update

Author: bob7783

cnn_class2/class_activation_maps.py

@@ -0,0 +1,97 @@
+# https://deeplearningcourses.com/c/advanced-computer-vision
+# https://www.udemy.com/advanced-computer-vision
+
+from __future__ import print_function, division
+from builtins import range, input
+# Note: you may need to update your version of future
+# sudo pip install -U future
+
+from keras.layers import Input, Lambda, Dense, Flatten
+from keras.models import Model
+from keras.applications.resnet50 import ResNet50, preprocess_input, decode_predictions
+# from keras.applications.inception_v3 import InceptionV3, preprocess_input
+from keras.preprocessing import image
+from keras.preprocessing.image import ImageDataGenerator
+
+from sklearn.metrics import confusion_matrix
+import numpy as np
+import scipy as sp
+import matplotlib.pyplot as plt
+
+from glob import glob
+
+from skimage.transform import rescale, resize
+
+
+
+# useful for getting number of files
+image_files = glob('../large_files/256_ObjectCategories/*/*.jp*g')
+image_files += glob('../large_files/101_ObjectCategories/*/*.jp*g')
+
+
+
+# look at an image for fun
+plt.imshow(image.load_img(np.random.choice(image_files)))
+plt.show()
+
+
+# add preprocessing layer to the front of VGG
+resnet = ResNet50(input_shape=(224, 224, 3), weights='imagenet', include_top=True)
+
+# view the structure of the model
+# if you want to confirm we need activation_49
+resnet.summary()
+
+# make a model to get output before flatten
+activation_layer = resnet.get_layer('activation_49')
+
+# create a model object
+model = Model(inputs=resnet.input, outputs=activation_layer.output)
+
+# get the feature map weights
+final_dense = resnet.get_layer('fc1000')
+W = final_dense.get_weights()[0]
+
+
+while True:
+  img = image.load_img(np.random.choice(image_files), target_size=(224, 224))
+  x = preprocess_input(np.expand_dims(img, 0))
+  fmaps = model.predict(x)[0] # 7 x 7 x 2048
+
+  # get predicted class
+  probs = resnet.predict(x)
+  classnames = decode_predictions(probs)[0]
+  print(classnames)
+  classname = classnames[0][1]
+  pred = np.argmax(probs[0])
+
+  # get the 2048 weights for the relevant class
+  w = W[:, pred]
+
+  # "dot" w with fmaps
+  cam = fmaps.dot(w)
+
+  # upsample to 224 x 224
+  cam = sp.ndimage.zoom(cam, (32, 32), order=1)
+
+  plt.subplot(1,2,1)
+  plt.imshow(img, alpha=0.8)
+  plt.imshow(cam, cmap='jet', alpha=0.5)
+  plt.subplot(1,2,2)
+  plt.imshow(img)
+  plt.title(classname)
+  plt.show()
+
+  ans = input("Continue? (Y/n)")
+  if ans and ans[0].lower() == 'n':
+    break
+
+
+
+# def slowversion(A, w):
+#   N = len(w)
+#   result = np.zeros(A.shape[:-1])
+#   for i in range(N):
+#     result += A[:,:,i]*w[i]
+#   return result
+