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PDF spec parser

pdfParser.py

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+import PyPDF2
+import camelot
+
+
+vendor = 'jcrew'
+pdf_filename = '馬克資料/19S06123+19S06124-0.430.2019.pdf'
+pdf_file = open(pdf_filename, 'rb')
+clothes_type = 'Top'
+
+top_keys = {'BODY_FRONT': 'Front Body Length',
+            'BODY_BACK': 'Back Body Length',
+            'CHEST': 'Chest Circumference',
+            'SWEEP': 'Sweep Circumference',
+            'SLEEVE_LEN': 'CB Neck to Sleeve',
+            'MUSCLE': 'Muscle Circumference',
+            'CUFF_OPEN': 'Sleeve Opening Circumference',
+            'CUFF_HEIGHT': 'Sleeve Cuff Heigh',
+            'NECK_WIDTH': 'Neck Width at HPS',
+            'COLLAR_HEIGHT': 'Collar Height at CB',
+            'COLLAR_LEN': 'Collar Length'}
+
+
+def get_key_values(keys, total_string_dictionary, key_paegs, base_size, key_start_index, key_record_string):
+    output_keys = {}
+    for key, values in keys.items():
+        for i in range(len(key_paegs)):
+            dfs = camelot.read_pdf(pdf_filename, pages=str(key_paegs[i] + 1))
+            now_df = dfs[0].df
+            # get start index
+            start_index = now_df[now_df[0].str.contains(key_start_index) == True][0].index[0]
+            new_df = now_df.loc[start_index + 1:, :]
+            new_df.columns = [now_df.loc[start_index, :].values]
+
+            # fix \n strings
+            new_df = new_df.replace(r'\n',  ' ', regex=True)
+
+            # compare key with csv
+            row_index = None
+            for index in new_df.index:
+                row = new_df.loc[index, key_record_string].str.contains(values)
+                if row[-1]:
+                    row_index = index
+                    break
+            # print('row_index: {}'.format(row_index))
+            if row_index != None:
+                value = new_df.loc[row_index, base_size][-1]
+                value = value.split(' ')
+                if len(value) == 2:
+                    value[-1] = float(value[-1].split('/')[0]) / float(value[-1].split('/')[1])
+                    final_value = float(value[0]) + value[-1]
+                else:
+                    final_value = float(value[0])
+                # print('value: {}'.format(final_value))
+                output_keys[key] = final_value
+                break
+    return output_keys
+
+
+def get_base_size(key_base_size, key_page_string):
+    key_loc = key_page_string.find(key_base_size)
+    # Base Size:XXXS, XXS, XS, S, M, L, XL, XXL, 2X, 3X
+    base_size = key_page_string[key_loc + len(key_base_size) + 1: key_loc + len(key_base_size) + 5]
+    base_size = base_size.replace(' ', '')
+    return base_size
+
+
+def get_key_page(key_page_name, total_string_dictionary):
+    pages = []
+    for page, string in total_string_dictionary.items():
+        if key_page_name in string:
+            pages.append(int(page))
+    return pages
+
+
+def main():
+    total_string_dictionary = {}
+    read_pdf = PyPDF2.PdfFileReader(pdf_file)
+    number_of_pages = read_pdf.getNumPages()
+    print('number of pages {}'.format(number_of_pages))
+    # get type keys
+    keys = {}
+    key_start_index = ''
+    key_page_name = ''
+    key_base_size = ''
+    key_record_string = ''
+    if vendor == 'jcrew':
+        key_start_index = 'Section POM #'
+        key_page_name = 'Graded Measurement'
+        key_base_size = 'Sample Size'
+        key_record_string = 'POM Name'
+
+    if clothes_type == 'Top':
+        keys = top_keys
+
+    # extract all string in pdf
+    for i in range(number_of_pages):
+        page = read_pdf.getPage(i)
+        page_content = page.extractText()
+        extrast_string = page_content.encode('utf-8')
+        total_string_dictionary[i] = str(extrast_string)
+
+    # get data pages
+    key_paegs = get_key_page(key_page_name, total_string_dictionary)
+    print('key_paegs: {}'.format(key_paegs))
+    # print(total_string_dictionary[key_paegs[0]])
+
+    # get Bas_ Size
+    base_size = get_base_size(key_base_size, total_string_dictionary[key_paegs[0]])
+    print('base_size: {}'.format(base_size))
+
+    # use dataframe to get key values
+    output_key_values = get_key_values(keys, total_string_dictionary, key_paegs, base_size, key_start_index,
+                                       key_record_string)
+    print(output_key_values)
+    # now_df.to_csv('01_1.csv')
+
+
+if __name__ == "__main__":
+    main()