Reading and Preprocessing the image

Note:

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 net.
cv2.imread : Reads an image.
plt.imshow(image) : Displays data as an image.

INSTRUCTIONS

Use cv2.imread to read the image we want to detect the objects in, into img at /cxldata/dlcourse/mask_rcnn_model_data/dining_table.jpg.
```
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 img.
```
plt.imshow(fixColor(img))
```

Use cv2.dnn.blobFromImage to get the blob of the input image.

blob = << your code comes here >>(img, swapRB=True, crop=False)

Set blob as input to the network net using setInput.
```
net.<< your code comes here >>(blob)
```

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