def test_picture_exists_returns_true_when_picture_exists(self):
pic_id = uuid.uuid4()
doc_1 = {
'_id': str(pic_id),
'type': 'picture'
}
ps.save_picture_document(doc_1)
assert True == ps.picture_exists(pic_id)
def edge_detect(img_id_in,
alternate_img_id_in,
auto_id,
wide_id=None,
tight_id=None):
if picture_exists(alternate_img_id_in):
img_id_in = alternate_img_id_in
pic_dict_in = find_picture(img_id_in)
image_in = cv2.imread(pic_dict_in['uri'])
gray = cv2.cvtColor(image_in, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (3, 3), 0)
# apply Canny edge detection using a wide threshold, tight
# threshold, and automatically determined threshold
auto = auto_canny(blurred)
auto = auto_canny(image_in)
auto_filename = build_picture_name(auto_id)
auto_path_out = build_picture_path(picture_name=auto_filename,
snap_id=pic_dict_in['snap_id'])
cv2.imwrite(auto_path_out, auto)
auto_dict_out = make_edge_picture_dict(pic_id=auto_id,
pic_filename=auto_filename,
pic_path=auto_path_out,
snap_id=pic_dict_in['snap_id'],
group_id=pic_dict_in['group_id'],
source_pic_id=img_id_in,
edge_detect_type='auto')
save_picture_document(auto_dict_out)
if wide_id:
wide = cv2.Canny(blurred, 10, 200)
wide_filename = build_picture_name(wide_id)
wide_path_out = build_picture_path(picture_name=wide_filename,
snap_id=pic_dict_in['snap_id'])
cv2.imwrite(wide_path_out, wide)
wide_dict_out = make_edge_picture_dict(
pic_id=wide_id,
pic_filename=wide_filename,
pic_path=wide_path_out,
snap_id=pic_dict_in['snap_id'],
group_id=pic_dict_in['group_id'],
source_pic_id=img_id_in,
edge_detect_type='wide')
save_picture_document(wide_dict_out)
if tight_id:
tight = cv2.Canny(blurred, 225, 250)
tight_filename = build_picture_name(tight_id)
tight_path_out = build_picture_path(picture_name=tight_filename,
snap_id=pic_dict_in['snap_id'])
cv2.imwrite(tight_path_out, tight)
tight_dict_out = make_edge_picture_dict(
pic_id=tight_id,
pic_filename=tight_filename,
pic_path=tight_path_out,
snap_id=pic_dict_in['snap_id'],
group_id=pic_dict_in['group_id'],
source_pic_id=img_id_in,
edge_detect_type='tight')
save_picture_document(tight_dict_out)
def merge_images(img1_primary_id_in, img1_alternate_id_in, img2_id_in,
img_id_out, group_id):
#deal with the fact that different merge methods require different parameters
group_document = get_group_document(group_id)
group_id = group_document['_id']
img1_id_in = img1_primary_id_in
if picture_exists(img1_alternate_id_in):
img1_id_in = img1_alternate_id_in
if 'merge_type' in group_document:
merge_type = group_document['merge_type']
if hasattr(ImageChops, merge_type):
merge_method = getattr(ImageChops, merge_type)
else:
merge_method = getattr(ImageChops, 'screen')
img1_dict_in = find_picture(str(img1_id_in))
img1_filename_in = img1_dict_in['filename']
img2_dict_in = find_picture(str(img2_id_in))
img2_filename_in = img2_dict_in['filename']
img_filename_out = build_picture_name(img_id_out)
pic1_path_in = build_picture_path(picture_name=img1_filename_in,
snap_id=img1_dict_in['snap_id'])
pic2_path_in = build_picture_path(picture_name=img2_filename_in,
snap_id=img1_dict_in['snap_id'])
pic_path_out = build_picture_path(picture_name=img_filename_out,
snap_id=img1_dict_in['snap_id'])
image1_in = Image.open(pic1_path_in)
image2_in = Image.open(pic2_path_in)
image_out = merge_method(image1_in.convert('RGBA'),
image2_in.convert('RGBA'))
image_out.save(pic_path_out)
img_dict_out = {
'_id': str(img_id_out),
'type': 'picture',
'source': 'merge',
'source_image_id_1': str(img1_id_in),
'source_image_id_2': str(img2_id_in),
'merge_type': merge_type,
'group_id': group_id,
'snap_id': img1_dict_in['snap_id'],
'filename': img_filename_out,
'uri': pic_path_out,
'created': str(datetime.datetime.now())
}
save_picture_document(img_dict_out)
def merge_images(img1_primary_id_in, img1_alternate_id_in, img2_id_in, img_id_out, group_id):
#deal with the fact that different merge methods require different parameters
group_document = get_group_document(group_id)
group_id = group_document['_id']
img1_id_in = img1_primary_id_in
if picture_exists(img1_alternate_id_in):
img1_id_in = img1_alternate_id_in
if 'merge_type' in group_document:
merge_type = group_document['merge_type']
if hasattr(ImageChops, merge_type):
merge_method = getattr(ImageChops, merge_type)
else:
merge_method = getattr(ImageChops, 'screen')
img1_dict_in = find_picture(str(img1_id_in))
img1_filename_in = img1_dict_in['filename']
img2_dict_in = find_picture(str(img2_id_in))
img2_filename_in = img2_dict_in['filename']
img_filename_out = build_picture_name(img_id_out)
pic1_path_in = build_picture_path(picture_name=img1_filename_in, snap_id=img1_dict_in['snap_id'])
pic2_path_in = build_picture_path(picture_name=img2_filename_in, snap_id=img1_dict_in['snap_id'])
pic_path_out = build_picture_path(picture_name=img_filename_out, snap_id=img1_dict_in['snap_id'])
image1_in = Image.open(pic1_path_in)
image2_in = Image.open(pic2_path_in)
image_out = merge_method(image1_in.convert('RGBA'), image2_in.convert('RGBA'))
image_out.save(pic_path_out)
img_dict_out = {
'_id': str(img_id_out),
'type': 'picture',
'source': 'merge',
'source_image_id_1': str(img1_id_in),
'source_image_id_2': str(img2_id_in),
'merge_type': merge_type,
'group_id': group_id,
'snap_id': img1_dict_in['snap_id'],
'filename': img_filename_out,
'uri': pic_path_out,
'created': str(datetime.datetime.now())
}
save_picture_document(img_dict_out)
def edge_detect(img_id_in, alternate_img_id_in, auto_id, wide_id=None, tight_id=None):
if picture_exists(alternate_img_id_in):
img_id_in = alternate_img_id_in
pic_dict_in = find_picture(img_id_in)
image_in = cv2.imread(pic_dict_in['uri'])
gray = cv2.cvtColor(image_in, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (3, 3), 0)
# apply Canny edge detection using a wide threshold, tight
# threshold, and automatically determined threshold
auto = auto_canny(blurred)
auto = auto_canny(image_in)
auto_filename = build_picture_name(auto_id)
auto_path_out = build_picture_path(picture_name=auto_filename, snap_id=pic_dict_in['snap_id'])
cv2.imwrite(auto_path_out, auto)
auto_dict_out = make_edge_picture_dict(pic_id=auto_id, pic_filename=auto_filename, pic_path=auto_path_out,
snap_id=pic_dict_in['snap_id'], group_id=pic_dict_in['group_id'],
source_pic_id=img_id_in, edge_detect_type='auto')
save_picture_document(auto_dict_out)
if wide_id:
wide = cv2.Canny(blurred, 10, 200)
wide_filename = build_picture_name(wide_id)
wide_path_out = build_picture_path(picture_name=wide_filename, snap_id=pic_dict_in['snap_id'])
cv2.imwrite(wide_path_out, wide)
wide_dict_out = make_edge_picture_dict(pic_id=wide_id, pic_filename=wide_filename, pic_path=wide_path_out,
snap_id=pic_dict_in['snap_id'], group_id=pic_dict_in['group_id'],
source_pic_id=img_id_in, edge_detect_type='wide')
save_picture_document(wide_dict_out)
if tight_id:
tight = cv2.Canny(blurred, 225, 250)
tight_filename = build_picture_name(tight_id)
tight_path_out = build_picture_path(picture_name=tight_filename, snap_id=pic_dict_in['snap_id'])
cv2.imwrite(tight_path_out, tight)
tight_dict_out = make_edge_picture_dict(pic_id=tight_id, pic_filename=tight_filename, pic_path=tight_path_out,
snap_id=pic_dict_in['snap_id'], group_id=pic_dict_in['group_id'],
source_pic_id=img_id_in, edge_detect_type='tight')
save_picture_document(tight_dict_out)
def test_picture_exists_returns_false_when_picture_does_not_exist(self):
pic_id = uuid.uuid4()
assert False == ps.picture_exists(pic_id)
def test_picture_exists_returns_true_when_picture_exists(self):
pic_id = uuid.uuid4()
doc_1 = {'_id': str(pic_id), 'type': 'picture'}
ps.save_picture_document(doc_1)
assert True == ps.picture_exists(pic_id)
def test_picture_exists_returns_false_when_picture_does_not_exist(self):
pic_id = uuid.uuid4()
assert False == ps.picture_exists(pic_id)