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- import numpy as np
- from PIL import Image
- from anonymizer.utils import Box
- from anonymizer.detection import Detector
- from anonymizer.detection import download_weights, get_weights_path
- def box_covers_box(covering_box: Box, covered_box: Box):
- return (covered_box.x_min > covering_box.x_min and covered_box.y_min > covering_box.y_min and
- covered_box.x_max < covering_box.x_max and covered_box.y_max < covering_box.y_max)
- def load_np_image(image_path):
- image = Image.open(image_path).convert('RGB')
- np_image = np.array(image)
- return np_image
- class TestDetector:
- @staticmethod
- def test_it_detects_obvious_faces(tmp_path):
- weights_directory = tmp_path / 'weights'
- face_weights_path = get_weights_path(weights_directory, kind='face')
- download_weights(weights_directory)
- detector = Detector(kind='face', weights_path=face_weights_path)
- np_image = load_np_image('./test/detection/face_test_image.jpg')
- left_face = Box(x_min=267, y_min=64, x_max=311, y_max=184, score=0.0, kind='face')
- right_face = Box(x_min=369, y_min=68, x_max=420, y_max=152, score=0.0, kind='face')
- boxes = detector.detect(np_image, detection_threshold=0.2)
- assert len(boxes) >= 2
- for box in boxes:
- assert box.score >= 0.2
- assert boxes[0].score >= 0.5 and boxes[1].score >= 0.5
- assert ((box_covers_box(boxes[0], left_face) and box_covers_box(boxes[1], right_face)) or
- (box_covers_box(boxes[1], left_face) and box_covers_box(boxes[0], right_face)))
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