gistfile1.py

from keras.applications.resnet50 import ResNet50
from keras.preprocessing import image
from keras.applications.resnet50 import preprocess_input, decode_predictions
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image

### Subroutine to calculate likelihood for hummingbird in an image
def calculateLikelihood(x):
 x = np.expand_dims(x, axis=0)
 x = preprocess_input(x)
 preds = model.predict(x)
 # The class index for hummingbird is 94 in ImageNet.
 return preds[0][94]

### Load model and define parameters
model = ResNet50(weights='imagenet')dim = 861
height = 1080
width = 1920
expecteddim = 224
data = []
N = 48
### Loop through the images 
for n in range(1, N):
  # Read the image 
  name = 'thumb' + '{0:04d}'.format(n) + '.jpg'
  img_path = name
  img = image.load_img(img_path)
  # Crop the image, resize it and make prediction
  startx = 0
  starty = 0 
  endx = startx + dim  
  endy = starty + dim
  cropped_img = img.crop((startx, starty, endx, endy))
  resize_img = cropped_img.resize((expecteddim, expecteddim), Image.ANTIALIAS)
  x = image.img_to_array(resize_img)
  p1 = calculateLikelihood(x)
  # Repeat above but close to another flower
  startx = width - dim
  starty = 0 
  endx = startx + dim  
  endy = starty + dim
  cropped_img = img.crop((startx, starty, endx, endy))
  resize_img = cropped_img.resize((expecteddim, expecteddim), Image.ANTIALIAS)
  x = image.img_to_array(resize_img)
  p2 = calculateLikelihood(x)
  # Take the maximum likelihood as the return likelihood
  print(n, max(p1, p2))  
  data.append(max(p1, p2))
  
### Compare with ground truth labels
trueXList = []
trueYList = []
falseXList = []
falseYList = []
f = open('gt.txt', 'r')
tmp = f.readline().rstrip()
for i in range(1, N):
  info = tmp.split()
  index = int(info[0])
  if info[1] == 't':
    trueXList.append(index)
    trueYList.append(data[index - 1])
  else:
    falseXList.append(index)
    falseYList.append(data[index - 1])
  tmp = f.readline().rstrip()
f.close()

### Generate plots
plt.scatter(falseXList, falseYList, color='red')
plt.scatter(trueXList, trueYList, color='green')
plt.hlines(y=0.05, xmin=0, xmax=N, color='b')
plt.show()