def resize_image(self, image):
""" Appropriately resize a single image """
if self.resize_method == 'crop':
# resize keeping aspect ratio
resize_height = int(round(float(self.height) * self.resized_width / self.width))
resized = image_preprocessing.resize(image, (self.resized_width, resize_height))
# Crop the part we want
crop_y_cutoff = resize_height - CROP_OFFSET - self.resized_height
cropped = resized[crop_y_cutoff: crop_y_cutoff + self.resized_height, :]
return cropped
elif self.resize_method == 'scale':
return image_preprocessing.resize(image, (self.resized_width, self.resized_height))
else:
raise ValueError('Unrecognized image resize method.')
def hog_pipeline(gray_image, **kwargs):
if 'binary_image' in kwargs:
binary_image = kwargs['binary_image']
else:
binary_image, _ = image_preprocessing(gray_image)
return hog(resize(binary_image, 0.4), feature_vector=True, block_norm='L2-Hys')[:1000]
def get_observation(self):
assert self.buffer_count >= self.buffer_length
index = self.buffer_count % self.buffer_length - 1
max_image = self.screen_buffer[index]
for i in xrange(1, self.buffer_length):
max_image = np.maximum(max_image, self.screen_buffer[index - i,
...])
return image_preprocessing.resize(max_image,
size=(self.flags.input_height,
self.flags.input_width))
def resize_image(self, image):
""" Appropriately resize a single image """
if self.resize_method == 'crop':
# resize keeping aspect ratio
resize_height = int(round(
float(self.height) * self.resized_width / self.width))
resized = image_preprocessing.resize(image, (self.resized_width, resize_height))
# Crop the part we want
crop_y_cutoff = resize_height - CROP_OFFSET - self.resized_height
cropped = resized[crop_y_cutoff:
crop_y_cutoff + self.resized_height, :]
return cropped
elif self.resize_method == 'scale':
return image_preprocessing.resize(image, (self.resized_width, self.resized_height))
else:
raise ValueError('Unrecognized image resize method.')
