OpenCV Deep Learning Vision using Caffe

opencv_deep_learning_vision_caffe.py

import numpy as np
import cv2
IMAGE_TO_PROCESS = 'image.png'
TXT_BASED_CLASSIFICATION = 'words.txt'

def get_txt_data(file):
  rows = open(file).read().strip().split('\n')
  return rows

img = cv2.imread(IMAGE_TO_PROCESS)
rows = get_txt_data(TXT_BASED_CLASSIFICATION)
classes = [r[r.find(' ') + 1:] for r in rows]
net = cv2.dnn.readNetFromCaffe('./FILE.prototxt','./MODEL.caffemodel')
blob = cv2.dnn.blobFromImage(img, 1, (224,224))
net.setInput(blob)
output = net.forward()

# Get last 10 images
idx = np.argsort(outp[0])[::-1][:10]

# Print Probability 
for (i,id) in enumerate(idx):
  print(f'{i+1} {classes[id]} ({id}): Probability {output[0][id]*100}')

# If you would like to show the image uncomment the lines below
# cv2.imshow('Image', img)
## Wait for key before exiting
# cv2.waitKey(0)
## Make sure to destroy the windows at the end
# cv2.destroyAllWindows()