I used mask rcnn for object detect I git my keras model l have a function to predict the image my question is : how to c

[answerhappygod]

I used mask rcnn for object detect
I git my keras model
l have a function to predict the image
my question is : how to convert plot_actual_vs_predicted function to opencv real time camera I wand to predict real time object not specific images just convert the function to real time using cv2.videoCapture(0)
thank you
# plot a number of photos with ground truth and predictions
def plot_actual_vs_predicted(dataset, model, cfg, n_images=5):
# load image and mask
for i in range(n_images):
# load the image and mask
image = dataset.load_image(i)
mask, _ = dataset.load_mask(i)
# convert pixel values (e.g. center)
scaled_image = mold_image(image, cfg)
# convert image into one sample
sample = expand_dims(scaled_image, 0)
# make prediction
yhat = model.detect(sample, verbose=0)[0]
# define subplot
pyplot.subplot(n_images, 2, i*2+1)
# plot raw pixel data
pyplot.imshow(image)
pyplot.title('Actual')
# plot masks
for j in range(mask.shape[2]):
pyplot.imshow(mask[:, :, j], cmap='gray', alpha=0.3)
# get the context for drawing boxes
pyplot.subplot(n_images, 2, i*2+2)
# plot raw pixel data
pyplot.imshow(image)
pyplot.title('Predicted')
ax = pyplot.gca()
# plot each box
for box in yhat['rois']:
# get coordinates
y1, x1, y2, x2 = box
# calculate width and height of the box
width, height = x2 - x1, y2 - y1
# create the shape
rect = Rectangle((x1, y1), width, height, fill=False, color='red')
# draw the box
ax.add_patch(rect)
# show the figure
pyplot.show()

# load the train dataset
train_set = KangarooDataset()
train_set.load_dataset('kangaroo', is_train=True)
train_set.prepare()
print('Train: %d' % len(train_set.image_ids))
# load the test dataset
test_set = KangarooDataset()
test_set.load_dataset('kangaroo', is_train=False)
test_set.prepare()
print('Test: %d' % len(test_set.image_ids))
# create config
cfg = PredictionConfig()
# define the model
model = MaskRCNN(mode='inference', model_dir='./', config=cfg)
# load model weights
model_path = 'mask_rcnn_kangaroo_cfg_0005.h5'
model.load_weights(model_path, by_name=True)
# plot predictions for train dataset
plot_actual_vs_predicted(train_set, model, cfg)
# plot predictions for test dataset
plot_actual_vs_predicted(test_set, model, cfg)