ошибка: (-215: подтверждение не выполнено)! _sr c .empty () в функции 'cvtColor' при использовании Jetson Nano и Raspberry Pi Camera Module V2 - PullRequest
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ошибка: (-215: подтверждение не выполнено)! _sr c .empty () в функции 'cvtColor' при использовании Jetson Nano и Raspberry Pi Camera Module V2

0 голосов
/ 21 марта 2020

Hy,

Я использую Jetson Nano и Raspberry Pi модуль камеры V2 и хочу запустить код из курса об обнаружении объектов YOLO +.

Когда я пытаюсь запустить код с помощью Spyder (python 3.6), эта ошибка возникает:

Файл «/home/dlinano/Documents/Python/UdemyTrainingYOLO/activity-1/detecting-object.py», строка 44 , в frame_HSV = cv2.cvtColor (frame_BGR, cv2.COLOR_BGR2HSV)

ошибка: OpenCV (4.1.1) / home / nvidia / host / build_opencv / nv_opencv / modules / imgproc / src /color.cpp:182: ошибка: (-215: подтверждение не выполнено)! _sr c .empty () в функции 'cvtColor'

Это код: 

# Detecting Object with chosen Colour Mask
#
# Algorithm:
# Reading RGB image --> Converting to HSV --> Implementing Mask -->
# --> Finding Contour Points --> Extracting Rectangle Coordinates -->
# --> Drawing Bounding Box --> Putting Label
#
# Result:
# Window with Detected Object, Bounding Box and Label in Real Time
# Importing needed library
import cv2

# Defining lower bounds and upper bounds of founded Mask
min_blue, min_green, min_red = 21, 222, 70
max_blue, max_green, max_red = 176, 255, 255

# Getting version of OpenCV that is currently used
# Converting string into the list by dot as separator
# and getting first number
v = cv2.__version__.split('.')[0]

# Defining object for reading video from camera
camera = cv2.VideoCapture(0)


# Defining loop for catching frames
while True:
    # Capture frame-by-frame from camera
    _, frame_BGR = camera.read()

    # Converting current frame to HSV
    frame_HSV = cv2.cvtColor(frame_BGR, cv2.COLOR_BGR2HSV)

    # Implementing Mask with founded colours from Track Bars to HSV Image
    mask = cv2.inRange(frame_HSV,
                       (min_blue, min_green, min_red),
                       (max_blue, max_green, max_red))

    # Showing current frame with implemented Mask
    # Giving name to the window with Mask
    # And specifying that window is resizable
    cv2.namedWindow('Binary frame with Mask', cv2.WINDOW_NORMAL)
    cv2.imshow('Binary frame with Mask', mask)

    # Finding Contours
    # Pay attention!
    # Different versions of OpenCV returns different number of parameters
    # when using function cv2.findContours()

    # In OpenCV version 3 function cv2.findContours() returns three parameters:
    # modified image, found Contours and hierarchy
    # All found Contours from current frame are stored in the list
    # Each individual Contour is a Numpy array of(x, y) coordinates
    # of the boundary points of the Object
    # We are interested only in Contours

    # Checking if OpenCV version 3 is used
    if v == '3':
        _, contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)

    # In OpenCV version 4 function cv2.findContours() returns two parameters:
    # found Contours and hierarchy
    # All found Contours from current frame are stored in the list
    # Each individual Contour is a Numpy array of(x, y) coordinates
    # of the boundary points of the Object
    # We are interested only in Contours

    # Checking if OpenCV version 4 is used
    else:
        contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)

    # Finding the biggest Contour by sorting from biggest to smallest
    contours = sorted(contours, key=cv2.contourArea, reverse=True)

    # Extracting Coordinates of the biggest Contour if any was found
    if contours:
        # Getting rectangle coordinates and spatial size from biggest Contour
        # Function cv2.boundingRect() is used to get an approximate rectangle
        # around the region of interest in the binary image after Contour was found
        (x_min, y_min, box_width, box_height) = cv2.boundingRect(contours[0])

        # Drawing Bounding Box on the current BGR frame
        cv2.rectangle(frame_BGR, (x_min - 15, y_min - 15),
                      (x_min + box_width + 15, y_min + box_height + 15),
                      (0, 0, 255), 3)

        # Preparing text for the Label
        label = 'Detected Object'

        # Putting text with Label on the current BGR frame
        cv2.putText(frame_BGR, label, (x_min - 5, y_min - 25),
                    cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0, 0, 255), 2)

    # Showing current BGR frame with Detected Object
    # Giving name to the window with Detected Object
    # And specifying that window is resizable
    cv2.namedWindow('Detected Object', cv2.WINDOW_NORMAL)
    cv2.imshow('Detected Object', frame_BGR)

    # Breaking the loop if 'q' is pressed
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# Destroying all opened windows
cv2.destroyAllWindows()

Я уже проверил следующие вещи:

1.) dlinano@jetson-nano: ~ $ ls -l / dev / video0 crw-rw ---- + 1 root video 81, 0 21 марта 10:56 / dev / video0 (это нормально, я думаю)

2.) Gst-launch-1.0 nvarguscamerasr c sensor_mode = 0! 'video / x-raw (память: NVMM), ширина = 3820, высота = 2464, частота кадров = 21/1, формат = NV12'! nvvidconv flip-method = 0! 'video / x-raw, ширина = 960, высота = 616'! nvvidconv! nvegltransform! nveglglessink -e (все работало нормально с хорошим качеством изображения)

3.) Выполнение того же кода на моем Windows 10 ноутбуке со Spyder (python 3.6) и ноутбуках на встроенной камере: результат: все работал нормально

Кто-нибудь может мне помочь?

BR Крис

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