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| import json | |
| import os.path | |
| from PIL import Image | |
| import tqdm | |
| from sacred import Experiment | |
| from engie_pipeline.pipeline import run, pipeline, set_up_pipeline, pipeline_experiment | |
| eval_experiment = Experiment('eval', ingredients=[pipeline_experiment]) | |
| def config(): | |
| label_path = "/Users/benoit/Projects/EngieTableauElectrique/data/dataset/test.json" | |
| image_folder = "data/test_data" | |
| conformity_threshold = 0.9 | |
| def intersection_over_union(boxA, boxB): | |
| # determine the (x, y)-coordinates of the intersection rectangle | |
| xA = max(boxA[0], boxB[0]) | |
| yA = max(boxA[1], boxB[1]) | |
| xB = min(boxA[2], boxB[2]) | |
| yB = min(boxA[3], boxB[3]) | |
| # compute the area of intersection rectangle | |
| interArea = max(0, xB - xA) * max(0, yB - yA) | |
| # compute the area of both the prediction and ground-truth | |
| # rectangles | |
| boxAArea = (boxA[2] - boxA[0] + 1) * (boxA[3] - boxA[1] + 1) | |
| boxBArea = (boxB[2] - boxB[0] + 1) * (boxB[3] - boxB[1] + 1) | |
| # compute the intersection over union by taking the intersection | |
| # area and dividing it by the sum of prediction + ground-truth | |
| # areas - the interesection area | |
| iou = interArea / float(boxAArea + boxBArea - interArea) | |
| # return the intersection over union value | |
| return iou | |
| def evaluate(label_path, image_folder, conformity_threshold): | |
| models = set_up_pipeline() | |
| confusion_matrix = { | |
| "tp": 0, | |
| "fp": 0, | |
| "fn": 0, | |
| } | |
| with open(label_path) as file: | |
| test_data = json.load(file) | |
| for image in tqdm.tqdm(test_data['images']): | |
| _, boxes, labels, scores, _ = pipeline(image=Image.open(os.path.join(image_folder, image['filename'])), **models, force_detr=True) | |
| annotations = [annotation for annotation in test_data['annotations'] if annotation['image_id'] == image['id'] and annotation['category_id'] == 2] | |
| boxes = boxes[labels == 2] | |
| scores = scores[labels == 2] | |
| boxes = boxes[scores >= conformity_threshold] | |
| bbox_useful = [False for _ in boxes] | |
| for annotation in annotations: | |
| x, y, w, h = annotation['bbox'] | |
| is_detected = False | |
| for bbox_id, box in enumerate(boxes): | |
| if intersection_over_union(box, [x, y, x+w, y+h]) > 0: | |
| confusion_matrix['tp'] += 1 | |
| is_detected = True | |
| bbox_useful[bbox_id] = True | |
| break | |
| if not is_detected: | |
| confusion_matrix['fn'] += 1 | |
| for bbox_id, box in enumerate(boxes): | |
| is_useful = False | |
| for annotation in annotations: | |
| x, y, w, h = annotation['bbox'] | |
| if intersection_over_union(box, [x, y, x + w, y + h]) > 0: | |
| is_useful = True | |
| break | |
| if not is_useful: | |
| confusion_matrix['fp'] += 1 | |
| return confusion_matrix | |