def test_save_load_config(capsys):
cfg = config.PipelinesConfig()
cfg.morph_kernels_thickness = 10
cfg.save_yaml('test_cfg.yaml')
cfg2 = config.PipelinesConfig('test_cfg.yaml')
assert (cfg.__dict__ == cfg2.__dict__)
cfg.new_var = 10
cfg.save_yaml('test_cfg.yaml')
cfg2.load_yaml('test_cfg.yaml')
captured = capsys.readouterr()
assert ("WARNING" in captured.out)
def test_update_num_iterations():
cfg = config.PipelinesConfig()
cfg.height_range = (5, 5)
cfg.width_range = [(10, 10), (20, 20)]
cfg.update_num_iterations()
assert (cfg.num_iterations == 2)
assert (len(cfg.height_range) == 2)
assert (len(cfg.width_range) == 2)
def DefaultConfig():
cfg = config.PipelinesConfig()
cfg.width_range = (30, 45)
cfg.height_range = (30, 45)
cfg.scaling_factors = [1.0]
cfg.wh_ratio_range = (0.5, 1.5)
cfg.group_size_range = (1, 100)
cfg.dilation_iterations = 0
cfg.morph_kernels_type = 'rectangles'
return cfg
def test_autoconfig_from_vott_simple():
vott_dir = "tests/data/autoconfig_simple"
file_path = "tests/data/autoconfig_simple/dummy_example.png"
cfg = config.PipelinesConfig()
cfg.autoconfigure_from_vott(vott_dir, class_tags=['box'])
checkboxes = pipelines.get_checkboxes(file_path,
cfg=cfg,
px_threshold=0.01,
plot=False,
verbose=False)
assert (len(checkboxes) == 12)
cfg = config.PipelinesConfig()
cfg.autoconfigure_from_vott(vott_dir, class_tags=['box'])
rects, groups, _, _ = pipelines.get_boxes(file_path, cfg=cfg, plot=False)
assert (len(rects) == 23)
assert (len(groups) == 14)
def test_autoconfig_simple():
box_sizes = [(42, 44), (41, 47), (41, 44), (41, 44), (125, 54), (92, 103)]
file_path = "tests/data/autoconfig_simple/dummy_example.png"
cfg = config.PipelinesConfig()
cfg.autoconfigure(box_sizes)
checkboxes = pipelines.get_checkboxes(file_path,
cfg=cfg,
px_threshold=0.01,
plot=False,
verbose=False)
assert (len(checkboxes) == 12)
cfg = config.PipelinesConfig()
cfg.autoconfigure(box_sizes)
rects, groups, _, _ = pipelines.get_boxes(file_path, cfg=cfg, plot=False)
assert (len(rects) == 23)
assert (len(groups) == 14)
def get_checkbox_data_from_file(path):
ig = cv.imread(path)
data = []
try:
img_gry = cv.cvtColor(ig, cv.COLOR_BGR2GRAY) # Gray scaling the image
except Exception:
name = path.split("\\")[-1]
print(f" {name} is not a Image file.")
return
height, width = img_gry.shape # To get the height and width of the image
# Configuration for get_checkboxes
cfg = config.PipelinesConfig()
cfg.width_range = (15, 35)
cfg.height_range = (15, 40)
cfg.scaling_factors = [0.7]
cfg.wh_ratio_range = (0.5, 1.7)
# Checkboxes will contain data about all checkboxes in the image
checkboxes = get_checkboxes(img_gry, cfg=cfg)
print(f"{len(checkboxes)} checkbox found."
) # This will print total no of checkboxes found
for checkbox in checkboxes:
# checkbox[2] contains the pixel matrix of the current checkbox
if box_checker(checkbox[2]):
x, y, w, h = checkbox[0]
# Crop out of image 80px in y direction
# Here +80 is a kind of hard code, assuming 80 px from the top of box contains all data.
cp = img_gry[y:y + 80, x:x + width]
# This code will give desired data, but high chance of getting garbage value along with the data.
text = tess.image_to_string(cp)
data.append(text)
if not data:
print("No checkbox is checked")
return data
from common.common import compute_partial_ratio, compute_ratio, get_text_from_ocr, score_and_rank, apply_erosion, apply_dilatation, get_projection, load_image, find_runs, analyze_runs
from Training.Auto_Labelling.basic_implementation.autolabel_training import call_ocr
best_score = {}
config.thickness = 3
DATA_PATH = '../Data/'
file_name = 'form_example1.png'
# # Now we autodetect boxes on the form
# In[ ]:
from boxdetect import config
cfg = config.PipelinesConfig()
# important to adjust these values to match the size of boxes on your image
cfg.width_range = (30, 50)
cfg.height_range = (30, 35)
cfg.morph_kernels_type = 'rectangles'
# w/h ratio range for boxes/rectangles filtering
cfg.wh_ratio_range = (0.5, 1.6)
cfg.group_size_range = (2, 1000)
# In[ ]:
from boxdetect.pipelines import get_boxes