diff --git a/camelot/cli.py b/camelot/cli.py index 9ca14e5..e3948e0 100644 --- a/camelot/cli.py +++ b/camelot/cli.py @@ -396,7 +396,8 @@ def network(c, *args, **kwargs): "Please specify output file format using --format") tables = read_pdf( - filepath, pages=pages, flavor="network", suppress_stdout=quiet, **kwargs + filepath, pages=pages, flavor="network", + suppress_stdout=quiet, **kwargs ) click.echo("Found {} tables".format(tables.n)) if plot_type is not None: diff --git a/camelot/core.py b/camelot/core.py index 4e2e44d..00591c3 100644 --- a/camelot/core.py +++ b/camelot/core.py @@ -454,7 +454,9 @@ class Table(): self.page = None self.flavor = None # Flavor of the parser used self.pdf_size = None # Dimensions of the original PDF page - self.parse_details = None # Field holding debug data + self._bbox = None # Bounding box in original document + self.parse = None # Parse information + self.parse_details = None # Field holding extra debug data self._image = None self._image_path = None # Temporary file to hold an image of the pdf diff --git a/camelot/handlers.py b/camelot/handlers.py index af6838d..b07bea3 100644 --- a/camelot/handlers.py +++ b/camelot/handlers.py @@ -7,7 +7,7 @@ import logging from PyPDF2 import PdfFileReader, PdfFileWriter from .core import TableList -from .parsers import Stream, Lattice, Network +from .parsers import Stream, Lattice, Network, Hybrid from .utils import ( build_file_path_in_temp_dir, get_page_layout, @@ -23,6 +23,7 @@ PARSERS = { "lattice": Lattice, "stream": Stream, "network": Network, + "hybrid": Hybrid, } @@ -177,7 +178,8 @@ class PDFHandler(): Parameters ---------- flavor : str (default: 'lattice') - The parsing method to use ('lattice', 'stream', or 'network'). + The parsing method to use ('lattice', 'stream', 'network', + or 'hybrid'). Lattice is used by default. suppress_stdout : str (default: False) Suppress logs and warnings. diff --git a/camelot/image_processing.py b/camelot/image_processing.py index 7b87101..9f23430 100644 --- a/camelot/image_processing.py +++ b/camelot/image_processing.py @@ -6,7 +6,9 @@ import cv2 import numpy as np -def adaptive_threshold(imagename, process_background=False, blocksize=15, c=-2): +def adaptive_threshold( + imagename, process_background=False, + blocksize=15, c=-2): """Thresholds an image using OpenCV's adaptiveThreshold. Parameters @@ -19,12 +21,12 @@ def adaptive_threshold(imagename, process_background=False, blocksize=15, c=-2): Size of a pixel neighborhood that is used to calculate a threshold value for the pixel: 3, 5, 7, and so on. - For more information, refer `OpenCV's adaptiveThreshold `_. + For more information, refer `OpenCV's adaptiveThreshold `_. # noqa c : int, optional (default: -2) Constant subtracted from the mean or weighted mean. Normally, it is positive but may be zero or negative as well. - For more information, refer `OpenCV's adaptiveThreshold `_. + For more information, refer `OpenCV's adaptiveThreshold `_. # noqa Returns ------- @@ -39,7 +41,10 @@ def adaptive_threshold(imagename, process_background=False, blocksize=15, c=-2): if process_background: threshold = cv2.adaptiveThreshold( - gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, blocksize, c + gray, + 255, + cv2.ADAPTIVE_THRESH_GAUSSIAN_C, + cv2.THRESH_BINARY, blocksize, c ) else: threshold = cv2.adaptiveThreshold( @@ -54,7 +59,8 @@ def adaptive_threshold(imagename, process_background=False, blocksize=15, c=-2): def find_lines( - threshold, regions=None, direction="horizontal", line_scale=15, iterations=0 + threshold, regions=None, + direction="horizontal", line_scale=15, iterations=0 ): """Finds horizontal and vertical lines by applying morphological transformations on an image. @@ -78,7 +84,7 @@ def find_lines( iterations : int, optional (default: 0) Number of times for erosion/dilation is applied. - For more information, refer `OpenCV's dilate `_. + For more information, refer `OpenCV's dilate `_. # noqa Returns ------- @@ -100,13 +106,15 @@ def find_lines( size = threshold.shape[1] // line_scale el = cv2.getStructuringElement(cv2.MORPH_RECT, (size, 1)) elif direction is None: - raise ValueError("Specify direction as either 'vertical' or 'horizontal'") + raise ValueError( + "Specify direction as either 'vertical' or 'horizontal'" + ) if regions is not None: region_mask = np.zeros(threshold.shape) for region in regions: x, y, w, h = region - region_mask[y : y + h, x : x + w] = 1 + region_mask[y:y + h, x:x + w] = 1 threshold = np.multiply(threshold, region_mask) threshold = cv2.erode(threshold, el) @@ -115,12 +123,14 @@ def find_lines( try: _, contours, _ = cv2.findContours( - threshold.astype(np.uint8), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE + threshold.astype(np.uint8), cv2.RETR_EXTERNAL, + cv2.CHAIN_APPROX_SIMPLE ) except ValueError: # for opencv backward compatibility contours, _ = cv2.findContours( - threshold.astype(np.uint8), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE + threshold.astype(np.uint8), cv2.RETR_EXTERNAL, + cv2.CHAIN_APPROX_SIMPLE ) for c in contours: @@ -202,7 +212,7 @@ def find_joints(contours, vertical, horizontal): tables = {} for c in contours: x, y, w, h = c - roi = joints[y : y + h, x : x + w] + roi = joints[y:y + h, x:x + w] try: __, jc, __ = cv2.findContours( roi.astype(np.uint8), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE diff --git a/camelot/io.py b/camelot/io.py index b7db318..4657058 100644 --- a/camelot/io.py +++ b/camelot/io.py @@ -99,7 +99,7 @@ def read_pdf( """ layout_kwargs = layout_kwargs or {} - if flavor not in ["lattice", "stream", "network"]: + if flavor not in ["lattice", "stream", "network", "hybrid"]: raise NotImplementedError( "Unknown flavor specified." " Use either 'lattice', 'stream', or 'network'" diff --git a/camelot/parsers/__init__.py b/camelot/parsers/__init__.py index f19c599..b33a1bf 100644 --- a/camelot/parsers/__init__.py +++ b/camelot/parsers/__init__.py @@ -3,3 +3,4 @@ from .stream import Stream from .lattice import Lattice from .network import Network +from .hybrid import Hybrid diff --git a/camelot/parsers/base.py b/camelot/parsers/base.py index 0ad4c42..f6c9ea6 100644 --- a/camelot/parsers/base.py +++ b/camelot/parsers/base.py @@ -34,8 +34,9 @@ class BaseParser(): self.id = parser_id self.table_regions = table_regions self.table_areas = table_areas - self.table_bbox = {} + self.table_bbox_parses = {} + self.columns = None self.copy_text = copy_text self.split_text = split_text self.strip_text = strip_text @@ -47,10 +48,18 @@ class BaseParser(): self.t_bbox = None # For plotting details of parsing algorithms + self.resolution = 300 # default plotting resolution of the PDF. self.parse_details = {} if not debug: self.parse_details = None + def table_bboxes(self): + return sorted( + self.table_bbox_parses.keys(), + key=lambda x: x[1], + reverse=True + ) + def prepare_page_parse(self, filename, layout, dimensions, page_idx, layout_kwargs): self.filename = filename @@ -142,6 +151,7 @@ class BaseParser(): table = Table(cols, rows) table.page = self.page table.order = table_idx + 1 + table._bbox = self.table_bboxes()[table_idx] return table @staticmethod @@ -177,7 +187,7 @@ class BaseParser(): table.cells[r_idx][c_idx].text = text return pos_errors - def _generate_columns_and_rows(self, bbox, table_idx): + def _generate_columns_and_rows(self, bbox, user_cols): # Pure virtual, must be defined by the derived parser raise NotImplementedError() @@ -199,20 +209,23 @@ class BaseParser(): _tables = [] # sort tables based on y-coord - for table_idx, bbox in enumerate( - sorted( - self.table_bbox.keys(), - key=lambda x: x[1], - reverse=True - ) - ): + for table_idx, bbox in enumerate(self.table_bboxes()): + if self.columns is not None and self.columns[table_idx] != "": + # user has to input boundary columns too + # take (0, pdf_width) by default + # similar to else condition + # len can't be 1 + user_cols = self.columns[table_idx].split(",") + user_cols = [float(c) for c in user_cols] + else: + user_cols = None + cols, rows, v_s, h_s = self._generate_columns_and_rows( bbox, - table_idx + user_cols ) table = self._generate_table( table_idx, cols, rows, v_s=v_s, h_s=h_s) - table._bbox = bbox _tables.append(table) return _tables @@ -222,6 +235,7 @@ class BaseParser(): """ table.flavor = self.id table.filename = self.filename + table.parse = self.table_bbox_parses[table._bbox] table.parse_details = self.parse_details pos_errors = self.compute_parse_errors(table) table.accuracy = compute_accuracy([[100, pos_errors]]) @@ -453,17 +467,16 @@ class TextBaseParser(BaseParser): raise ValueError("Length of table_areas and columns" " should be equal") - def record_parse_metadata(self, table): - """Record data about the origin of the table - """ - super().record_parse_metadata(table) - # for plotting - table._bbox = self.table_bbox - table._segments = None - def _generate_table(self, table_idx, cols, rows, **kwargs): table = self._initialize_new_table(table_idx, cols, rows) table = table.set_all_edges() self.record_parse_metadata(table) return table + + def record_parse_metadata(self, table): + """Record data about the origin of the table + """ + super().record_parse_metadata(table) + # for plotting + table._segments = None diff --git a/camelot/parsers/hybrid.py b/camelot/parsers/hybrid.py new file mode 100644 index 0000000..e84dfa8 --- /dev/null +++ b/camelot/parsers/hybrid.py @@ -0,0 +1,221 @@ +# -*- coding: utf-8 -*- + +from ..utils import ( + bboxes_overlap, + boundaries_to_split_lines, +) + +from .base import BaseParser +from .network import Network +from .lattice import Lattice + + +class Hybrid(BaseParser): + """Defines a hybrid parser, leveraging both network and lattice parsers. + + Parameters + ---------- + table_regions : list, optional (default: None) + List of page regions that may contain tables of the form x1,y1,x2,y2 + where (x1, y1) -> left-top and (x2, y2) -> right-bottom + in PDF coordinate space. + table_areas : list, optional (default: None) + List of table area strings of the form x1,y1,x2,y2 + where (x1, y1) -> left-top and (x2, y2) -> right-bottom + in PDF coordinate space. + columns : list, optional (default: None) + List of column x-coordinates strings where the coordinates + are comma-separated. + split_text : bool, optional (default: False) + Split text that spans across multiple cells. + flag_size : bool, optional (default: False) + Flag text based on font size. Useful to detect + super/subscripts. Adds around flagged text. + strip_text : str, optional (default: '') + Characters that should be stripped from a string before + assigning it to a cell. + edge_tol : int, optional (default: 50) + Tolerance parameter for extending textedges vertically. + row_tol : int, optional (default: 2) + Tolerance parameter used to combine text vertically, + to generate rows. + column_tol : int, optional (default: 0) + Tolerance parameter used to combine text horizontally, + to generate columns. + + """ + + def __init__( + self, + table_regions=None, + table_areas=None, + columns=None, + flag_size=False, + split_text=False, + strip_text="", + edge_tol=None, + row_tol=2, + column_tol=0, + debug=False, + **kwargs): + super().__init__( + "hybrid", + table_regions=table_regions, + table_areas=table_areas, + flag_size=flag_size, + split_text=split_text, + strip_text=strip_text, + debug=debug, + ) + self.network_parser = Network( + table_regions=table_regions, + table_areas=table_areas, + columns=columns, + flag_size=flag_size, + split_text=split_text, + strip_text=strip_text, + edge_tol=edge_tol, + row_tol=row_tol, + column_tol=column_tol, + debug=debug, + ) + self.lattice_parser = Lattice( + table_regions=table_regions, + table_areas=table_areas, + flag_size=flag_size, + split_text=split_text, + strip_text=strip_text, + edge_tol=edge_tol, + row_tol=row_tol, + column_tol=column_tol, + debug=debug, + ) + + def prepare_page_parse(self, filename, layout, dimensions, + page_idx, layout_kwargs): + super().prepare_page_parse(filename, layout, dimensions, + page_idx, layout_kwargs) + self.network_parser.prepare_page_parse( + filename, layout, dimensions, page_idx, layout_kwargs) + self.lattice_parser.prepare_page_parse( + filename, layout, dimensions, page_idx, layout_kwargs) + + def _generate_columns_and_rows(self, bbox, table_idx): + parser = self.table_bbox_parses[bbox] + return parser._generate_columns_and_rows(bbox, table_idx) + + def _generate_table(self, table_idx, cols, rows, **kwargs): + bbox = self.table_bboxes()[table_idx] + parser = self.table_bbox_parses[bbox] + return parser._generate_table(table_idx, cols, rows, **kwargs) + + @staticmethod + def _augment_boundaries_with_splits(boundaries, splits, tolerance=0): + """ Augment existing boundaries using provided hard splits. + + Boundaries: |---| |-| |---------| + Splits: | | | | + Augmented: |-------|-----|-------|--| + """ + idx_boundaries = len(boundaries) - 1 + idx_splits = len(splits) - 1 + previous_boundary = None + while True: + if idx_splits < 0: + # No more splits to incorporate, we're done + break + split = splits[idx_splits] + + if idx_boundaries < 0: + # Need to insert remaining splits + new_boundary = [split, boundaries[0][0]] + boundaries.insert(0, new_boundary) + idx_splits = idx_splits - 1 + else: + boundary = \ + boundaries[idx_boundaries] + if boundary[1] < \ + split + tolerance: + # The lattice column is further to the right of our + # col boundary. We move our left boundary to match. + boundary[1] = split + # And if there was another segment after, we make its + # right boundary match as well so that there's no gap + if previous_boundary is not None: + previous_boundary[0] = split + idx_splits = idx_splits - 1 + elif boundary[0] > \ + split - tolerance: + # Our boundary is fully after the split, move on + idx_boundaries = idx_boundaries - 1 + previous_boundary = boundary + else: + # The split is inside our boundary: split it + new_boundary = [split, boundary[1]] + boundaries.insert(idx_boundaries + 1, new_boundary) + boundary[1] = split + previous_boundary = new_boundary + idx_splits = idx_splits - 1 + return boundaries + + def _merge_bbox_analysis(self, lattice_bbox, network_bbox): + """ Identify splits that were only detected by lattice or by network + """ + lattice_parse = self.lattice_parser.table_bbox_parses[lattice_bbox] + lattice_cols, lattice_rows = \ + lattice_parse["col_anchors"], lattice_parse["row_anchors"] + + network_bbox_data = self.network_parser.table_bbox_parses[network_bbox] + network_cols_boundaries = network_bbox_data["cols_boundaries"] + + # Favor hybrid, but complete or adjust its columns based on the + # splits identified by lattice. + if network_cols_boundaries is None: + self.table_bbox_parses[lattice_bbox] = self.lattice_parser + else: + network_cols_boundaries = self._augment_boundaries_with_splits( + network_cols_boundaries, lattice_cols) # self.column_tol??? + augmented_bbox = ( + network_cols_boundaries[0][0], network_bbox[1], + network_cols_boundaries[-1][1], network_bbox[3], + ) + network_bbox_data["cols_anchors"] = \ + boundaries_to_split_lines(network_cols_boundaries) + + del self.network_parser.table_bbox_parses[network_bbox] + self.network_parser.table_bbox_parses[augmented_bbox] = \ + network_bbox_data + self.table_bbox_parses[augmented_bbox] = self.network_parser + + def _generate_table_bbox(self): + # Collect bboxes from both parsers + self.lattice_parser._generate_table_bbox() + _lattice_bboxes = sorted( + self.lattice_parser.table_bbox_parses, + key=lambda bbox: (bbox[0], -bbox[1])) + self.network_parser._generate_table_bbox() + _network_bboxes = sorted( + self.network_parser.table_bbox_parses, + key=lambda bbox: (bbox[0], -bbox[1])) + + # Merge the data from both processes + for lattice_bbox in _lattice_bboxes: + merged = False + + for idx in range(len(_network_bboxes)-1, -1, -1): + network_bbox = _network_bboxes[idx] + if not bboxes_overlap(lattice_bbox, network_bbox): + continue + self._merge_bbox_analysis(lattice_bbox, network_bbox) + # network_bbox_data["cols_boundaries"] + del _network_bboxes[idx] + merged = True + if not merged: + self.table_bbox_parses[lattice_bbox] = self.lattice_parser + + # Add the bboxes from network that haven't been merged + for network_bbox in _network_bboxes: + self.table_bbox_parses[network_bbox] = self.network_parser + + def record_parse_metadata(self, table): + super().record_parse_metadata(table) diff --git a/camelot/parsers/lattice.py b/camelot/parsers/lattice.py index 84ce5a2..05bd649 100644 --- a/camelot/parsers/lattice.py +++ b/camelot/parsers/lattice.py @@ -2,8 +2,6 @@ from __future__ import division import os -import copy - from .base import BaseParser from ..utils import ( @@ -173,7 +171,6 @@ class Lattice(BaseParser): super().record_parse_metadata(table) # for plotting table._image = self.pdf_image # Reuse the image used for calc - table._bbox_unscaled = self.table_bbox_unscaled table._segments = (self.vertical_segments, self.horizontal_segments) def _generate_table_bbox(self): @@ -193,7 +190,7 @@ class Lattice(BaseParser): os.path.basename(self.filename), ".png" ) - export_pdf_as_png(self.filename, self.image_path) + export_pdf_as_png(self.filename, self.image_path, self.resolution) self.pdf_image, self.threshold = adaptive_threshold( self.image_path, process_background=self.process_background, @@ -250,17 +247,59 @@ class Lattice(BaseParser): areas = scale_areas(self.table_areas) table_bbox = find_joints(areas, vertical_mask, horizontal_mask) - self.table_bbox_unscaled = copy.deepcopy(table_bbox) - [ - self.table_bbox, + self.table_bbox_parses, self.vertical_segments, self.horizontal_segments ] = scale_image( table_bbox, vertical_segments, horizontal_segments, pdf_scalers ) - def _generate_columns_and_rows(self, bbox, table_idx): + for bbox, parse in self.table_bbox_parses.items(): + joints = parse["joints"] + + # Merge x coordinates that are close together + line_tol = self.line_tol + # Sort the joints, make them a list of lists (instead of sets) + joints_normalized = list( + map( + lambda x: list(x), + sorted(joints, key=lambda j: - j[0]) + ) + ) + for idx in range(1, len(joints_normalized)): + x_left, x_right = \ + joints_normalized[idx-1][0], joints_normalized[idx][0] + if x_left - line_tol <= x_right <= x_left + line_tol: + joints_normalized[idx][0] = x_left + + # Merge y coordinates that are close together + joints_normalized = sorted(joints_normalized, key=lambda j: -j[1]) + for idx in range(1, len(joints_normalized)): + y_bottom, y_top = \ + joints_normalized[idx-1][1], joints_normalized[idx][1] + if y_bottom - line_tol <= y_top <= y_bottom + line_tol: + joints_normalized[idx][1] = y_bottom + + # FRHTODO: check this is useful, otherwise get rid of the code + # above + parse["joints_normalized"] = joints_normalized + + cols = list(map(lambda coords: coords[0], joints)) + cols.extend([bbox[0], bbox[2]]) + rows = list(map(lambda coords: coords[1], joints)) + rows.extend([bbox[1], bbox[3]]) + + # sort horizontal and vertical segments + cols = merge_close_lines(sorted(cols), line_tol=self.line_tol) + rows = merge_close_lines( + sorted(rows, reverse=True), + line_tol=self.line_tol + ) + parse["col_anchors"] = cols + parse["row_anchors"] = rows + + def _generate_columns_and_rows(self, bbox, user_cols): # select elements which lie within table_bbox v_s, h_s = segments_in_bbox( bbox, self.vertical_segments, self.horizontal_segments @@ -270,21 +309,17 @@ class Lattice(BaseParser): self.horizontal_text, self.vertical_text ) + parse = self.table_bbox_parses[bbox] - cols, rows = zip(*self.table_bbox[bbox]) - cols, rows = list(cols), list(rows) - cols.extend([bbox[0], bbox[2]]) - rows.extend([bbox[1], bbox[3]]) - # sort horizontal and vertical segments - cols = merge_close_lines(sorted(cols), line_tol=self.line_tol) - rows = merge_close_lines( - sorted(rows, reverse=True), - line_tol=self.line_tol - ) # make grid using x and y coord of shortlisted rows and cols - cols = [(cols[i], cols[i + 1]) for i in range(0, len(cols) - 1)] - rows = [(rows[i], rows[i + 1]) for i in range(0, len(rows) - 1)] - + cols = [ + (parse["col_anchors"][i], parse["col_anchors"][i + 1]) + for i in range(0, len(parse["col_anchors"]) - 1) + ] + rows = [ + (parse["row_anchors"][i], parse["row_anchors"][i + 1]) + for i in range(0, len(parse["row_anchors"]) - 1) + ] return cols, rows, v_s, h_s def _generate_table(self, table_idx, cols, rows, **kwargs): diff --git a/camelot/parsers/network.py b/camelot/parsers/network.py index 801da82..3356053 100644 --- a/camelot/parsers/network.py +++ b/camelot/parsers/network.py @@ -19,7 +19,8 @@ from ..utils import ( text_in_bbox, textlines_overlapping_bbox, bbox_from_textlines, - find_columns_coordinates, + find_columns_boundaries, + boundaries_to_split_lines, text_in_bbox_per_axis, ) @@ -438,7 +439,7 @@ class TextNetworks(TextAlignments): tls_search_space.remove(most_aligned_tl) tls_in_bbox = [most_aligned_tl] last_bbox = None - last_cols_cand = [most_aligned_tl.x0, most_aligned_tl.x1] + last_cols_bounds = [(most_aligned_tl.x0, most_aligned_tl.x1)] while last_bbox != bbox: if parse_details_search is not None: # Store debug info @@ -479,9 +480,9 @@ class TextNetworks(TextAlignments): # of the new row won't reduce the number of columns. # This happens when text covers multiple rows - that's only # allowed in the header, treated separately. - cols_cand = find_columns_coordinates(tls_in_new_box) + cols_bounds = find_columns_boundaries(tls_in_new_box) if direction in ["bottom", "top"] and \ - len(cols_cand) < len(last_cols_cand): + len(cols_bounds) < len(last_cols_bounds): continue # We have an expansion candidate: register it, update the @@ -489,7 +490,7 @@ class TextNetworks(TextAlignments): # We use bbox_from_textlines instead of cand_bbox in case some # overlapping textlines require a large bbox for strict fit. bbox = cand_bbox = list(bbox_from_textlines(tls_in_new_box)) - last_cols_cand = cols_cand + last_cols_bounds = cols_bounds tls_in_bbox.extend(new_tls) for i in range(len(tls_search_space) - 1, -1, -1): textline = tls_search_space[i] @@ -591,7 +592,7 @@ class Network(TextBaseParser): textlines = self._apply_regions_filter(all_textlines) textlines_processed = {} - self.table_bbox = {} + self.table_bbox_parses = {} if self.parse_details is not None: parse_details_network_searches = [] self.parse_details["network_searches"] = \ @@ -641,7 +642,8 @@ class Network(TextBaseParser): # Get all the textlines that overlap with the box, compute # columns tls_in_bbox = textlines_overlapping_bbox(bbox_body, textlines) - cols_anchors = find_columns_coordinates(tls_in_bbox) + cols_boundaries = find_columns_boundaries(tls_in_bbox) + cols_anchors = boundaries_to_split_lines(cols_boundaries) # Unless the user gave us strict bbox_body, try to find a header # above the body to build the full bbox. @@ -662,10 +664,11 @@ class Network(TextBaseParser): table_parse = { "bbox_body": bbox_body, + "cols_boundaries": cols_boundaries, "cols_anchors": cols_anchors, "bbox_full": bbox_full } - self.table_bbox[bbox_full] = table_parse + self.table_bbox_parses[bbox_full] = table_parse if self.parse_details is not None: self.parse_details["col_searches"].append(table_parse) @@ -678,7 +681,7 @@ class Network(TextBaseParser): textlines )) - def _generate_columns_and_rows(self, bbox, table_idx): + def _generate_columns_and_rows(self, bbox, user_cols): # select elements which lie within table_bbox self.t_bbox = text_in_bbox_per_axis( bbox, @@ -706,18 +709,14 @@ class Network(TextBaseParser): rows_grouped = self._group_rows(all_tls, row_tol=self.row_tol) rows = self._join_rows(rows_grouped, text_y_max, text_y_min) - if self.columns is not None and self.columns[table_idx] != "": - # user has to input boundary columns too - # take (0, pdf_width) by default - # similar to else condition - # len can't be 1 - cols = self.columns[table_idx].split(",") - cols = [float(c) for c in cols] - cols.insert(0, text_x_min) - cols.append(text_x_max) - cols = [(cols[i], cols[i + 1]) for i in range(0, len(cols) - 1)] + if user_cols is not None: + cols = [text_x_min] + user_cols + [text_x_max] + cols = [ + (cols[i], cols[i + 1]) + for i in range(0, len(cols) - 1) + ] else: - parse_details = self.table_bbox[bbox] + parse_details = self.table_bbox_parses[bbox] col_anchors = parse_details["cols_anchors"] cols = list(map( lambda idx: [col_anchors[idx], col_anchors[idx + 1]], diff --git a/camelot/parsers/stream.py b/camelot/parsers/stream.py index 6a1da23..1f8d8c6 100644 --- a/camelot/parsers/stream.py +++ b/camelot/parsers/stream.py @@ -122,14 +122,14 @@ class Stream(TextBaseParser): self.horizontal_text) hor_text.extend(region_text) # find tables based on nurminen's detection algorithm - table_bbox = self._nurminen_table_detection(hor_text) + table_bbox_parses = self._nurminen_table_detection(hor_text) else: - table_bbox = {} + table_bbox_parses = {} for area_str in self.table_areas: - table_bbox[bbox_from_str(area_str)] = None - self.table_bbox = table_bbox + table_bbox_parses[bbox_from_str(area_str)] = None + self.table_bbox_parses = table_bbox_parses - def _generate_columns_and_rows(self, bbox, table_idx): + def _generate_columns_and_rows(self, bbox, user_cols): # select elements which lie within table_bbox self.t_bbox = text_in_bbox_per_axis( bbox, @@ -140,26 +140,18 @@ class Stream(TextBaseParser): text_x_min, text_y_min, text_x_max, text_y_max = bbox_from_textlines( self.t_bbox["horizontal"] + self.t_bbox["vertical"] ) - # FRHTODO: - # This algorithm takes the horizontal textlines in the bbox, and groups - # them into rows based on their bottom y0. - # That's wrong: it misses the vertical items, and misses out on all - # the alignment identification work we've done earlier. + rows_grouped = self._group_rows( self.t_bbox["horizontal"], row_tol=self.row_tol) rows = self._join_rows(rows_grouped, text_y_max, text_y_min) elements = [len(r) for r in rows_grouped] - if self.columns is not None and self.columns[table_idx] != "": - # user has to input boundary columns too - # take (0, pdf_width) by default - # similar to else condition - # len can't be 1 - cols = self.columns[table_idx].split(",") - cols = [float(c) for c in cols] - cols.insert(0, text_x_min) - cols.append(text_x_max) - cols = [(cols[i], cols[i + 1]) for i in range(0, len(cols) - 1)] + if user_cols is not None: + cols = [text_x_min] + user_cols + [text_x_max] + cols = [ + (cols[i], cols[i + 1]) + for i in range(0, len(cols) - 1) + ] else: # calculate mode of the list of number of elements in # each row to guess the number of columns @@ -175,8 +167,8 @@ class Stream(TextBaseParser): ncols = max(set(elements), key=elements.count) else: warnings.warn( - "No tables found in table area {}" - .format(table_idx + 1) + "No tables found in table area {bbox}".format( + bbox=bbox) ) cols = [ (t.x0, t.x1) diff --git a/camelot/plotting.py b/camelot/plotting.py index 4fe57a6..26aec3e 100644 --- a/camelot/plotting.py +++ b/camelot/plotting.py @@ -74,7 +74,7 @@ def draw_labeled_bbox( ) -def draw_pdf(table, ax, to_pdf_scale=True): +def draw_pdf(table, ax): """Draw the content of the table's source pdf into the passed subplot Parameters @@ -83,14 +83,9 @@ def draw_pdf(table, ax, to_pdf_scale=True): ax : matplotlib.axes.Axes (optional) - to_pdf_scale : bool (optional) - """ img = table.get_pdf_image() - if to_pdf_scale: - ax.imshow(img, extent=(0, table.pdf_size[0], 0, table.pdf_size[1])) - else: - ax.imshow(img) + ax.imshow(img, extent=(0, table.pdf_size[0], 0, table.pdf_size[1])) def draw_parse_constraints(table, ax): @@ -132,8 +127,6 @@ def draw_text(table, ax): table : camelot.core.Table ax : matplotlib.axes.Axes (optional) - ax : matplotlib.axes.Axes - """ bbox = bbox_from_textlines(table.textlines) for t in table.textlines: @@ -150,18 +143,14 @@ def draw_text(table, ax): extend_axe_lim(ax, bbox) -def prepare_plot(table, ax=None, to_pdf_scale=True): +def prepare_plot(table, ax=None): """Initialize plot and draw common components Parameters ---------- table : camelot.core.Table + ax : matplotlib.axes.Axes (optional) - to_pdf_scale : - - ax : matplotlib.axes.Axes - - to_pdf_scale : bool (optional) Returns ------- @@ -170,7 +159,7 @@ def prepare_plot(table, ax=None, to_pdf_scale=True): if ax is None: fig = plt.figure() ax = fig.add_subplot(111, aspect="equal") - draw_pdf(table, ax, to_pdf_scale) + draw_pdf(table, ax) draw_parse_constraints(table, ax) return ax @@ -186,7 +175,8 @@ class PlotMethods(): table: camelot.core.Table A Camelot Table. kind : str, optional (default: 'text') - {'text', 'grid', 'contour', 'joint', 'line'} + {'text', 'grid', 'contour', 'joint', 'line', + 'network_table_search'} The element type for which a plot should be generated. filepath: str, optional (default: None) Absolute path for saving the generated plot. @@ -203,9 +193,12 @@ class PlotMethods(): raise NotImplementedError( "Lattice flavor does not support kind='{}'".format(kind) ) - if table.flavor in ["stream", "network"] and kind in ["line"]: + if table.flavor != "lattice" and kind in ["line"]: raise NotImplementedError( - "Stream flavor does not support kind='{}'".format(kind) + "{flavor} flavor does not support kind='{kind}'".format( + flavor=table.flavor, + kind=kind + ) ) plot_method = getattr(self, kind) @@ -274,25 +267,21 @@ class PlotMethods(): """ _FOR_LATTICE = table.flavor == "lattice" - ax = prepare_plot(table, ax, to_pdf_scale=not _FOR_LATTICE) - - if _FOR_LATTICE: - table_bbox = table._bbox_unscaled - else: - table_bbox = {table._bbox: None} + ax = prepare_plot(table, ax) if not _FOR_LATTICE: draw_text(table, ax) - for t in table_bbox.keys(): - ax.add_patch( - patches.Rectangle( - (t[0], t[1]), t[2] - t[0], t[3] - t[1], - fill=False, color="red" - ) + ax.add_patch( + patches.Rectangle( + (table._bbox[0], table._bbox[1]), + table._bbox[2] - table._bbox[0], + table._bbox[3] - table._bbox[1], + fill=False, color="red" ) - if not _FOR_LATTICE: - extend_axe_lim(ax, t) + ) + if not _FOR_LATTICE: + extend_axe_lim(ax, table._bbox) return ax.get_figure() @@ -393,14 +382,12 @@ class PlotMethods(): fig : matplotlib.fig.Figure """ - ax = prepare_plot(table, ax, to_pdf_scale=False) - table_bbox = table._bbox_unscaled + ax = prepare_plot(table, ax) x_coord = [] y_coord = [] - for k in table_bbox.keys(): - for coord in table_bbox[k]: - x_coord.append(coord[0]) - y_coord.append(coord[1]) + for coord in table.parse["joints"]: + x_coord.append(coord[0]) + y_coord.append(coord[1]) ax.plot(x_coord, y_coord, "ro") return ax.get_figure() diff --git a/camelot/utils.py b/camelot/utils.py index 27d5b1e..d24230d 100644 --- a/camelot/utils.py +++ b/camelot/utils.py @@ -297,8 +297,9 @@ def scale_image(tables, v_segments, h_segments, factors): j_x, j_y = zip(*tables[k]) j_x = [scale(j, scaling_factor_x) for j in j_x] j_y = [scale(abs(translate(-img_y, j)), scaling_factor_y) for j in j_y] - joints = zip(j_x, j_y) - tables_new[(x1, y1, x2, y2)] = joints + tables_new[(x1, y1, x2, y2)] = { + "joints": list(zip(j_x, j_y)) + } v_segments_new = [] for v in v_segments: @@ -434,6 +435,16 @@ def bbox_from_str(bbox_str): ) +def bboxes_overlap(bbox1, bbox2): + (left1, bottom1, right1, top1) = bbox1 + (left2, bottom2, right2, top2) = bbox2 + return ( + (left1 < left2 < right1) or (left1 < right2 < right1) + ) and ( + (bottom1 < bottom2 < top1) or (bottom1 < top2 < top1) + ) + + def textlines_overlapping_bbox(bbox, textlines): """Returns all text objects which overlap or are within a bounding box. @@ -451,12 +462,10 @@ def textlines_overlapping_bbox(bbox, textlines): List of PDFMiner text objects. """ - (left, bottom, right, top) = bbox t_bbox = [ t for t in textlines - if ((left < t.x0 < right) or (left < t.x1 < right)) - and ((bottom < t.y0 < top) or (bottom < t.y1 < top)) + if bboxes_overlap(bbox, (t.x0, t.y0, t.x1, t.y1)) ] return t_bbox @@ -560,27 +569,25 @@ def bbox_from_textlines(textlines): return bbox -def find_columns_coordinates(tls, min_gap=1.0): - """Given a list of text objects, guess columns boundaries and returns a - list of x-coordinates for split points between columns. +def find_columns_boundaries(tls, min_gap=1.0): + """Make a list of disjunct cols boundaries for a list of text objects Parameters ---------- tls : list of PDFMiner text object. - min_gap : minimum distance between columns. Any elements closer than this - threshold are merged together. This is to prevent spaces between words - to be misinterpreted as column boundaries. + min_gap : minimum distance between columns. Any elements closer than + this threshold are merged together. This is to prevent spaces between + words to be misinterpreted as boundaries. Returns ------- - cols_anchors : list - List of x-coordinates for columns. + boundaries : list + List x-coordinates for cols. + [(1st col left, 1st col right), (2nd col left, 2nd col right), ...] + """ - # Make a list of disjunct cols boundaries across the textlines - # that comprise the table. - # [(1st col left, 1st col right), (2nd col left, 2nd col right), ...] cols_bounds = [] tls.sort(key=lambda tl: tl.x0) for tl in tls: @@ -588,18 +595,64 @@ def find_columns_coordinates(tls, min_gap=1.0): cols_bounds.append([tl.x0, tl.x1]) else: cols_bounds[-1][1] = max(cols_bounds[-1][1], tl.x1) + return cols_bounds + +def find_rows_boundaries(tls, min_gap=1.0): + """Make a list of disjunct rows boundaries for a list of text objects + + Parameters + ---------- + tls : list of PDFMiner text object. + + min_gap : minimum distance between rows. Any elements closer than + this threshold are merged together. + + Returns + ------- + boundaries : list + List y-coordinates for rows. + [(1st row bottom, 1st row top), (2nd row bottom, 2nd row top), ...] + + """ + rows_bounds = [] + tls.sort(key=lambda tl: tl.y0) + for tl in tls: + if (not rows_bounds) or rows_bounds[-1][1] + min_gap < tl.y0: + rows_bounds.append([tl.y0, tl.y1]) + else: + rows_bounds[-1][1] = max(rows_bounds[-1][1], tl.y1) + return rows_bounds + + +def boundaries_to_split_lines(boundaries): + """Find split lines given a list of boundaries between rows or cols. + + Boundaries: [ a ] [b] [ c ] [d] + Splits: | | | | | + + Parameters + ---------- + boundaries : list + List of tuples of x- (for columns) or y- (for rows) coord boundaries. + These are the (left, right most) or (bottom, top most) coordinates. + + Returns + ------- + anchors : list + List of coordinates representing the split points, each half way + between boundaries + + """ # From the row boundaries, identify splits by getting the mid points # between the boundaries. - # Row boundaries: [ a ] [b] [ c ] - # Splits: | | | | - cols_anchors = list(map( - lambda idx: (cols_bounds[idx-1][1] + cols_bounds[idx][0]) / 2.0, - range(1, len(cols_bounds)) + anchors = list(map( + lambda idx: (boundaries[idx-1][1] + boundaries[idx][0]) / 2.0, + range(1, len(boundaries)) )) - cols_anchors.insert(0, cols_bounds[0][0]) - cols_anchors.append(cols_bounds[-1][1]) - return cols_anchors + anchors.insert(0, boundaries[0][0]) + anchors.append(boundaries[-1][1]) + return anchors def get_index_closest_point(point, sorted_list, fn=lambda x: x): @@ -1129,17 +1182,20 @@ def get_text_objects(layout, ltype="char", t=None): return t -def export_pdf_as_png(pdf_path, destination_path): +def export_pdf_as_png(pdf_path, destination_path, resolution=300): """Generate an image from a pdf. Parameters ---------- pdf_path : str destination_path : str + resolution : int """ - gs_call = "-q -sDEVICE=png16m -o {destination_path} -r300 {pdf_path}"\ + gs_call = "-q -sDEVICE=png16m -o " \ + "{destination_path} -r{resolution} {pdf_path}" \ .format( destination_path=destination_path, + resolution=resolution, pdf_path=pdf_path ) gs_call = gs_call.encode().split() diff --git a/parser-comparison-notebook.ipynb b/parser-comparison-notebook.ipynb index b95934c..e6baacc 100644 --- a/parser-comparison-notebook.ipynb +++ b/parser-comparison-notebook.ipynb @@ -42,7 +42,9 @@ "from camelot.__version__ import generate_version\n", "from camelot.utils import get_text_objects, text_in_bbox\n", "from camelot.parsers.stream import Stream\n", + "from camelot.parsers.lattice import Lattice\n", "from camelot.parsers.network import Network\n", + "from camelot.parsers.hybrid import Hybrid\n", "from camelot.handlers import PDFHandler\n", "from camelot.plotting import draw_pdf\n", "from tests.data import *\n", @@ -63,12 +65,12 @@ "kwargs = {}\n", "data = None\n", "# pdf_file = \"vertical_header.pdf\"\n", - "# pdf_file, kwargs, data = \"superscript.pdf\", {\"flag_size\": True}, data_stream_flag_size # test_network_flag_size\n", - "# pdf_file = \"health.pdf\" # test_network\n", + "# pdf_file, kwargs, data = \"superscript.pdf\", {\"flag_size\": True}, data_stream_flag_size # test_hybrid_flag_size\n", + "# pdf_file = \"health.pdf\" # test_hybrid\n", "# pdf_file = \"clockwise_table_2.pdf\"\n", "# pdf_file = \"tabula/12s0324.pdf\" # interesting because contains two separate tables\n", - "# pdf_file = \"clockwise_table_2.pdf\" # test_network_table_rotated / test_stream_table_rotated\n", - "# pdf_file, kwargs = \"tabula/us-007.pdf\", {\"table_regions\": [\"320,335,573,505\"]} # test_network_table_regions\n", + "# pdf_file = \"clockwise_table_2.pdf\" # test_hybrid_table_rotated / test_stream_table_rotated\n", + "# pdf_file, kwargs = \"tabula/us-007.pdf\", {\"table_regions\": [\"320,335,573,505\"]} # test_hybrid_table_regions\n", "# pdf_file, kwargs = \"detect_vertical_false.pdf\", {\"strip_text\": \" ,\\n\"} # data_stream_strip_text\n", "# pdf_file, kwargs, data = \"tabula/m27.pdf\", {\"columns\": [\"72,95,209,327,442,529,566,606,683\"], \"split_text\": True, }, data_stream_split_text # data_stream_split_text\n", "pdf_file = \"vertical_header.pdf\"\n", @@ -90,63 +92,27 @@ "metadata": {}, "outputs": [ { - "output_type": "stream", - "name": "stdout", - "text": "Showing table #0 found by stream:\n" - }, - { - "output_type": "display_data", - "data": { - "text/plain": " 0 1 2 \\\n0 Alcona Abdul El-Sayed\\n15 Shri Thanedar\\n28 \n1 Caledonia 5 15 \n2 Curtis 21 22 \n3 Greenbush 19 35 \n4 Gustin 0 10 \n5 Harrisville 19 20 \n6 Hawes 8 11 \n7 Haynes 11 9 \n8 Mikado 6 4 \n9 Millen 10 8 \n10 Mitchell 5 5 \n11 City Harrisville 13 20 \n12 Totals 132 187 \n\n 3 4 5 6 \\\n0 Gretchen Whitmer\\n80 Debbie Stabenow\\n117 Joe Weir\\n106 \n1 68 85 73 \n2 90 126 112 \n3 93 136 123 \n4 27 32 30 \n5 93 126 109 \n6 46 62 53 \n7 48 66 62 \n8 32 40 35 \n9 36 47 40 \n10 20 28 26 \n11 44 71 60 \n12 677 936 0 829 \n\n 7 8 9 10 11 12 13 14 15 \n0 Lora Greene\\n94 John E. 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0STATECONGRESSIONALLEGISLATIVECountyCounty Commissioner
1GovernorU.S. SenatorRep. 1st. Dist36th District106th DistrictRODRoad \\nComDist #1 Dist #2 Dist #3 Dist #4 Dist #5
2Abdul El-SayedShri ThanedarGretchen WhitmerDebbie StabenowJoe WeirLora GreeneJohn E. Norton, III
31528801171069417
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012345678910111213
0Abdul El-SayedShri ThanedarGretchen WhitmerDebbie StabenowJoe WeirLora GreeneJohn E. Norton, III
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2Caledonia5156885736910
3Curtis2122901261127731
4Greenbush19359313612311218
5Gustin0102732302610
6Harrisville1920931261091166
7Hawes811466253457
8Haynes119486662608
9Mikado64324035327
10Millen108364740413
11Mitchell55202826216
12City Harrisville1320447160665
13Totals132187677936082975912800000
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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n" - }, - "metadata": {}, - "execution_count": 3 + "output_type": "error", + "ename": "NameError", + "evalue": "name 'parsers' is not defined", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msuptitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Side-by-side Flavor Review'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mtables_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflavor\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparsers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 9\u001b[0m \u001b[0mtimer_before_parse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mperf_counter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0mtables\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcamelot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_pdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflavor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mflavor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdebug\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'parsers' is not defined" + ] } ], "source": [ "\n", "# Set up plots to be large enough for visualization\n", - "plt.rcParams[\"figure.figsize\"] = [26, 12]\n", - "fig, axes = plt.subplots(1, 3)\n", + "PARSERS = [\"stream\", \"lattice\", \"network\", \"hybrid\"]\n", + "PLOT_HEIGHT = 12\n", + "plt.rcParams[\"figure.figsize\"] = [PLOT_HEIGHT * len(PARSERS), PLOT_HEIGHT]\n", + "fig, axes = plt.subplots(1, len(PARSERS))\n", "fig.suptitle('Side-by-side Flavor Review')\n", "tables_list = []\n", - "for idx, flavor in enumerate([\"stream\", \"lattice\", \"network\"]):\n", + "for idx, flavor in enumerate(PARSERS):\n", " timer_before_parse = time.perf_counter()\n", " tables = camelot.read_pdf(filename, flavor=flavor, debug=True, **kwargs)\n", " tables_list.append(tables)\n", diff --git a/tests/data.py b/tests/data.py index 6723d01..c32ee28 100755 --- a/tests/data.py +++ b/tests/data.py @@ -2074,6 +2074,502 @@ data_network_vertical_headers = [ ], ] +# Compared to network, hybrid detects additional sparse columns +data_hybrid_vertical_headers = [ + [ + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "Congress-", + "", + "", + "Senator 36th", + "", + "Rep106th", + "", + "Reg. of", + "", + "Road", + "", + "", + "Distri", + "Dist", + "", + "", + "Dist", + ], + [ + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "1st Dist", + "Dist.", + "", + "", + "Dist.", + "Deeds", + "", + "Commission", + "", + "District #1", + "", + "ct #2", + "#3", + "Dist #4", + "", + "#5", + ], + [ + "", + "", + "", + "", + "", + "Governor", + "", + "", + "U.S. Senator", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + ], + [ + "", + "Number of Registered voters", + "Poll Book Totals", + "Brian Calley", + "Patrick Colbeck", + "Jim Hines", + "Bill Schuette", + "John James", + "Sandy Pensler", + "", + "Jack Bergman", + "", + "Jim Stamas", + "", + "Sue Allor", + "", + "Melissa A. Cordes", + "", + "Al Scully", + "", + "Daniel G. Gauthier", + "Craig M. Clemens", + "Craig Johnston", + "Carolyn Brummund", + "Adam Brege", + "David Bielusiak", + "", + ], + [ + "Alcona", + "963", + "439", + "55", + "26", + "47", + "164", + "173", + "111", + "", + "268", + "", + "272", + "", + "275", + "", + "269", + "", + "271", + "", + "224", + "76", + "", + "", + "", + "", + "", + ], + [ + "Caledonia", + "923", + "393", + "40", + "23", + "45", + "158", + "150", + "103", + "", + "244", + "", + "247", + "", + "254", + "", + "255", + "", + "244", + "", + "139", + "143", + "", + "", + "", + "", + "", + ], + [ + "Curtis", + "1026", + "349", + "30", + "30", + "25", + "102", + "95", + "84", + "", + "159", + "", + "164", + "", + "162", + "", + "161", + "", + "157", + "", + "", + "", + "", + "", + "", + "", + "", + ], + [ + "Greenbush", + "1212", + "423", + "56", + "26", + "40", + "126", + "104", + "131", + "", + "208", + "", + "213", + "", + "214", + "", + "215", + "", + "208", + "", + "", + "", + "", + "208", + "", + "", + "", + ], + [ + "Gustin", + "611", + "180", + "22", + "35", + "17", + "55", + "73", + "45", + "", + "108", + "", + "104", + "", + "111", + "", + "111", + "", + "109", + "", + "", + "", + "", + "", + "81", + "42", + "", + ], + [ + "Harrisville", + "1142", + "430", + "45", + "90", + "29", + "101", + "155", + "94", + "", + "226", + "", + "226", + "", + "232", + "", + "244", + "", + "226", + "", + "", + "", + "232", + "", + "", + "", + "", + ], + [ + "Hawes", + "884", + "293", + "38", + "36", + "27", + "109", + "121", + "84", + "", + "192", + "", + "195", + "", + "195", + "", + "193", + "", + "184", + "", + "", + "", + "", + "", + "118", + "87", + "", + ], + [ + "Haynes", + "626", + "275", + "31", + "20", + "32", + "104", + "121", + "53", + "", + "163", + "", + "163", + "", + "173", + "", + "161", + "", + "152", + "", + "", + "", + "76", + "", + "69", + "31", + "", + ], + [ + "Mikado", + "781", + "208", + "19", + "39", + "17", + "81", + "90", + "63", + "", + "149", + "", + "149", + "", + "145", + "", + "147", + "", + "143", + "", + "", + "", + "", + "113", + "", + "", + "", + ], + [ + "Millen", + "353", + "139", + "7", + "16", + "13", + "38", + "49", + "19", + "", + "62", + "", + "66", + "", + "67", + "", + "66", + "", + "62", + "", + "", + "", + "", + "", + "", + "", + "", + ], + [ + "Mitchell", + "327", + "96", + "12", + "17", + "7", + "29", + "41", + "17", + "", + "57", + "", + "55", + "", + "57", + "", + "60", + "", + "56", + "", + "", + "", + "", + "", + "", + "", + "", + ], + [ + "City Harrisville", + "389", + "171", + "16", + "15", + "18", + "35", + "49", + "31", + "", + "78", + "", + "80", + "", + "82", + "", + "81", + "", + "77", + "", + "", + "", + "73", + "", + "", + "", + "", + ], + [ + "Totals", + "9237", + "3396", + "371", + "373", + "317", + "1102", + "1221", + "835", + "0", + "1914", + "0", + "1934", + "", + "1967", + "", + "1963", + "0", + "1889", + "0", + "363", + "219", + "381", + "321", + "268", + "160", + "0", + ], +] data_stream_table_areas = [ diff --git a/tests/files/baseline_plots/test_joint_plot.png b/tests/files/baseline_plots/test_joint_plot.png index 9f98d68..48c8da9 100644 Binary files a/tests/files/baseline_plots/test_joint_plot.png and b/tests/files/baseline_plots/test_joint_plot.png differ diff --git a/tests/test_common.py b/tests/test_common.py index 55dd1f3..a429d1c 100644 --- a/tests/test_common.py +++ b/tests/test_common.py @@ -291,6 +291,19 @@ def test_network_layout_kwargs(): assert_frame_equal(df, tables[0].df) +# Hybrid parser +def test_hybrid_vertical_header(): + """Tests a complex table with a vertically text header. + """ + df = pd.DataFrame(data_hybrid_vertical_headers) + + filename = os.path.join(testdir, "vertical_header.pdf") + tables = camelot.read_pdf(filename, flavor="hybrid") + assert len(tables) == 1 + assert_frame_equal(df, tables[0].df) + + +# Lattice parser tests def test_lattice(): df = pd.DataFrame(data_lattice)