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 _calc_features(img_dirs, feature_calculator, *args):
#DEBUG
# counter = 0
#DEBUG END
features = []
for img_dir in img_dirs:
img = read_img(img_dir)
if args is None:
feature = feature_calculator(img)
else:
feature = feature_calculator(img, *args)
feature_files.save_features(img_dir, feature)
features.append(feature)
#DEBUG
# print 'image: ' + str(counter)
# counter = counter+1
#DEBUG END
return np.asarray(features)
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 calc_features(img_dirs, feature_names):
"""
calculates all features given by feature_names
:param img_dirs: a list of image paths
:type img_dirs: list
:param feature_names: a list containing all feature names
:type feature_names: list
:return: dict (img_dir, features) features is also a dict (feature_name, feature vector)
:rtype: dict
"""
features = {}
dir_haarfeatures = os.path.join(os.getcwd(), 'res', 'haarcascades')
face_frontal_cascade = cv2.CascadeClassifier(os.path.join(dir_haarfeatures, 'haarcascade_frontalface_default.xml'))
face_profile_cascade = cv2.CascadeClassifier(os.path.join(dir_haarfeatures, 'haarcascade_profileface.xml'))
model = FasterRCNNVGG16(pretrained_model='voc07')
for img_dir in img_dirs:
print 'calc features for: ' + img_dir
img = read_img(img_dir)
features[img_dir] = dict() #init features dict for each image
for name in feature_names:
if TU_feature_file_exists(img_dir, name):
continue
if name == Features.Face_count or name == Features.Rot_distance or name == Features.Face_bb:
face_count, rot_distance, face_bb = face_detection(img, face_frontal_cascade, face_profile_cascade)
feature_files.save_features(img_dir, Features.Face_count, face_count)
feature_files.save_features(img_dir, Features.Rot_distance, rot_distance)
feature_files.save_features(img_dir, Features.Face_bb, face_bb)
features[img_dir][Features.Face_count] = face_count
features[img_dir][Features.Rot_distance] = rot_distance
features[img_dir][Features.Face_bb] = face_bb
elif name == Features.Face_bb_full_img:
feature = _face_bb_full_img_calculator(img, face_frontal_cascade, face_profile_cascade)
feature_files.save_features(img_dir, Features.Face_bb_full_img, feature)
features[img_dir][Features.Face_bb_full_img] = feature
elif name == Features.Face_bb_quarter_imgs:
feature = _face_bb_quarter_imgs_calculator(img, face_frontal_cascade, face_profile_cascade)
feature_files.save_features(img_dir, Features.Face_bb_quarter_imgs, feature)
features[img_dir][Features.Face_bb_quarter_imgs] = feature
elif name == Features.Face_bb_eighth_imgs:
feature = _face_bb_eighth_imgs_calculator(img, face_frontal_cascade, face_profile_cascade)
feature_files.save_features(img_dir, Features.Face_bb_eighth_imgs, feature)
features[img_dir][Features.Face_bb_eighth_imgs] = feature
elif name == Features.Tilted_edges:
feature = _img_tilted_calculator(img)
feature_files.save_features(img_dir, Features.Tilted_edges, feature)
features[img_dir][Features.Tilted_edges] = feature
elif name == Features.Edge_hist_v0:
feature = _edge_hist_dir_calculator(img, False, False)
feature_files.save_features(img_dir, Features.Edge_hist_v0, feature)
features[img_dir][Features.Edge_hist_v0] = feature
elif name == Features.Edge_hist_v1:
feature = _edge_hist_dir_calculator(img, True, True)
feature_files.save_features(img_dir, Features.Edge_hist_v1, feature)
features[img_dir][Features.Edge_hist_v1] = feature
elif name == Features.Edge_hist_v2:
feature = _edge_hist_dir_calculator(img, True, False)
feature_files.save_features(img_dir, Features.Edge_hist_v2, feature)
features[img_dir][Features.Edge_hist_v2] = feature
elif name == Features.Symmetry:
feature = _symmetry_calculator(img, model)
feature_files.save_features(img_dir, Features.Symmetry, feature)
features[img_dir][Features.Symmetry] = feature
# precalculated features
elif name == Features.Hsv_hist:
feature = load_precalc_feature(img_dir, Features.Hsv_hist)
feature_files.save_features(img_dir, Features.Hsv_hist, feature)
features[img_dir][Features.Hsv_hist] = feature
elif name == Features.DenseSIFT_L0:
feature = load_precalc_feature(img_dir, Features.DenseSIFT_L0)
feature_files.save_features(img_dir, Features.DenseSIFT_L0, feature)
features[img_dir][Features.DenseSIFT_L0] = feature
elif name == Features.DenseSIFT_L1:
feature = load_precalc_feature(img_dir, Features.DenseSIFT_L1)
feature_files.save_features(img_dir, Features.DenseSIFT_L1, feature)
features[img_dir][Features.DenseSIFT_L1] = feature
elif name == Features.DenseSIFT_L2:
feature = load_precalc_feature(img_dir, Features.DenseSIFT_L2)
feature_files.save_features(img_dir, Features.DenseSIFT_L2, feature)
features[img_dir][Features.DenseSIFT_L2] = feature
elif name == Features.Hog_L0:
feature = load_precalc_feature(img_dir, Features.Hog_L0)
feature_files.save_features(img_dir, Features.Hog_L0, feature)
features[img_dir][Features.Hog_L0] = feature
elif name == Features.Hog_L1:
feature = load_precalc_feature(img_dir, Features.Hog_L1)
feature_files.save_features(img_dir, Features.Hog_L1, feature)
features[img_dir][Features.Hog_L1] = feature
elif name == Features.Hog_L2:
feature = load_precalc_feature(img_dir, Features.Hog_L2)
feature_files.save_features(img_dir, Features.Hog_L2, feature)
features[img_dir][Features.Hog_L2] = feature
elif name == Features.Lbp_L0:
feature = load_precalc_feature(img_dir, Features.Lbp_L0)
feature_files.save_features(img_dir, Features.Lbp_L0, feature)
features[img_dir][Features.Lbp_L0] = feature
elif name == Features.Lbp_L1:
feature = load_precalc_feature(img_dir, Features.Lbp_L1)
feature_files.save_features(img_dir, Features.Lbp_L1, feature)
features[img_dir][Features.Lbp_L1] = feature
elif name == Features.Lbp_L2:
feature = load_precalc_feature(img_dir, Features.Lbp_L2)
feature_files.save_features(img_dir, Features.Lbp_L2, feature)
features[img_dir][Features.Lbp_L2] = feature
elif name == Features.Gist:
feature = load_precalc_feature(img_dir, Features.Gist)
feature_files.save_features(img_dir, Features.Gist, feature)
features[img_dir][Features.Gist] = feature
elif name == Features.CNN_fc7:
feature = load_precalc_feature(img_dir, Features.CNN_fc7)
feature_files.save_features(img_dir, Features.CNN_fc7, feature)
features[img_dir][Features.CNN_fc7] = feature
elif name == Features.CNN_prob:
feature = load_precalc_feature(img_dir, Features.CNN_prob)
feature_files.save_features(img_dir, Features.CNN_prob, feature)
features[img_dir][Features.CNN_prob] = feature
else:
raise NotImplementedError
return features
def crop_black_borders(img_dirs):
"""
crops black borders around image. replaces image with the modified image.
:param img_dirs: a list containing all image paths
:type img_dirs: list
:return:
:rtype:
"""
for img_dir in img_dirs:
img = read_img(img_dir)
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
#sharpen image
kernel_sharpen_1 = np.array([[-1, -1, -1], [-1, 9, -1], [-1, -1, -1]])
img_gray = cv2.filter2D(img_gray, -1, kernel_sharpen_1)
edges = cv2.Canny(img_gray, 1, 1)
#morphological closing
kernel = np.ones((5, 5), np.uint8)
closing = cv2.morphologyEx(edges, cv2.MORPH_CLOSE, kernel)
for i in range(100):
closing = cv2.morphologyEx(closing, cv2.MORPH_CLOSE, kernel)
#check if border contains white pixels (contour)
contours_pixel_cnt = cv2.countNonZero(closing)
if contours_pixel_cnt > 0:
height, width, channels = img.shape
crop = False #is true if img should be cropped
row_idx_top, row_idx_bottom = find_horizontal_img_border(closing)
if row_idx_top != 0 and row_idx_bottom != height:
crop = True
#cv2.line(img, (0, row_idx_top), (width, row_idx_top), (0, 0, 255), 3)
#cv2.line(img, (0, row_idx_bottom), (width, row_idx_bottom), (0, 0, 255), 3)
col_idx_left, col_idx_right = find_vertical_img_border(closing)
if col_idx_left != 0 and col_idx_right != width:
crop = True
#cv2.line(img, (col_idx_left, 0), (col_idx_left, height), (0, 0, 255), 3)
#cv2.line(img, (col_idx_right, 0), (col_idx_right, height), (0, 0, 255), 3)
#crop image if necessary
if crop:
img = img[row_idx_top:row_idx_bottom, col_idx_left:col_idx_right]
# save img
'''
if not os.path.exists(directory + '\\cropped\\'):
os.makedirs(directory + '\\cropped\\')
imgPath = directory + '\\cropped\\' + imgName
success = cv2.imwrite(imgPath, img)
'''
success = cv2.imwrite(img_dir, img)
'''