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In the context of deep learning, a blob is also the entire image pre-processed and prepared for classification/training. Such pre-processing usually entails mean subtraction and scaling.
cv2.dnn.blobFromImage : a deep learning method from OpenCV that creates 4-dimensional blob from image. Optionally resizes and crops the image from the center, subtract mean values, scales values by scale factor, swap Blue and Red channels.
image: input image (with 1-, 3- or 4-channels).
swapRB: flag which indicates that swap first and last channels in a 3-channel image is necessary.
crop: flag which indicates whether the image will be cropped after resize or not.
net.setInput(blob) : Sets the input value
blob for the network
cv2.imread : Reads an image.
plt.imshow(image) : Displays data as an image.
cv2.imread to read the image we want to detect the objects in, into
img = << your code comes here >>('/cxldata/dlcourse/mask_rcnn_model_data/dining_table.jpg')
Visualize the image
img we have read using
fixColor, the function we defined previously, and
plt.imshow. Make sure to write this in a separate code-cell to be able to see the image
cv2.dnn.blobFromImage to get the blob of the input image.
blob = << your code comes here >>(img, swapRB=True, crop=False)
blob as input to the network
net.<< your code comes here >>(blob)
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