def item_length(image, gc_image):
"""
TODO
"""
def higher_lower_body_split_line(face_rect):
box_width = face_rect[0][2]
y_split = face_rect[0][1] + 4.5 * box_width
return y_split
def dress_length():
lower_body_ycrcb = YCrCb_image[y_split:gc_image.shape[0] - 1, :, :]
lower_bgr = cv2.cvtColor(lower_body_ycrcb, cv2.COLOR_YCR_CB2BGR)
only_skin_down = kassper.skin_detection_with_grabcut(
lower_bgr, image, 'skin')
only_skin_down = background_removal.get_masked_image(
lower_bgr, kassper.clutter_removal(only_skin_down, 500))
legs_prop = legs_face_proportion(face_rect[0], only_skin_down)
return legs_prop
def sleeve_length():
"""
"""
x, y, w, h = face_rect[0]
upper_body_ycrcb = YCrCb_image[0:y_split, :, :]
upper_bgr = cv2.cvtColor(upper_body_ycrcb, cv2.COLOR_YCR_CB2BGR)
only_skin_up = kassper.skin_detection_with_grabcut(
upper_bgr, upper_bgr, 'skin')
only_skin_up = background_removal.get_masked_image(
upper_bgr, kassper.clutter_removal(only_skin_up, 500))
right_hand = only_skin_up[y + int(1.3 * h):y_split,
1:int(x + w / 2), :]
left_hand = only_skin_up[y + int(1.3 * h):y_split,
int(x + w / 2):only_skin_up.shape[1] - 1, :]
prop_left, prop_right = arms_face_proportions(face_rect[0], right_hand,
left_hand)
return prop_left, prop_right
def arms_face_proportions(face_rect, right_hand, left_hand):
num_of_left_hand_pixels = cv2.countNonZero(left_hand[:, :, 0])
num_of_right_hand_pixels = cv2.countNonZero(right_hand[:, :, 0])
prop_left = num_of_left_hand_pixels / face_rect[3]
prop_right = num_of_right_hand_pixels / face_rect[3]
return prop_left, prop_right
def legs_face_proportion(face_rect, lower_bgr_skin):
num_of_legs_pixels = cv2.countNonZero(lower_bgr_skin[:, :, 0])
legs_prop = num_of_legs_pixels / face_rect[3]
return legs_prop
YCrCb_image = cv2.cvtColor(gc_image, cv2.COLOR_BGR2YCR_CB)
face_rect = background_removal.find_face(image)
if len(face_rect) > 0:
y_split = higher_lower_body_split_line(face_rect)
prop_left, prop_right = sleeve_length()
legs_prop = dress_length()
else:
print 'no faces were detected'
return -1, -1, -1
# TODO: add general code that deals with that case
return legs_prop, prop_left, prop_right
def make_mask_test(image_url, bb=None):
svg_address = constants.svg_folder
image = Utils.get_cv2_img_array(image_url) # turn the URL into a cv2 image
small_image, resize_ratio = background_removal.standard_resize(
image, 400) # shrink image for faster process
bb = [int(b) for b in (np.array(bb) / resize_ratio)
] # shrink bb in the same ratio
fg_mask = background_removal.get_fg_mask(
small_image, bb
) # returns the grab-cut mask (if bb => PFG-PBG gc, if !bb => face gc)
# bb_mask = background_removal.get_binary_bb_mask(small_image, bb) # bounding box mask
# combined_mask = cv2.bitwise_and(fg_mask, bb_mask) # for sending the right mask to the fp
gc_image = background_removal.get_masked_image(small_image, fg_mask)
face_rect = background_removal.find_face(small_image)
if len(face_rect) > 0:
x, y, w, h = face_rect[0]
face_image = image[y:y + h, x:x + w, :]
without_skin = kassper.skin_removal(face_image, gc_image)
crawl_mask = kassper.clutter_removal(without_skin, 200)
without_clutter = background_removal.get_masked_image(
without_skin, crawl_mask)
mask = kassper.get_mask(without_clutter)
else:
mask = kassper.get_mask(gc_image)
return mask
def skin_removal_test():
image, ratio = background_removal.standard_resize(
background_removal.get_image(), 400)
fg_mask = background_removal.get_fg_mask(image)
gc_image = background_removal.get_masked_image(image, fg_mask)
face_rect = background_removal.find_face(image)
x, y, w, h = face_rect[0]
face_image = image[y:y + h, x:x + w, :]
without_skin = kassper.skin_removal(gc_image, face_image)
cv2.imshow('original', image)
cv2.imshow('gc', gc_image)
cv2.imshow('after skin', without_skin)
cv2.waitKey(0)
def crop_and_center_face(img_arr,
face_cascade=None,
eye_cascade=None,
x_size=250,
y_size=250):
if not face_cascade:
face_cascade = cv2.CascadeClassifier(
constants.classifiers_folder +
'/haarcascade_frontalface_default.xml')
if not eye_cascade:
eye_cascade = cv2.CascadeClassifier(constants.classifiers_folder +
'/haarcascade_eye.xml')
if not face_cascade:
logging.warning('cant get face cascade in cropface in gender.py')
if not eye_cascade:
logging.warning('cant get eye cascade in cropface in gender.py')
faces = background_removal.find_face(img_arr, max_num_of_faces=1)
gray = cv2.cvtColor(img_arr, constants.BGR2GRAYCONST)
i = 0
cropped = None
if faces is not None:
for (x, y, w, h) in faces:
roi_gray = gray[y:y + h, x:x + w]
eyes = eye_cascade.detectMultiScale(roi_gray)
if len(eyes) == 2:
left = eyes[0]
right = eyes[1]
if left[0] > right[0]:
temp = left
left = right
right = temp
original_left = [left[0] + x, left[1] + y, left[2], left[3]]
original_right = [
right[0] + x, right[1] + y, right[2], right[3]
]
lined_up = line_up_eyes(img_arr, original_left, original_right)
lined_up_roi = lined_up[0:y_size, 0:x_size]
dst = copy.copy(lined_up_roi) # rotated.copy.deepcopy()
return dst
if i == 0:
cropped = img_arr[y:y + h, x:x + w]
i += 1
# no set of two eyes found so just return as is
dst = copy.copy(cropped) # rotated.copy.deepcopy()
return dst
else:
return img_arr
def find_images(description):
print('starting to find ' + str(description))
for i in range(0, 500):
mdoc = dbUtils.lookfor_next_unbounded_feature_from_db_category(
current_item=i,
skip_if_marked_to_skip=True,
which_to_show='showAll',
filter_type='byWordInDescription',
category_id=None,
word_in_description=description,
db=None)
if mdoc is not None and 'doc' in mdoc:
doc = mdoc['doc']
# print doc
xlarge_url = doc['image']['sizes']['XLarge']['url']
# print('large img url:' + str(xlarge_url))
img_arr = Utils.get_cv2_img_array(xlarge_url)
if img_arr is None:
return None
small_img = background_removal.standard_resize(img_arr, 400)[0]
show_visual_output = False
if show_visual_output == True:
cv2.imshow('im1', img_arr)
k = cv2.waitKey(200)
cv2.imshow('smallim1', small_img)
k = cv2.waitKey(200)
relevance = background_removal.image_is_relevant(small_img)
if not relevance:
print('image is not relevant:' + str(description))
continue
print('image is relevant:' + str(description))
face1 = background_removal.find_face(img_arr)
if face1 is not None and len(face1) != 0:
print('face1:' + str(face1))
bb1 = face1[0]
get_pose_est_bbs(xlarge_url,
description,
n=i,
show_visual_output=show_visual_output,
bb=bb1)
else:
get_pose_est_bbs(xlarge_url,
description,
n=i,
show_visual_output=show_visual_output)
def get_bb(img_array, use_visual_output=True, fname='filename', item='dress'):
faces = background_removal.find_face(img_array, max_num_of_faces=1)
if faces is not None and faces is not [] and faces is not (
) and len(faces) == 1:
print('faces:' + str(faces))
face = faces[0]
else:
return None
if item == 'dress':
item_length = 12
item_width = 4
item_y_offset = 0
else:
item_length = 12
item_width = 4
item_y_offset = 0
orig_h, orig_w, d = img_array.shape
head_x0 = face[0]
head_y0 = face[1]
w = face[2]
h = face[3]
item_w = w * item_width
item_y0 = head_y0 + h + item_y_offset
item_h = min(h * item_length, orig_h - item_y0 - 1)
item_x0 = max(0, head_x0 + w / 2 - item_w / 2)
item_w = min(w * item_width, orig_w - item_x0 - 1)
item_box = [item_x0, item_y0, item_w, item_h]
if use_visual_output == True:
cv2.rectangle(img_array, (item_box[0], item_box[1]),
(item_box[0] + item_box[2], item_box[1] + item_box[3]),
GREEN,
thickness=1)
print('plotting img, dims:' + str(orig_w) + ' x ' + str(orig_h))
# im = plt.imshow(img_array)
# plt.show(block=False)
cv2.imshow(fname, img_array)
cv2.moveWindow('win', 100, 200)
k = cv2.waitKey(50)
# raw_input('enter to continue')
cv2.destroyAllWindows()
if k in [27, ord('Q'), ord('q')]: # exit on ESC
pass
assert (Utils.bounding_box_inside_image(img_array, item_box))
return item_box
for description in descriptions:
for i in range(0, 100):
mdoc = dbUtils.lookfor_next_unbounded_feature_from_db_category(
item_number=i,
skip_if_marked_to_skip=True,
which_to_show='showAll',
filter_type='byWordInDescription',
category_id=None,
word_in_description=description,
db=None,
)
if 'doc' in mdoc:
doc = mdoc['doc']
print doc
xlarge_url = doc['image']['sizes']['XLarge']['url']
print('large img url:' + str(xlarge_url))
img_arr = Utils.get_cv2_img_array(xlarge_url)
face1 = background_removal.find_face(img_arr)
if face1 is not None and len(face1) != 0:
print('face1:' + str(face1))
bb1 = face1
get_pose_est_bbs(xlarge_url, description, n=i, bb=bb1)
else:
get_pose_est_bbs(xlarge_url, description, n=i)
#Vnecks round neck natanel
# tight vs nontight dresses yakir