Difference between revisions of "Artificial Intelligence/Robotoff/Adding new detections"
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+ | [[Category:Robotoff]] | ||
== Finding opportunities == | == Finding opportunities == | ||
+ | === Option 1 === | ||
# Download the OCR JSONL | # Download the OCR JSONL | ||
− | # <pre> | + | # <pre>zgrep -n '05.01.07' FILE | wc -l</pre> |
+ | |||
+ | === Option 2 === | ||
+ | # Right click on an image in a product page, in edit mode (eg 1.jpg) | ||
+ | # Replace `.jpg` by `.json` | ||
+ | # Find patterns or `logoAnnotations` in the JSON | ||
+ | |||
== Adding detections using REGEXes == | == Adding detections using REGEXes == | ||
https://github.com/openfoodfacts/robotoff/tree/master/robotoff/insights/ocr | https://github.com/openfoodfacts/robotoff/tree/master/robotoff/insights/ocr |
Latest revision as of 14:27, 23 February 2023
Finding opportunities
Option 1
- Download the OCR JSONL
zgrep -n '05.01.07' FILE | wc -l
Option 2
- Right click on an image in a product page, in edit mode (eg 1.jpg)
- Replace `.jpg` by `.json`
- Find patterns or `logoAnnotations` in the JSON
Adding detections using REGEXes
https://github.com/openfoodfacts/robotoff/tree/master/robotoff/insights/ocr
Add a function to add the desired match
- Choose the relevant file within the insights/ocr directory (in this case: https://github.com/openfoodfacts/robotoff/blob/master/robotoff/insights/ocr/packager_code.py)
- Write the function
def process_fsc_match(match) -> str: fsc_code = match.group(1) return "FSC-{}".format(fsc_code).upper()
Add the function to the processing pipeline to plug it to Robotoff
This happens in the same file.
PACKAGER_CODE: Dict[str, OCRRegex] = { "fr_emb": OCRRegex( re.compile(r"emb ?(\d ?\d ?\d ?\d ?\d) ?([a-z])?(?![a-z0-9])"), field=OCRField.text_annotations, lowercase=True, processing_func=process_fr_emb_match, ), "fsc": OCRRegex( re.compile(r"fsc.? ?(c\d{6})"), field=OCRField.text_annotations, lowercase=True, processing_func=process_fsc_match, ), "eu_fr": OCRRegex( re.compile(
Grab yourself a sandwich
Adding detections using Flashtext
https://github.com/openfoodfacts/robotoff/tree/master/data/ocr
Expanding existing detections
- Choose a file matching the detection you want to add
In this example: https://github.com/openfoodfacts/robotoff/blob/master/data/ocr/brand_logo_annotation.txt
- Add entries one per line. The raw `logoAnnotation` you find in the OCR JSON is on the left, the value you want to add in the brand field is on the right.
alnatura||Alnatura Andros France||Andros andros||Andros
Add a new type of detection
- Create a file `fishing_flashtext.txt` and add one value to extract per line
Add it to the Robotoff settings
robotoff/settings.py OCR_LABEL_WHITELIST_DATA_PATH = OCR_DATA_DIR / "label_whitelist.txt" OCR_FISHING_FLASHTEXT_DATA_PATH = OCR_DATA_DIR / "fishing_flashtext.txt"
Plug it into the OCR pipeline
robotoff/insights/ocr/packager_code.py codes = text_file_iter(settings.OCR_FISHING_FLASHTEXT_DATA_PATH) return generate_keyword_processor(("{}||{}".format(c.upper(), c) for c in codes))