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)