as np
import time 

MARGIN = 10  # pixels
ROW_SIZE = 10  # pixels
FONT_SIZE = 1
FONT_THICKNESS = 1
TEXT_COLOR = (255, 0, 0)  # red

def visualize(
    image,
    detection_result
) -> np.ndarray:
  """Draws bounding boxes on the input image and return it.
  Args:
    image: The input RGB image.
    detection_result: The list of all "Detection" entities to be visualize.
  Returns:
    Image with bounding boxes.
  """
  for detection in detection_result.detections:
    # Draw bounding_box
    bbox = detection.bounding_box
    start_point = bbox.origin_x, bbox.origin_y
    end_point = bbox.origin_x + bbox.width, bbox.origin_y + bbox.height
    cv2.rectangle(image, start_point, end_point, TEXT_COLOR, 3)

    # Draw label and score
    category = detection.categories[0]
    category_name = category.category_name
    probability = round(category.score, 2)
    result_text = category_name + ' (' + str(probability) + ')'
    text_location = (MARGIN + bbox.origin_x,
                     MARGIN + ROW_SIZE + bbox.origin_y)
    cv2.putText(image, result_text, text_location, cv2.FONT_HERSHEY_PLAIN,
                FONT_SIZE, TEXT_COLOR, FONT_THICKNESS)

  return image

model_path = 'efficientdet_lite0.tflite'

BaseOptions = mp.tasks.BaseOptions
DetectionResult = mp.tasks.components.containers.DetectionResult
ObjectDetector = mp.tasks.vision.ObjectDetector
ObjectDetectorOptions = mp.tasks.vision.ObjectDetectorOptions
VisionRunningMode = mp.tasks.vision.RunningMode

def print_result(result: DetectionResult, output_image: mp.Image, timestamp_ms: int):
     print('detection result: {}'.format(result))

options = ObjectDetectorOptions(
    base_options=BaseOptions(model_asset_path=model_path),
    running_mode=VisionRunningMode.LIVE_STREAM,
    max_results=5,
    result_callback= print_result)

with ObjectDetector.create_from_options(options) as detector:
    cap = cv2.VideoCapture(0)

    while True:
        ret, frame = cap.read()

        if not ret:
            break

        mp_image = mp.Image(image_format=mp.ImageFormat.SRGB,
                                                data=frame)

        frame_timestamp_ms = int(time.time() * 1000)

        detector.detect_async(mp_image, frame_timestamp_ms)

        annotated_image = visualize(mp_image, DetectionResult)

        cv2.imshow('Object Detection', annotated_image)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

    cap.release()
    cv2.destroyAllWindows()

型
我也使用Python 3.11.3和opencv-python 4.8.0.74。
我检查了类DetectionResult，看看是否有一个名为detections的属性，确实有一个。我不知道为什么我得到一个错误。
下面是我没有使用visualize函数的代码，下面是我的一般输出。

import mediapipe as mp
import cv2
import numpy as np
import time 

MARGIN = 10  # pixels
ROW_SIZE = 10  # pixels
FONT_SIZE = 1
FONT_THICKNESS = 1
TEXT_COLOR = (255, 0, 0)  # red

def visualize(
    image,
    detection_result
) -> np.ndarray:
  """Draws bounding boxes on the input image and return it.
  Args:
    image: The input RGB image.
    detection_result: The list of all "Detection" entities to be visualize.
  Returns:
    Image with bounding boxes.
  """
  for detection in detection_result.detections:
    # Draw bounding_box
    bbox = detection.bounding_box
    start_point = bbox.origin_x, bbox.origin_y
    end_point = bbox.origin_x + bbox.width, bbox.origin_y + bbox.height
    cv2.rectangle(image, start_point, end_point, TEXT_COLOR, 3)

    # Draw label and score
    category = detection.categories[0]
    category_name = category.category_name
    probability = round(category.score, 2)
    result_text = category_name + ' (' + str(probability) + ')'
    text_location = (MARGIN + bbox.origin_x,
                     MARGIN + ROW_SIZE + bbox.origin_y)
    cv2.putText(image, result_text, text_location, cv2.FONT_HERSHEY_PLAIN,
                FONT_SIZE, TEXT_COLOR, FONT_THICKNESS)

  return image

model_path = 'efficientdet_lite0.tflite'

BaseOptions = mp.tasks.BaseOptions
DetectionResult = mp.tasks.components.containers.DetectionResult
ObjectDetector = mp.tasks.vision.ObjectDetector
ObjectDetectorOptions = mp.tasks.vision.ObjectDetectorOptions
VisionRunningMode = mp.tasks.vision.RunningMode

def print_result(result: DetectionResult, output_image: mp.Image, timestamp_ms: int):
     print('detection result: {}'.format(result))

options = ObjectDetectorOptions(
    base_options=BaseOptions(model_asset_path=model_path),
    running_mode=VisionRunningMode.LIVE_STREAM,
    max_results=5,
    result_callback= print_result)

with ObjectDetector.create_from_options(options) as detector:
    cap = cv2.VideoCapture(0)

    while True:
        ret, frame = cap.read()

        if not ret:
            break

        mp_image = mp.Image(image_format=mp.ImageFormat.SRGB,
                                                data=frame)

        frame_timestamp_ms = int(time.time() * 1000)

        detector.detect_async(mp_image, frame_timestamp_ms)

        # Removed
        # annotated_image = visualize(mp_image, DetectionResult) 
        # cv2.imshow('Object Detection', annotated_image)

        # Added
        cv2.imshow('Object Detection', frame)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

    cap.release()
    cv2.destroyAllWindows()

型
输出量：

(face_detection) [simon@simon dog]$ python main.py
2023-07-18 15:19:26.338527: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
2023-07-18 15:19:26.474704: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
2023-07-18 15:19:29.716547: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
INFO: Created TensorFlow Lite XNNPACK delegate for CPU.
detection result: DetectionResult(detections=[Detection(bounding_box=BoundingBox(origin_x=9, origin_y=2, width=621, height=474), categories=[Category(index=None, score=0.8671875, display_name=None, category_name='person')], keypoints=[]), Detection(bounding_box=BoundingBox(origin_x=0, origin_y=225, width=251, height=247), categories=[Category(index=None, score=0.26171875, display_name=None, category_name='person')], keypoints=[]), Detection(bounding_box=BoundingBox(origin_x=485, origin_y=135, width=149, height=256), categories=[Category(index=None, score=0.23046875, display_name=None, category_name='couch')], keypoints=[]), Detection(bounding_box=BoundingBox(origin_x=0, origin_y=72, width=41, height=76), categories=[Category(index=None, score=0.14453125, display_name=None, category_name='book')], keypoints=[]), Detection(bounding_box=BoundingBox(origin_x=423, origin_y=287, width=35, height=51), categories=[Category(index=None, score=0.09765625, display_name=None, category_name='remote')], keypoints=[])])
detection result: DetectionResult(detections=[Detection(bounding_box=BoundingBox(origin_x=9, origin_y=2, width=621, height=474), categories=[Category(index=None, score=0.81640625, display_name=None, category_name='person')], keypoints=[]), Detection(bounding_box=BoundingBox(origin_x=491, origin_y=140, width=144, height=256), categories=[Category(index=None, score=0.3203125, display_name=None, category_name='couch')], keypoints=[]), Detection(bounding_box=BoundingBox(origin_x=0, origin_y=183, width=251, height=293), categories=[Category(index=None, score=0.24609375, display_name=None, category_name='person')], keypoints=[]), Detection(bounding_box=BoundingBox(origin_x=1, origin_y=284, width=92, height=191), categories=[Category(index=None, score=0.12109375, display_name=None, category_name='couch')], keypoints=[]), Detection(bounding_box=BoundingBox(origin_x=0, origin_y=74, width=41, height=74), categories=[Category(index=None, score=0.11328125, display_name=None, category_name='book')], keypoints=[])])
detection result: DetectionResult(detections=[Detection(bounding_box=BoundingBox(origin_x=3, origin_y=0, width=633, height=480), categories=[Category(index=None, score=0.85546875, display_name=None, category_name='person')], keypoints=[]), Detection(bounding_box=BoundingBox(origin_x=21, origin_y=218, width=355, height=256), categories=[Category(index=None, score=0.19921875, display_name=None, category_name='person')], keypoints=[]), Detection(bounding_box=BoundingBox(origin_x=512, origin_y=139, width=125, height=199), categories=[Category(index=None, score=0.1328125, display_name=None, category_name='couch')], keypoints=[]), Detection(bounding_box=BoundingBox(origin_x=0, origin_y=2, width=219, height=430), categories=[Category(index=None, score=0.1328125, display_name=None, category_name='person')], keypoints=[]), Detection(bounding_box=BoundingBox(origin_x=423, origin_y=289, width=37, height=51), categories=[Category(index=None, score=0.12109375, display_name=None, category_name='cell phone')], keypoints=[])])
detection result: DetectionResult(detections=[Detection(bounding_box=BoundingBox(origin_x=3, origin_y=0, width=633, height=480), categories=[Category(index=None, score=0.80078125, display_name=None, category_name='person')], keypoints=[]), Detection(bounding_box=BoundingBox(origin_x=490, origin_y=138, width=146, height=251), categories=[Category(index=None, score=0.24609375, display_name=None, category_name='couch')], keypoints=[]), Detection(bounding_box=BoundingBox(origin_x=0, origin_y=203, width=261, height=261), categories=[Category(index=None, score=0.19921875, display_name=None, category_name='dog')], keypoints=[]), Detection(bounding_box=BoundingBox(origin_x=0, origin_y=72, width=41, height=76), categories=[Category(index=None, score=0.15625, display_name=None, category_name='book')], keypoints=[]), Detection(bounding_box=BoundingBox(origin_x=423, origin_y=286, width=37, height=52), categories=[Category(index=None, score=0.07421875, display_name=None, category_name='remote')], keypoints=[])])
detection result: DetectionResult(detections=[Detection(bounding_box=BoundingBox(origin_x=3, origin_y=2, width=633, height=474), categories=[Category(index=None, score=0.84375, display_name=None, category_name='person')], keypoints=[]), Detection(bounding_box=BoundingBox(origin_x=0, origin_y=198, width=251, height=252), categories=[Category(index=None, score=0.2109375, display_name=None, category_name='person')], keypoints=[]), Detection(bounding_box=BoundingBox(origin_x=484, origin_y=130, width=152, height=246), categories=[Category(index=None, score=0.16796875, display_name=None, category_name='chair')], keypoints=[]), Detection(bounding_box=BoundingBox(origin_x=0, origin_y=71, width=42, height=77), categories=[Category(index=None, score=0.15625, display_name=None, category_name='book')], keypoints=[]), Detection(bounding_box=BoundingBox(origin_x=58, origin_y=311, width=564, height=155), categories=[Category(index=None, score=0.10546875, display_name=None, category_name='person')], keypoints=[])])
detection result: DetectionResult(detections=[Detection(bounding_box=BoundingBox(origin_x=3, origin_y=2, width=633, height=474), categories=[Category(index=None, score=0.76953125, display_name=None, category_name='person')], keypoints=[]), Detection(bounding_box=BoundingBox(origin_x=481, origin_y=138, width=158, height=299), categories=[Category(index=None, score=0.19921875, display_name=None, category_name='couch')], keypoints=[]), Detection(bounding_box=BoundingBox(origin_x=14, origin_y=225, width=256, height=242), categories=[Category(index=None, score=0.18359375, display_name=None, category_name='person')], keypoints=[]), Detection(bounding_box=BoundingBox(origin_x=0, origin_y=73, width=41, height=74), categories=[Category(index=None, score=0.15625, display_name=None, category_name='book')], keypoints=[]), Detection(bounding_box=BoundingBox(origin_x=2, origin_y=284, width=94, height=191), categories=[Category(index=None, score=0.08203125, display_name=None, category_name='person')], keypoints=[])])
detection result: DetectionResult(detections=[Detection(bounding_box=BoundingBox(origin_x=3, origin_y=2, width=633, height=474), categories=[Category(index=None, score=0.81640625, display_name=None, category_name='person')], keypoints=[]), Detection(bounding_box=BoundingBox(origin_x=0, origin_y=225, width=251, height=242), categories=[Category(index=None, score=0.26171875, display_name=None, category_name='person')], keypoints=[]), Detection(bounding_box=BoundingBox(origin_x=0, origin_y=69, width=42, height=82), categories=[Category(index=None, score=0.14453125, display_name=None, category_name='book')], keypoints=[]), Detection(bounding_box=BoundingBox(origin_x=477, origin_y=108, width=158, height=335), categories=[Category(index=None, score=0.1328125, display_name=None, category_name='couch')], keypoints=[]), Detection(bounding_box=BoundingBox(origin_x=26, origin_y=257, width=223, height=72), categories=[Category(index=None, score=0.09765625, display_name=None, category_name='person')], keypoints=[])])
detection result: DetectionResult(detections=[Detection(bounding_box=BoundingBox(origin_x=3, origin_y=2, width=633, height=474), categories=[Category(index=None, score=0.84375, display_name=None, category_name='person')], keypoints=[]), Detection(bounding_box=BoundingBox(origin_x=14, origin_y=223, width=355, height=247), categories=[Category(index=None, score=0.19921875, display_name=None, category_name='person')], keypoints=[]), Detection(bounding_box=BoundingBox(origin_x=472, origin_y=120, width=168, height=323), categories=[Category(index=None, score=0.18359375, display_name=None, category_name='couch')], keypoints=[]), Detection(bounding_box=BoundingBox(origin_x=0, origin_y=70, width=42, height=77), categories=[Category(index=None, score=0.15625, display_name=None, category_name='book')], keypoints=[]), Detection(bounding_box=BoundingBox(origin_x=2, origin_y=310, width=96, height=164), categories=[Category(index=None, score=0.08203125, display_name=None, category_name='couch')], keypoints=[])])
detection result: DetectionResult(detections=[Detection(bounding_box=BoundingBox(origin_x=3, origin_y=0, width=633, height=480), categories=[Category(index=None, score=0.8671875, display_name=None, category_name='person')], keypoints=[]), Detection(bounding_box=BoundingBox(origin_x=0, origin_y=177, width=251, height=293), categories=[Category(index=None, score=0.2109375, display_name=None, category_name='person')], keypoints=[]), Detection(bounding_box=BoundingBox(origin_x=0, origin_y=69, width=42, height=85), categories=[Category(index=None, score=0.14453125, display_name=None, category_name='book')], keypoints=[]), Detection(bounding_box=BoundingBox(origin_x=523, origin_y=136, width=116, height=207), categories=[Category(index=None, score=0.11328125, display_name=None, category_name='couch')], keypoints=[]), Detection(bounding_box=BoundingBox(origin_x=30, origin_y=313, width=552, height=164), categories=[Category(index=None, score=0.08984375, display_name=None, category_name='couch')], keypoints=[])])
detection result: DetectionResult(detections=[Detection(bounding_box=BoundingBox(origin_x=3, origin_y=2, width=633, height=474), categories=[Category(index=None, score=0.76953125, display_name=None, category_name='person')], keypoints=[]), Detection(bounding_box=BoundingBox(origin_x=473, origin_y=136, width=164, height=305), categories=[Category(index=None, score=0.24609375, display_name=None, category_name='couch')], keypoints=[]), Detection(bounding_box=BoundingBox(origin_x=0, origin_y=208, width=260, height=256), categories=[Category(index=None, score=0.16796875, display_name=None, category_name='person')], keypoints=[]), Detection(bounding_box=BoundingBox(origin_x=0, origin_y=68, width=42, height=80), categories=[Category(index=None, score=0.1328125, display_name=None, category_name='book')], keypoints=[]), Detection(bounding_box=BoundingBox(origin_x=0, origin_y=387, width=77, height=92), categories=[Category(index=None, score=0.06640625, display_name=None, category_name='bed')], keypoints=[])])
(face_detection) [simon@simon dog]$

型

opencv

1条答案

按热度按时间
o2gm4chl

o2gm4chl1#

我检查了所有的mediapipe解决方案，但我找不到任何mp.tasks.components.containers.DetectionResult
但是，我只能找到mp.tasks.components.containers.LandmarksDetectionResult和mp.tasks.components.containers.detection，所以你确定你没有写正确的东西吗？或者你可以给你给予这个链接mp.tasks.components.containers.DetectionResult

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