def calc_histograms(directory):
'''
calc histograms of images in directory. saves them into ./histograms/imgname_histogram.jpg
:param directory: path to images (e.x.: 'D:\\PR aus Visual Computing\\Interestingness16data\\allvideos\\images\\interesting\\cropped')
:return:
'''
print('calc_histograms')
imgNames = read_img_names(directory)
for imgName in imgNames:
img = read_img(os.path.join(directory, imgName))
color = ('b', 'g', 'r')
for i, col in enumerate(color):
histr = cv2.calcHist([img], [i], None, [256], [0, 256])
plt.plot(histr, color=col)
plt.xlim([0, 256])
#plt.show()
# save img
if not os.path.exists(directory + '\\histograms\\'):
os.makedirs(directory + '\\histograms\\')
imgPath = directory + '\\histograms\\' + imgName.split(
'.jpg')[0] + '_histogram.png'
plt.savefig(imgPath)
def calc_histograms_bw(directory):
'''
calc grayscale histograms of images in directory. saves them into ./histograms/imgname_histogram.jpg
:param directory: path to images (e.x.: 'D:\\PR aus Visual Computing\\Interestingness16data\\allvideos\\images\\interesting\\cropped')
:return:
'''
print('calc_histograms_bw')
imgNames = read_img_names(directory)
for imgName in imgNames:
img = read_img(os.path.join(directory, imgName))
gray_image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
plt.hist(gray_image.ravel(), 256, [0, 256])
#plt.show()
# save img
if not os.path.exists(directory + '\\histograms\\grayscale\\'):
os.makedirs(directory + '\\histograms\\grayscale\\')
imgPath = directory + '\\histograms\\grayscale\\' + imgName.split(
'.jpg')[0] + '_histogram.png'
plt.savefig(imgPath)
plt.clf()
def face_to_img_ratios_to_csv(directory, face_frontal_cascade,
face_profile_cascade):
'''
calculates the ratio between image height and face heights and writes them in a csv file.
saves csv file at './face_img_ratios.csv'
:param directory: path to images (e.x.: 'D:\\PR aus Visual Computing\\Interestingness16data\\allvideos\\images\\interesting\\cropped\\faces')
:return:
'''
imgNames = read_img_names(directory)
csvfile = open(directory + '\\face_to_img_ratios.csv', 'wb')
csvwriter = csv.writer(csvfile,
delimiter=',',
quoting=csv.QUOTE_NONNUMERIC)
csvwriter.writerow(
['shotname', 'faces_detected', 'heights (relative to img height)'])
for imgName in imgNames:
img = read_img(os.path.join(directory, imgName))
faces_height = calc_face_to_img_height_ratio(img, face_frontal_cascade,
face_profile_cascade)
csvwriter.writerow([
imgName,
len(faces_height), ",".join(map(str, faces_height.values()))
])
csvfile.close()
def draw_phi_grid(directory):
'''
draws phi grid into image.
saves images in subdirectory ./phigrid
:param directory: path to images (e.x.: 'D:\\PR aus Visual Computing\\Interestingness16data\\allvideos\\images\\interesting\\cropped')
:return:
'''
print("draw_phi_grid")
imgNames = read_img_names(directory)
for imgName in imgNames:
img = read_img(os.path.join(directory, imgName))
img = draw_grid(img)
#save img
if not os.path.exists(directory + '\\phigrid\\'):
os.makedirs(directory + '\\phigrid\\')
imgPath = directory + '\\phigrid\\' + imgName
success = cv2.imwrite(imgPath, img)
def calc_histograms_plus_img(directory):
'''
calc histograms of images in directory. saves them beside image into ./histograms/combined/imgname_histogram.jpg
:param directory: path to images (e.x.: 'D:\\PR aus Visual Computing\\Interestingness16data\\allvideos\\images\\interesting\\cropped')
:return:
'''
print('calc_histograms_plus_img')
imgNames = read_img_names(directory)
fig = plt.figure()
for imgName in imgNames:
img = read_img(os.path.join(directory, imgName))
a = fig.add_subplot(2, 1, 1)
plt.imshow(cv2.cvtColor(img, cv2.COLOR_RGB2BGR))
a.set_title('Image')
a = fig.add_subplot(2, 1, 2)
color = ('b', 'g', 'r')
for i, col in enumerate(color):
histr = cv2.calcHist([img], [i], None, [256], [0, 256])
plt.plot(histr, color=col)
plt.xlim([0, 256])
a.set_title('Histogram')
#plt.show()
# save img
if not os.path.exists(directory + '\\histograms\\combined\\'):
os.makedirs(directory + '\\histograms\\combined\\')
imgPath = directory + '\\histograms\\combined\\' + imgName.split(
'.jpg')[0] + '_histogram.png'
plt.savefig(imgPath)
plt.clf()
def selectTrainingAndTestData(dir_interesting_imgs,
dir_uninteresting_imgs,
output_dir_training,
output_dir_test,
interesting_training_samples=50,
uninteresting_training_samples=500,
interesting_test_samples=50,
uninteresting_test_samples=500):
"""
selects randomly interesting and uninteresting imgs and stores them at output_directory
:param dir_interesting_imgs: directory of all interesting images
:param dir_uninteresting_imgs: directory of all uninteresting images
:param output_dir_training: output directory for training data
:param output_dir_test: output directory for test data
:param interesting_training_samples: number of how much interesting imgs should be selected for training data set
:param uninteresting_training_samples: number of how much uninteresting imgs should be selected for training data set
:param interesting_test_samples: number of how much interesting imgs should be selected for test data set
:param uninteresting_test_samples: number of how much uninteresting imgs should be selected for test data set
:return:
"""
#select random img_names
#interesting
img_names_interesting = read_img_names(dir_interesting_imgs)
total_sample_number_interesting = interesting_training_samples + interesting_test_samples
rnd_interesting_img_names = random.sample(img_names_interesting,
total_sample_number_interesting)
rnd_interesting_img_names_training = rnd_interesting_img_names[:
interesting_training_samples]
rnd_interesting_img_names_test = rnd_interesting_img_names[
interesting_training_samples:total_sample_number_interesting]
if not os.path.exists(os.path.join(output_dir_training, 'interesting')):
os.makedirs(os.path.join(output_dir_training, 'interesting'))
if not os.path.exists(os.path.join(output_dir_test, 'interesting')):
os.makedirs(os.path.join(output_dir_test, 'interesting'))
for img_name in rnd_interesting_img_names_training:
copyfile(os.path.join(dir_interesting_imgs, img_name),
os.path.join(output_dir_training, 'interesting', img_name))
for img_name in rnd_interesting_img_names_test:
copyfile(os.path.join(dir_interesting_imgs, img_name),
os.path.join(output_dir_test, 'interesting', img_name))
#uninteresting
img_names_uninteresting = read_img_names(dir_uninteresting_imgs)
total_sample_number_uninteresting = uninteresting_training_samples + uninteresting_test_samples
rnd_uninteresting_img_names = random.sample(
img_names_uninteresting, total_sample_number_uninteresting)
rnd_uninteresting_img_names_training = rnd_uninteresting_img_names[:
uninteresting_training_samples]
rnd_uninteresting_img_names_test = rnd_uninteresting_img_names[
uninteresting_training_samples:total_sample_number_uninteresting]
if not os.path.exists(os.path.join(output_dir_training, 'uninteresting')):
os.makedirs(os.path.join(output_dir_training, 'uninteresting'))
if not os.path.exists(os.path.join(output_dir_test, 'uninteresting')):
os.makedirs(os.path.join(output_dir_test, 'uninteresting'))
for img_name in rnd_uninteresting_img_names_training:
copyfile(os.path.join(dir_uninteresting_imgs, img_name),
os.path.join(output_dir_training, 'uninteresting', img_name))
for img_name in rnd_uninteresting_img_names_test:
copyfile(os.path.join(dir_uninteresting_imgs, img_name),
os.path.join(output_dir_test, 'uninteresting', img_name))