This notebook covers how to use Binary Neural Networks on Pynq. It shows an example of handwritten digit recognition using a binarized neural network composed of 4 fully connected layers with 1024 neurons each, trained on the MNIST dataset of handwritten digits. In order to reproduce this notebook, you will need an external USB Camera connected to the PYNQ Board.
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import bnn
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print(bnn.available_params(bnn.NETWORK_LFCW1A1))
Two sets of weights are available for the LFCW1A1 network, the MNIST and one for character recognition (NIST).
Creating a classifier will automatically download the correct bitstream onto the device and load the weights trained on the specified dataset. This example works with the LFCW1A1 for inferring MNIST handwritten digits. Passing a runtime attribute will allow to choose between hardware accelerated or pure software inference.
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hw_classifier = bnn.LfcClassifier(bnn.NETWORK_LFCW1A1,"mnist",bnn.RUNTIME_HW)
sw_classifier = bnn.LfcClassifier(bnn.NETWORK_LFCW1A1,"mnist",bnn.RUNTIME_SW)
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print(hw_classifier.classes)
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import cv2
from PIL import Image as PIL_Image
from PIL import ImageEnhance
from PIL import ImageOps
# says we capture an image from a webcam
cap = cv2.VideoCapture(0)
_ , cv2_im = cap.read()
cv2_im = cv2.cvtColor(cv2_im,cv2.COLOR_BGR2RGB)
img = PIL_Image.fromarray(cv2_im).convert("L")
#original captured image
#orig_img_path = '/home/xilinx/jupyter_notebooks/bnn/pictures/webcam_image_mnist.jpg'
#img = PIL_Image.open(orig_img_path).convert("L")
#Image enhancement
contr = ImageEnhance.Contrast(img)
img = contr.enhance(3) # The enhancement values (contrast and brightness)
bright = ImageEnhance.Brightness(img) # depends on backgroud, external lights etc
img = bright.enhance(4.0)
#img = img.rotate(180) # Rotate the image (depending on camera orientation)
#Adding a border for future cropping
img = ImageOps.expand(img,border=80,fill='white')
img
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from PIL import Image as PIL_Image
import numpy as np
import math
from scipy import misc
#Find bounding box
inverted = ImageOps.invert(img)
box = inverted.getbbox()
img_new = img.crop(box)
width, height = img_new.size
ratio = min((28./height), (28./width))
background = PIL_Image.new('RGB', (28,28), (255,255,255))
if(height == width):
img_new = img_new.resize((28,28))
elif(height>width):
img_new = img_new.resize((int(width*ratio),28))
background.paste(img_new, (int((28-img_new.size[0])/2),int((28-img_new.size[1])/2)))
else:
img_new = img_new.resize((28, int(height*ratio)))
background.paste(img_new, (int((28-img_new.size[0])/2),int((28-img_new.size[1])/2)))
background
img_data=np.asarray(background)
img_data = img_data[:,:,0]
misc.imsave('/home/xilinx/img_webcam_mnist.png', img_data)
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from array import *
from PIL import Image as PIL_Image
from PIL import ImageOps
img_load = PIL_Image.open('/home/xilinx/img_webcam_mnist.png').convert("L")
# Convert to BNN input format
# The image is resized to comply with the MNIST standard. The image is resized at 28x28 pixels and the colors inverted.
#Resize the image and invert it (white on black)
smallimg = ImageOps.invert(img_load)
smallimg = smallimg.rotate(0)
data_image = array('B')
pixel = smallimg.load()
for x in range(0,28):
for y in range(0,28):
if(pixel[y,x] == 255):
data_image.append(255)
else:
data_image.append(1)
# Setting up the header of the MNIST format file - Required as the hardware is designed for MNIST dataset
hexval = "{0:#0{1}x}".format(1,6)
header = array('B')
header.extend([0,0,8,1,0,0])
header.append(int('0x'+hexval[2:][:2],16))
header.append(int('0x'+hexval[2:][2:],16))
header.extend([0,0,0,28,0,0,0,28])
header[3] = 3 # Changing MSB for image data (0x00000803)
data_image = header + data_image
output_file = open('/home/xilinx/img_webcam_mnist_processed', 'wb')
data_image.tofile(output_file)
output_file.close()
smallimg
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class_out = hw_classifier.classify_mnist("/home/xilinx/img_webcam_mnist_processed")
print("Class number: {0}".format(class_out))
print("Class name: {0}".format(hw_classifier.class_name(class_out)))
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class_out=sw_classifier.classify_mnist("/home/xilinx/img_webcam_mnist_processed")
print("Class number: {0}".format(class_out))
print("Class name: {0}".format(hw_classifier.class_name(class_out)))
In [10]:
from pynq import Xlnk
xlnk = Xlnk()
xlnk.xlnk_reset()