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crop the image before training corresponding to predicting #15

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crop the image before training corresponding to predicting #15

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8 changes: 6 additions & 2 deletions train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,13 +5,15 @@
import datetime
import time
import pickle
from numpy import array, zeros, r_
from PIL import Image
from numpy import array, zeros, r_, asarray
from numpy.random import seed, randn
from cost_function import cost_function, gradients
from scipy.optimize import fmin_l_bfgs_b
from scipy.misc import imread, imresize
from configuration import CLASSIFICATION_LABELS_AND_VALUES, IMAGE_DIMENSIONS
from configuration import LAMBDA, HIDDEN_LAYER_SIZE, CLASSIFICATION_LABELS
from configuration import PICAMERA_RESOLUTION_HEIGHT, PICAMERA_RESOLUTION_WIDTH

def load_images_to_array(classification_label_and_values):
"""Loads images to array"""
Expand All @@ -20,7 +22,9 @@ def load_images_to_array(classification_label_and_values):
print("Loading images to array...")
for class_label, class_value in classification_label_and_values.iteritems():
for filename in glob.glob("./"+class_label+"/*"):
image_array = imread(filename, flatten=True)
image = Image.open(filename).convert('L')
image = image.crop((0, PICAMERA_RESOLUTION_HEIGHT / 2, PICAMERA_RESOLUTION_WIDTH, PICAMERA_RESOLUTION_HEIGHT))
image_array = asarray(image)
resized_image_array = imresize(image_array, IMAGE_DIMENSIONS)
training_image_array = r_[training_image_array, [resized_image_array.flatten()]]
training_image_value = r_[training_image_value, [class_value]]
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