diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..f076b37 --- /dev/null +++ b/.gitignore @@ -0,0 +1,47 @@ +# See https://help.github.com/articles/ignoring-files/ for more about ignoring files. + +# dependencies +/node_modules +/.pnp +.pnp.js + +# testing +/coverage + +# next.js +/.next/ +/out/ + +# production +/build + +# misc +.DS_Store +*.pem +db.sqlite3 + +# debug +npm-debug.log* +yarn-debug.log* +yarn-error.log* + +# local env files +.env*.local +/api/gl_config.json +/api/*.json +/api/external + +# vercel +.vercel + +# typescript +*.tsbuildinfo +next-env.d.ts + +# python +__pycache__ + +# vim +*.swp + +.env \ No newline at end of file diff --git a/README.md b/README.md index 66c584c..bdab091 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,79 @@ -# model-evaluation-tool -A simple tool for evaluating the performance of your Groundlight ML model +# Model Evaluation Tool +A simple tool for evaluating the performance of your Groundlight Binary ML model + +## Installation + +The dependencies for this script can be installed through either using poetry (recommended) or `requirements.txt`. + +Using poetry + +```bash +poetry install +``` + +Using `requirements.txt` +```bash +pip install -r requirements.txt +``` + +## Usage + +### Setting Up Your Account + +To train a ML model, make sure to create a binary detector on the [Online Dashboard](https://dashboard.groundlight.ai/). + +You will also need to create an API Token to start uploading images to the account. You can go [here](https://dashboard.groundlight.ai/reef/my-account/api-tokens) to create one. + +After you have created your API token, add the token to your terminal as an variable: + +```bash +export GROUNDLIIGHT_API_TOKEN="YOUR_API_TOKEN" +``` + +### Formatting Dataset + +To train or evaluate the ML model with your custom dataset, structure your dataset into the following format: + +```bash +└── dataset + ├── dataset.csv + └── images + ├── 1.jpg + ├── 10.jpg + ├── 11.jpg + ├── 12.jpg + ├── 13.jpg + ├── 14.jpg +``` + +The `dataset.csv` file should have two columns: image_name and label (YES/NO), for example: + +```bash +1.jpg,YES +11.jpg,NO +12.jpg,YES +13.jpg,YES +14.jpg,NO +``` + +The corresponding image file should be placed inside the `images` folder. + +### Training the Detector + +To train the ML model for a detector, simply run the script `label-upload.py` with the following arguments: + +```bash +poetry run python label-upload.py --detector-id YOUR_DETECTOR_ID --dataset PATH_TO_DATASET_TRAIN_FOLDER +``` + +Optionally, set the `--delay` argument to prevent going over the throttling limit of your account. + +### Evaluate the Detector + +To evaluate the ML model performance for a detector, simply run the script `evaluate-accuracy.py` with the following arguments: + +```bash +poetry run python evaluate-accuracy.py --detector-id YOUR_DETECTOR_ID --dataset PATH_TO_DATASET_TEST_FOLDER +``` + +Optionally, set the `--delay` argument to prevent going over the throttling limit of your account. \ No newline at end of file diff --git a/evaluate-accuracy.py b/evaluate-accuracy.py new file mode 100644 index 0000000..032b215 --- /dev/null +++ b/evaluate-accuracy.py @@ -0,0 +1,106 @@ +#!/usr/bin/env python3 +""" +A script to evaluate the accuracy of a detector on a given dataset. +It will upload the images to the detector and compare the predicted labels with the ground truth labels. +You can specify the delay between uploads. +""" + +import argparse +import os +import PIL +import time +import PIL.Image +import pandas as pd +import logging + +from groundlight import Groundlight, Detector, BinaryClassificationResult +from tqdm.auto import tqdm + +logger = logging.getLogger(__name__) +logging.basicConfig(level=logging.INFO) + + +def upload_image(gl: Groundlight, detector: Detector, image: PIL) -> BinaryClassificationResult: + """ + Upload a image with a label to a detector. + + Args: + gl: The Groundlight object. + detector: The detector to upload to. + image: The image to upload. + Returns: + The predicted label (YES/NO). + """ + + # Convert image to jpg if not already + if image.format != "JPEG": + image = image.convert("RGB") + + # Use ask_ml to upload the image and then add the label to the image query + iq = gl.ask_ml(detector=detector, image=image) + return iq.result + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Evaluate the accuracy of a detector on a given dataset.") + parser.add_argument("--detector-id", type=str, required=True, help="The ID of the detector to evaluate.") + parser.add_argument("--dataset", type=str, required=True, help="The folder containing the dataset.csv and images folder") + parser.add_argument("--delay", type=float, required=False, default=0.1, help="The delay between uploads.") + args = parser.parse_args() + + gl = Groundlight() + detector = gl.get_detector(args.detector_id) + + # Load the dataset from the CSV file and images from the images folder + # The CSV file should have two columns: image_name and label (YES/NO) + + dataset = pd.read_csv(os.path.join(args.dataset, "dataset.csv")) + images = os.listdir(os.path.join(args.dataset, "images")) + + logger.info(f"Evaluating {len(dataset)} images on detector {detector.name} with delay {args.delay}.") + + # Record the number of correct predictions + # Also record the number of false positives and false negatives + correct = 0 + total_processed = 0 + false_positives = 0 + false_negatives = 0 + average_confidence = 0 + + for image_name, label in tqdm(dataset.values): + if image_name not in images: + logger.warning(f"Image {image_name} not found in images folder.") + continue + + if label not in ["YES", "NO"]: + logger.warning(f"Invalid label {label} for image {image_name}. Skipping.") + continue + + image = PIL.Image.open(os.path.join(args.dataset, "images", image_name)) + result = upload_image(gl=gl, detector=detector, image=image) + + if result.label == label: + correct += 1 + elif result.label == "YES" and label == "NO": + false_positives += 1 + elif result.label == "NO" and label == "YES": + false_negatives += 1 + + average_confidence += result.confidence + total_processed += 1 + + time.sleep(args.delay) + + # Calculate the accuracy, precision, and recall + accuracy = correct / total_processed if total_processed > 0 else 0 + precision = correct / (correct + false_positives) if correct + false_positives > 0 else 0 + recall = correct / (correct + false_negatives) if correct + false_negatives > 0 else 0 + + logger.info(f"Processed {total_processed} images.") + logger.info(f"Correct: {correct}/{total_processed}") + logger.info(f"Average Confidence: {average_confidence / total_processed:.2f}") + logger.info(f"False Positives: {false_positives}") + logger.info(f"False Negatives: {false_negatives}") + logger.info(f"Accuracy: {accuracy:.2f}") + logger.info(f"Precision: {precision:.2f}") + logger.info(f"Recall: {recall:.2f}") diff --git a/label-upload.py b/label-upload.py new file mode 100644 index 0000000..da51fbd --- /dev/null +++ b/label-upload.py @@ -0,0 +1,72 @@ +#!/usr/bin/env python3 +""" +A script to upload frames with labels to a detector in a controlled manner. +You can specify the delay between uploads. +""" + +import argparse +import os +import PIL +import time +import PIL.Image +import pandas as pd +import logging + +from groundlight import Groundlight, Detector +from tqdm.auto import tqdm + +logger = logging.getLogger(__name__) +logging.basicConfig(level=logging.INFO) + + +def upload_image(gl: Groundlight, detector: Detector, image: PIL, label: str) -> None: + """ + Upload a image with a label to a detector. + + Args: + gl: The Groundlight object. + detector: The detector to upload to. + image: The image to upload. + label: The label to upload. + """ + + # Convert image to jpg if not already + if image.format != "JPEG": + image = image.convert("RGB") + + if label not in ["YES", "NO"]: + raise ValueError(f"Invalid label: {label}, must be 'YES' or 'NO'.") + + # Use ask_ml to upload the image and then add the label to the image query + iq = gl.ask_ml(detector=detector, image=image) + gl.add_label(image_query=iq, label=label) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Upload images with labels to a detector.") + parser.add_argument("--detector-id", type=str, required=True, help="The ID of the detector to upload to.") + parser.add_argument("--dataset", type=str, required=True, help="The folder containing the dataset.csv and images folder") + parser.add_argument("--delay", type=float, required=False, default=0.1, help="The delay between uploads.") + args = parser.parse_args() + + gl = Groundlight() + detector = gl.get_detector(args.detector_id) + + # Load the dataset from the CSV file and images from the images folder + # The CSV file should have two columns: image_name and label (YES/NO) + + dataset = pd.read_csv(os.path.join(args.dataset, "dataset.csv")) + images = os.listdir(os.path.join(args.dataset, "images")) + + logger.info(f"Uploading {len(dataset)} images to detector {detector.name} with delay {args.delay}.") + + for image_name, label in tqdm(dataset.values): + if image_name not in images: + logger.warning(f"Image {image_name} not found in images folder.") + continue + + image = PIL.Image.open(os.path.join(args.dataset, "images", image_name)) + upload_image(gl=gl, detector=detector, image=image, label=label) + time.sleep(args.delay) + + logger.info("Upload complete. 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