Dekker1/ResearchMethods
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Merge branch 'master' of https://github.com/Dekker1/ResearchMethods

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This commit is contained in:
Kelvin Davis 2018-05-25 11:01:34 +10:00
commit c391cc7919
1 changed files with 2 additions and 16 deletions
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18
mini_proj/Load_Images.py
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@ -19,8 +19,6 @@ def preprocess(img):
hsv[:, :, 2] = exposure.equalize_hist(hsv[:, :, 2])
img = color.hsv2rgb(hsv)
#img = img/255 # Scaling images down to values of 0-255
return img
'''
@ -66,8 +64,7 @@ def gen_data(w_path, n_w_path):
pic = augment(pic)
pic_roll = np.rollaxis(pic, -1) # rolls colour axis to 0
imgs_raw.append(pic_roll)
imgs_lbl.append(1)
imgs_lbl.append(1) # Value of 1 as Waldo is still present in the transformed image
print('Completed: {0}/{1} Waldo images'.format(w+1, total_w))
w += 1
@ -91,7 +88,7 @@ def gen_data(w_path, n_w_path):
## Randomise and split data into training and test sets
# Code was modified from code written by: Kyle O'Brien (medium.com/@kylepob61392)
n_images = len(imgs_raw)
TRAIN_TEST_SPLIT = 0.75
TRAIN_TEST_SPLIT = 0.75 # Amount of trainingdata as a percentage of the total
# Split at the given index
split_index = int(TRAIN_TEST_SPLIT * n_images)
@ -113,17 +110,6 @@ def gen_data(w_path, n_w_path):
test_data.append(imgs_raw[index])
test_lbl.append(imgs_lbl[index])
# # Calculate what 30% of each set is
# third_of_w = math.floor(0.3*total_w)
# third_of_nw = math.floor(0.3*total_nw)
# # Split data into training and test data (60%/30%)
# train_data = np.append(imgs_raw[(third_of_w+1):total_w], imgs_raw[(total_w + third_of_nw + 1):len(imgs_raw)-1], axis=0)
# train_lbl = np.append(imgs_lbl[(third_of_w+1):total_w], imgs_lbl[(total_w + third_of_nw + 1):len(imgs_lbl)-1], axis=0)
# # If axis not given, both arrays are flattened before being appended
# test_data = np.append(imgs_raw[0:third_of_w], imgs_raw[total_w:(total_w + third_of_nw)], axis=0)
# test_lbl = np.append(imgs_lbl[0:third_of_w], imgs_lbl[total_w:(total_w + third_of_nw)], axis=0)
try:
# Save the data as numpy files
np.save('Waldo_train_data.npy', train_data)