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 gc2mask_test(image, bb):
small_image, resize_ratio = background_removal.standard_resize(
image, 400) # shrink image for faster process
bb = np.array(bb) / resize_ratio
bb = bb.astype(np.uint16) # shrink bb in the same ratio
# bb = [int(b) for b in (np.array(bb)/resize_ratio)]
x, y, w, h = bb
cv2.rectangle(small_image, (x, y), (x + w, y + h), [0, 255, 0], 2)
cv2.imshow('1', small_image)
cv2.waitKey(0)
fg_mask = background_removal.get_fg_mask(
small_image, bb
) # returns the grab-cut mask (if bb => PFG-PBG gc, if !bb => face gc)
cv2.imshow('2', background_removal.get_masked_image(small_image, fg_mask))
cv2.waitKey(0)
bb_mask = background_removal.get_binary_bb_mask(small_image,
bb) # bounding box mask
cv2.imshow('3', background_removal.get_masked_image(small_image, bb_mask))
cv2.waitKey(0)
combined_mask = cv2.bitwise_and(
fg_mask, bb_mask) # for sending the right mask to the fp
cv2.imshow('4',
background_removal.get_masked_image(small_image, combined_mask))
cv2.waitKey(0)
return
def pd_test(image_url):
'''not sure if this is currenltly kosher 10.12.16'''
image = Utils.get_cv2_img_array(image_url)
mask, labels, pose = paperdoll.paperdoll_parse_enqueue.paperdoll_enqueue(
image_url, async=False)
cv2.imshow('image', image)
cv2.imshow('color_mask', color_paperdoll_mask(mask))
bgnd_mask = []
for num in np.unique(mask):
# convert numbers to labelsC
category = list(labels.keys())[list(labels.values()).index(num)]
item_mask = 255 * np.array(mask == num, dtype=np.uint8)
if category == 'null':
bgnd_mask = 255 - item_mask
if cv2.countNonZero(item_mask) > 2000:
item_image = background_removal.get_masked_image(image, item_mask)
after_gc = create_gc_mask(image, item_mask, bgnd_mask)
cv2.imshow(category + "'s image (" + str(num) + ')', item_image)
cv2.imshow(
category + "'s gc image",
background_removal.get_masked_image(
image,
background_removal.get_masked_image(image, after_gc)))
# cv2.imshow(category + "'s mask", 255 * item_mask / num)
cv2.waitKey(0)
cv2.destroyWindow(category + "'s image (" + str(num) + ')')
cv2.destroyWindow(category + "'s gc image")
cv2.destroyAllWindows()
def get_mask(small_image, bb=None):
if bb is not None:
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)
gc_image = background_removal.get_masked_image(small_image, fg_mask)
without_skin = kassper.skin_removal(gc_image, small_image)
crawl_mask = kassper.clutter_removal(without_skin, 400)
without_clutter = background_removal.get_masked_image(without_skin, crawl_mask)
fp_mask = kassper.get_mask(without_clutter)
return fp_mask
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 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 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 item_length_test(dir):
print dir
images_list = Utils.get_images_list(dir)
legs_prop_list = []
for image in images_list:
fg_mask = background_removal.get_fg_mask(image)
gc_image = background_removal.get_masked_image(image, fg_mask)
legs_prop, prop_left, prop_right = item_length(image, gc_image)
if legs_prop != -1:
legs_prop_list.append(legs_prop)
if len(images_list) > 100:
break
fig = plt.hist(legs_prop_list)
plt.title("proportion Histogram")
plt.xlabel("Value")
plt.ylabel("Frequency")
fig.show()
fig.close()
return
