utility scripts

dataframe.py

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+import os
+import pandas as pd
+from PIL import Image
+from io import BytesIO
+import io
+
+
+# Path to the folders containing images and captions
+image_folder = "images"
+ocr_folder = "ocr-text"
+
+# List to store data
+data = []
+count = 0
+
+# Iterate through images and captions
+for index, image_file in enumerate(os.listdir(image_folder)):
+    if index >= 1000:
+        break
+    count += 1
+    print("adding image: " + image_file + " to dataframe" + " count: " + str(count))
+    image_path = os.path.join(image_folder, image_file)
+    
+    # Assuming caption file names match image file names
+    ocr_path = os.path.join(ocr_folder, image_file.replace(".jpg", "_words.txt"))
+
+    # Read image and get image dimensions
+    image = Image.open(image_path)
+    image_width, image_height = image.size
+
+    # Read caption
+    with open(ocr_path, "r", encoding="utf-8") as ocr_file:
+        caption = ocr_file.read().strip()
+
+    # Convert image to byte array
+    img = Image.open(image_path)
+    img_byte_arr = io.BytesIO()
+    img.save(img_byte_arr, format=img.format)
+    img_byte_arr = img_byte_arr.getvalue()
+
+    # Append data to the list
+    data.append({
+        'image': img_byte_arr,
+        'ocr_annotation_texts': caption,
+        'image_height': image_height,
+        'image_width': image_width
+    })
+
+# Create DataFrame
+df = pd.DataFrame(data)
+
+current_directory = os.getcwd()
+
+parquet_file_name = "my_dataframe.parquet"
+
+# Combine the current directory and the filename to create the full path
+parquet_file_path = os.path.join(current_directory, parquet_file_name)
+df.to_parquet(parquet_file_path, index=False)
+
+# Display the DataFrame
+print(df)

json-to-annotation.py

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+import os
+import json
+
+input_folder = "words"
+output_folder = "ocr-text"
+
+json_files = [file for file in os.listdir(input_folder) if file.endswith('.json')]
+
+for json_file in json_files:
+    print(f'Processing {json_file}')
+    input_file_path = os.path.join(input_folder, json_file)
+    output_file_path = os.path.join(output_folder, os.path.splitext(json_file)[0] + '.txt')
+
+    with open(input_file_path, 'r') as file:
+        data = json.load(file)
+
+    words_list = data.get("words", [])
+
+    #calculate width and height of the image
+    width = data["image_rect"][2]
+    height = data["image_rect"][3]
+
+    result_strings = []
+    for word in words_list:
+        bbox_values = word["bbox"]
+
+        #calculate (x,y,w,h)
+        x =  int((bbox_values[0])*100/width)
+        y =  int((bbox_values[1])*100/height)
+        box_width =  int((abs(bbox_values[2]-bbox_values[0]))*100/width)
+        box_height =  int((abs(bbox_values[3]-bbox_values[1]))*100/height)
+        #convert everything to a string with appropriate spacing and text at the end
+        result_string = f"{x} {y} {box_width} {box_height} {word['text']}"
+        result_strings.append(result_string)
+
+    result_string = ' '.join(result_strings)
+
+    with open(output_file_path, 'w', encoding='utf-8') as file:
+        file.write(result_string)