def createHeatmap(img):
#im = preprocess_image_batch(['examples/cars.jpg'], color_mode="bgr")
#img= cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
#img = cv2.resize(img,(224, 224))
im = np.asarray(img)
height, width = im.shape[:2]
im = cv2.resize(im,(width*2, height*2))
im = np.asarray([im])
#im = image.img_to_array([img])
#print(im.shape)
#None, 3, None, None
#(1, 800, 1280, 3)
#im=np.swapaxes(np.swapaxes(im, 1, 2), 0, 1)
im=np.swapaxes(im, 1, 3)
im=np.swapaxes(im, 2, 3)
out = model.predict(im)
#s = "n02084071"
s="n04576211"
ids = synset_to_dfs_ids(s)
heatmap = out[0,ids].sum(axis=0)
#plt.imshow(heatmap)
#plt.show()
return heatmap
def _demo_heatmap_script():
"""
Here is a script to compute the heatmap of the dog synsets.
We find the synsets corresponding to dogs on ImageNet website
"""
im = preprocess_image_batch(['examples/dog.jpg'], color_mode='rgb')
# Test pretrained model
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
model = convnet('alexnet', weights_path='weights/alexnet_weights.h5', heatmap=True)
model.compile(optimizer=sgd, loss='mse')
out = model.predict(im)
s = 'n02084071'
# Most of the synsets are not in the subset of the synsets used in ImageNet recognition task.
ids = np.array([id_ for id_ in synset_to_dfs_ids(s) if id_ is not None])
heatmap = out[0, ids, :, :].sum(axis=0)
return heatmap
img_batch = np.stack(img_list, axis=0)
except:
raise ValueError('when img_size and crop_size are None, images'
' in image_paths must have the same shapes.')
if out is not None and hasattr(out, 'append'):
out.append(img_batch)
else:
return img_batch
if __name__ == "__main__":
### Here is a script to compute the heatmap of the dog synsets.
## We find the synsets corresponding to dogs on ImageNet website
s = "n02084071"
ids = synset_to_dfs_ids(s)
# Most of the synsets are not in the subset of the synsets used in ImageNet recognition task.
ids = np.array([id_ for id_ in ids if id_ is not None])
im = preprocess_image_batch(['dog.jpg'], color_mode="rgb")
# Test pretrained model
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
model = convnet('alexnet',
weights_path="weights/alexnet_weights.h5",
heatmap=True)
model.compile(optimizer=sgd, loss='mse')
out = model.predict(im)
heatmap = out[0, ids, :, :].sum(axis=0)
img_batch = np.stack(img_list, axis=0)
if not out is None:
out.append(img_batch)
else:
return img_batch
if __name__ == "__main__":
### Here is a script to compute the heatmap of the cars synsets.
## We find the synsets corresponding to cars on ImageNet website
s = "n02084071"
ids = synset_to_dfs_ids(s)
# Most of the synsets are not in the subset of the synsets used in ImageNet recognition task.
ids = np.array([id for id in ids if id != None])
im = preprocess_image_batch(['examples/dog.jpg'],color_mode="bgr")
# Test pretrained model
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
model = convnet('alexnet',weights_path="weights/alexnet_weights.h5", heatmap=True)
model.compile(optimizer=sgd, loss='mse')
out = model.predict(im)
heatmap = out[0,ids,:,:].sum(axis=0)