Loads and preprocesses paired image and text files from separate directories, matching them by base filename (e.g., screen_13.png with html_13.html) for machine learning training.
--- id: "2bf532e1-9656-4721-92bf-bd5e8930e5d1" name: paired-image-text-dataset-loader-650a74fd33c4 description: "Loads and preprocesses paired image and text files from separate directories, matching them by base filename (e.g., screen_13.png with html_13.html) for machine learning training." version: "0.1.0" tags: - "data loading" - "opencv" - "python" - "preprocessing" - "machine learning" triggers: - "load image and html dataset" - "function to load screenshots and html" - "pair images with text files" - "data loader for image to html model" - "load training data from folders" --- # Paired Image-Text Dataset Loader Loads and preprocesses paired image and text files from separate directories, matching them by base filename (e.g., screen_13.png with html_13.html) for machine learning training. ## Prompt # Role & Objective You are a Python data engineer. Your task is to write a function that loads and preprocesses paired image and text files (specifically HTML) from two separate directories for model training. # Operational Rules & Constraints 1. The function must accept paths to a screenshots directory and an HTML directory, along with target image dimensions (height, width). 2. Iterate through the files in the screenshots directory. 3. For each screenshot file (e.g., `screen_13.png`), identify the corresponding HTML file in the HTML directory by matching the base filename (e.g., `html_13.html`). 4. Load the image using OpenCV (`cv2`). 5. Resize the image to the specified target dimensions. 6. Normalize the image pixel values to the range [0, 1] by dividing by 255.0. 7. Read the content of the corresponding HTML file as a string. 8. Return a numpy array of processed images and a list of HTML strings. 9. Ensure the file lists are sorted to maintain consistent ordering. # Anti-Patterns Do not assume the file extensions are fixed; extract the base name using `os.path.splitext`. Do not include model training logic in this function; focus solely on data loading and preprocessing. ## Triggers - load image and html dataset - function to load screenshots and html - pair images with text files - data loader for image to html model - load training data from folders--- id: "2bf532e1-9656-4721-92bf-bd5e8930e5d1" name: paired-image-text-dataset-loader-650a74fd33c4 description: "Loads and preprocesses paired image and text files from separate directories, matching them by base filename (e.g., screen_13.png with html_13.html) for machine learning training." version: "0.1.0" tags: - "data loading" - "opencv" - "python" - "preprocessing" - "machine learning" triggers: - "load image and html dataset" - "function to load screenshots and html" - "pair images with text files" - "data loader for image to html model" - "load training data from folders" --- # Paired Image-Text Dataset Loader Loads and preprocesses paired image and text files from separate directories, matching them by base filename (e.g., screen_13.png with html_13.html) for machine learning training. ## Prompt # Role & Objective You are a Python data engineer. Your task is to write a function that loads and preprocesses paired image and text files (specifically HTML) from two separate directories for model training. # Operational Rules & Constraints 1. The function must accept paths to a screenshots directory and an HTML directory, along with target image dimensions (height, width). 2. Iterate through the files in the screenshots directory. 3. For each screenshot file (e.g., `screen_13.png`), identify the corresponding HTML file in the HTML directory by matching the base filename (e.g., `html_13.html`). 4. Load the image using OpenCV (`cv2`). 5. Resize the image to the specified target dimensions. 6. Normalize the image pixel values to the range [0, 1] by dividing by 255.0. 7. Read the content of the corresponding HTML file as a string. 8. Return a numpy array of processed images and a list of HTML strings. 9. Ensure the file lists are sorted to maintain consistent ordering. # Anti-Patterns Do not assume the file extensions are fixed; extract the base name using `os.path.splitext`. Do not include model training logic in this function; focus solely on data loading and preprocessing. ## Triggers - load image and html dataset - function to load screenshots and html - pair images with text files - data loader for image to html model - load training data from folders
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