Я получаю сообщение об ошибке numpy.ndarray object has no attribute 'read'
и numpy.ndarray object has no attribute 'seek'
. Я попытался найти ответ в Интернете, но мне не удалось.
Я пытаюсь обнаружить объекты в видео - в этом случае я хочу обнаружить зебр.
Я взял детектор изображений и пытаюсь применить его к видео. Я пытаюсь l oop на каждом кадре видео и в конечном итоге передать кадр функции draw_boxes
.
Вот сообщение об ошибке:
Traceback (most recent call last):
File "/Users/ysquared/Library/Python/3.7/lib/python/site-packages/PIL/Image.py", line 2770, in open
fp.seek(0)
AttributeError: 'numpy.ndarray' object has no attribute 'seek'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<string>", line 204, in <module>
File "<string>", line 118, in load_image_pixels
File "/Users/ysquared/Library/Python/3.7/lib/python/site-packages/keras_preprocessing/image/utils.py", line 110, in load_img
img = pil_image.open(path)
File "/Users/ysquared/Library/Python/3.7/lib/python/site-packages/PIL/Image.py", line 2772, in open
fp = io.BytesIO(fp.read())
AttributeError: 'numpy.ndarray' object has no attribute 'read'
А вот соответствующий код:
model = load_model('model.h5')
# define the expected input shape for the model
input_w, input_h = 416, 416
# define the anchors
anchors = [[116,90, 156,198, 373,326], [30,61, 62,45, 59,119], [10,13, 16,30, 33,23]]
# define the labels
labels = ["person", "bicycle", "car", "motorbike", "aeroplane", "bus", "train", "truck",
"boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench",
"bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe",
"backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard",
"sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard",
"tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana",
"apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake",
"chair", "sofa", "pottedplant", "bed", "diningtable", "toilet", "tvmonitor", "laptop", "mouse",
"remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator",
"book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"]
vs = cv2.VideoCapture('Zebras.mp4')
fourcc = cv2.VideoWriter_fourcc(*'XVID')
writer = cv2.VideoWriter('output.avi', fourcc, 20.0, (640, 480))
class_threshold = 0.6
boxes = list()
while True:
(grabbed, frame) = vs.read()
if grabbed==True:
image, image_w, image_h = load_image_pixels(frame, (input_w, input_h))
yhat = model.predict(image)
for i in range(len(yhat)):
# decode the output of the network
boxes += decode_netout(yhat[i][0], anchors[i], class_threshhold, input_h, input_w)
# correct the sizes of the bounding boxes for the shape of the image
correct_yolo_boxes(boxes, image_h, image_w, input_h, input_w)
# suppress non-maximal boxes
do_nms(boxes, 0.5)
# get the details of the detected objects
v_boxes, v_labels, v_scores = get_boxes(boxes, labels, class_threshold)
# draw what we found
frame = draw_boxes(frame, v_boxes, v_labels, v_scores)
writer.write(frame)
cv2.imshow('frame', frame)
if cv2.waitkey(1) & 0xFF == ord('q'):
break
else:
break
vs.release()
writer.release()
cv2.destroyAllWindows()