import pytest import requests import requests_mock import simplehound.core as hound MOCK_API_KEY = "mock_api_key" MOCK_BYTES = b"Test" B64_ENCODED_MOCK_BYTES = "VGVzdA==" URL_DETECTIONS_DEV = hound.URL_DETECTIONS_BASE.format("dev") URL_DETECTIONS_PROD = hound.URL_DETECTIONS_BASE.format("prod") URL_RECOGNITIONS_DEV = hound.URL_RECOGNITIONS_BASE.format("dev") URL_RECOGNITIONS_PROD = hound.URL_RECOGNITIONS_BASE.format("prod") ## RAW API RESPONSES DETECTIONS = { "image": {"width": 960, "height": 480, "orientation": 1}, "objects": [ { "type": "face", "boundingBox": {"x": 305, "y": 151, "height": 28, "width": 30}, "attributes": { "gender": "male", "genderConfidence": 0.9733, "age": 33, "ageConfidence": 0.7801, "frontal": True, }, }, { "type": "face", "boundingBox": {"x": 855, "y": 147, "height": 29, "width": 24}, "attributes": { "gender": "male", "genderConfidence": 0.9834, "age": 37, "ageConfidence": 0.5096, "frontal": False, }, }, { "type": "person", "boundingBox": {"x": 227, "y": 133, "height": 245, "width": 125}, }, { "type": "person", "boundingBox": {"x": 833, "y": 137, "height": 268, "width": 93}, }, ], "requestId": "467f195c4bbf46c69f964b59884dee04", } RECOGNITIONS_LICENSEPLATE = { "image": {"width": 960, "height": 480, "orientation": 1}, "objects": [ { "objectType": "licenseplate", "licenseplateAnnotation": { "bounding": { "vertices": [ {"x": 494, "y": 294}, {"x": 542, "y": 294}, {"x": 542, "y": 318}, {"x": 494, "y": 318}, ] }, "attributes": { "system": { "string": {"name": "7XJT316", "confidence": 0.116}, "characters": [ { "bounding": { "vertices": [ {"y": 303, "x": 502}, {"y": 303, "x": 507}, {"y": 313, "x": 507}, {"y": 313, "x": 502}, ] }, "index": 0, "confidence": 0.9186, "character": "7", }, { "bounding": { "vertices": [ {"y": 302, "x": 507}, {"y": 302, "x": 512}, {"y": 313, "x": 512}, {"y": 313, "x": 507}, ] }, "index": 1, "confidence": 0.8351, "character": "X", }, { "bounding": { "vertices": [ {"y": 302, "x": 511}, {"y": 302, "x": 516}, {"y": 313, "x": 516}, {"y": 313, "x": 511}, ] }, "index": 2, "confidence": 0.8033, "character": "J", }, { "bounding": { "vertices": [ {"y": 302, "x": 516}, {"y": 302, "x": 521}, {"y": 313, "x": 521}, {"y": 313, "x": 516}, ] }, "index": 3, "confidence": 0.8243, "character": "T", }, { "bounding": { "vertices": [ {"y": 303, "x": 521}, {"y": 303, "x": 526}, {"y": 313, "x": 526}, {"y": 313, "x": 521}, ] }, "index": 4, "confidence": 0.8527, "character": "3", }, { "bounding": { "vertices": [ {"y": 302, "x": 525}, {"y": 302, "x": 530}, {"y": 313, "x": 530}, {"y": 313, "x": 525}, ] }, "index": 5, "confidence": 0.3805, "character": "1", }, { "bounding": { "vertices": [ {"y": 302, "x": 530}, {"y": 302, "x": 535}, {"y": 313, "x": 535}, {"y": 313, "x": 530}, ] }, "index": 6, "confidence": 0.7038, "character": "6", }, ], "region": {"name": "California", "confidence": 0.9918}, } }, }, } ], "requestId": "467f195c4bbf46c69f964b59884dee04", } RECOGNITIONS_VEHICLES = { "image": {"width": 1080, "height": 675, "orientation": 1}, "requestId": "7b09bdf1547441c78fcd336ac1b78077", "objects": [ { "objectId": "_vehicle_c3b12324-1f19-4606-90c6-39c25c8c39fb", "vehicleAnnotation": { "bounding": { "vertices": [ {"x": 289, "y": 150}, {"x": 1036, "y": 150}, {"x": 1036, "y": 602}, {"x": 289, "y": 602}, ] }, "recognitionConfidence": 0.8554, "attributes": { "system": { "make": {"name": "Ford", "confidence": 0.8554}, "model": {"name": "Ranger", "confidence": 0.8554}, "color": {"name": "black", "confidence": 0.9988}, "vehicleType": "car", } }, }, "objectType": "vehicle", } ], } RECOGNITIONS_ALL = { "image": {"width": 1080, "height": 675, "orientation": 1}, "requestId": "a14d1d7e426a429d960fa100d2351cdb", "objects": [ { "objectId": "_vehicle_c3b12324-1f19-4606-90c6-39c25c8c39fb", "vehicleAnnotation": { "bounding": { "vertices": [ {"x": 289, "y": 150}, {"x": 1036, "y": 150}, {"x": 1036, "y": 602}, {"x": 289, "y": 602}, ] }, "recognitionConfidence": 0.8554, "licenseplate": { "bounding": { "vertices": [ {"x": 755, "y": 377}, {"x": 914, "y": 377}, {"x": 914, "y": 419}, {"x": 755, "y": 419}, ] }, "attributes": { "system": { "string": {"name": "CV67CBU", "confidence": 0.4044}, "characters": [ { "bounding": { "vertices": [ {"y": 385, "x": 778}, {"y": 385, "x": 794}, {"y": 413, "x": 794}, {"y": 413, "x": 778}, ] }, "index": 0, "confidence": 0.9797, "character": "C", }, { "bounding": { "vertices": [ {"y": 384, "x": 796}, {"y": 384, "x": 812}, {"y": 412, "x": 812}, {"y": 412, "x": 796}, ] }, "index": 1, "confidence": 0.985, "character": "V", }, { "bounding": { "vertices": [ {"y": 384, "x": 812}, {"y": 384, "x": 829}, {"y": 412, "x": 829}, {"y": 412, "x": 812}, ] }, "index": 2, "confidence": 0.4732, "character": "6", }, { "bounding": { "vertices": [ {"y": 383, "x": 830}, {"y": 383, "x": 846}, {"y": 411, "x": 846}, {"y": 411, "x": 830}, ] }, "index": 3, "confidence": 0.9895, "character": "7", }, { "bounding": { "vertices": [ {"y": 383, "x": 853}, {"y": 383, "x": 869}, {"y": 411, "x": 869}, {"y": 411, "x": 853}, ] }, "index": 4, "confidence": 0.998, "character": "C", }, { "bounding": { "vertices": [ {"y": 382, "x": 869}, {"y": 382, "x": 886}, {"y": 410, "x": 886}, {"y": 410, "x": 869}, ] }, "index": 5, "confidence": 0.9933, "character": "B", }, { "bounding": { "vertices": [ {"y": 381, "x": 887}, {"y": 381, "x": 903}, {"y": 410, "x": 903}, {"y": 410, "x": 887}, ] }, "index": 6, "confidence": 0.9026, "character": "U", }, ], "region": {"name": "UK", "confidence": 0.9972}, } }, }, "attributes": { "system": { "make": {"name": "Ford", "confidence": 0.8554}, "model": {"name": "Ranger", "confidence": 0.8554}, "color": {"name": "black", "confidence": 0.9988}, "vehicleType": "car", } }, }, "objectType": "vehicle", } ], } ## Processed responses FACES = [ { "boundingBox": {"x": 305, "y": 151, "height": 28, "width": 30}, "gender": "male", "age": 33, }, { "boundingBox": {"x": 855, "y": 147, "height": 29, "width": 24}, "gender": "male", "age": 37, }, ] PEOPLE = [ {"boundingBox": {"x": 227, "y": 133, "height": 245, "width": 125}}, {"boundingBox": {"x": 833, "y": 137, "height": 268, "width": 93}}, ] METADATA = { "image_width": 960, "image_height": 480, "requestId": "467f195c4bbf46c69f964b59884dee04", } LICENSEPLATE_PROCESSED = [ { "boundingBox": { "vertices": [ {"x": 494, "y": 294}, {"x": 542, "y": 294}, {"x": 542, "y": 318}, {"x": 494, "y": 318}, ] }, "string": {"name": "7XJT316", "confidence": 0.116}, "region": {"name": "California", "confidence": 0.9918}, } ] VEHICLES_PROCESSED = [ { "boundingBox": { "vertices": [ {"x": 289, "y": 150}, {"x": 1036, "y": 150}, {"x": 1036, "y": 602}, {"x": 289, "y": 602}, ] }, "recognitionConfidence": 0.8554, "vehicleType": "car", "make": "Ford", "model": "Ranger", "color": "black", "licenseplate": "unknown", "region": "unknown", } ] ALL_PROCESSED = [ { "boundingBox": { "vertices": [ {"x": 289, "y": 150}, {"x": 1036, "y": 150}, {"x": 1036, "y": 602}, {"x": 289, "y": 602}, ] }, "recognitionConfidence": 0.8554, "vehicleType": "car", "make": "Ford", "model": "Ranger", "color": "black", "licenseplate": "CV67CBU", "region": "UK", } ] def test_bbox_to_tf_style(): bbox = {"x": 227, "y": 133, "height": 245, "width": 125} img_width = 960 img_height = 480 assert hound.bbox_to_tf_style(bbox, img_width, img_height) == ( 0.27708, 0.23646, 0.7875, 0.36667, ) def test_bboxvert_to_tf_style(): bbox = RECOGNITIONS_LICENSEPLATE["objects"][0]["licenseplateAnnotation"]["bounding"] img_width = 960 img_height = 480 assert hound.bboxvert_to_tf_style(bbox, img_width, img_height) == ( 0.6125, 0.51458, 0.6625, 0.56458, ) def test_encode_image(): assert hound.encode_image(MOCK_BYTES) == B64_ENCODED_MOCK_BYTES def test_get_license_plates(): assert hound.get_license_plates(RECOGNITIONS_LICENSEPLATE) == LICENSEPLATE_PROCESSED def test_get_vehicles(): assert hound.get_vehicles(RECOGNITIONS_VEHICLES) == VEHICLES_PROCESSED assert hound.get_vehicles(RECOGNITIONS_ALL) == ALL_PROCESSED def test_get_people(): assert hound.get_people(DETECTIONS) == PEOPLE def test_get_metadata(): assert hound.get_metadata(DETECTIONS) == METADATA def test_good_run_detection(): with requests_mock.Mocker() as mock_req: mock_req.post(URL_DETECTIONS_DEV, status_code=hound.HTTP_OK, json=DETECTIONS) response = hound.run_detection( B64_ENCODED_MOCK_BYTES, MOCK_API_KEY, URL_DETECTIONS_DEV ) assert response == DETECTIONS def test_good_run_recognition(): with requests_mock.Mocker() as mock_req: mock_req.post( URL_RECOGNITIONS_DEV + "licenseplate", status_code=hound.HTTP_OK, json=RECOGNITIONS_LICENSEPLATE, ) response = hound.run_recognition( B64_ENCODED_MOCK_BYTES, MOCK_API_KEY, URL_RECOGNITIONS_DEV, "licenseplate", ) assert response == RECOGNITIONS_LICENSEPLATE def test_cloud_init(): """Test that the dev or prod url are being set correctly.""" api_dev = hound.cloud(MOCK_API_KEY) assert api_dev._url_detections == URL_DETECTIONS_DEV assert api_dev._url_recognitions == URL_RECOGNITIONS_DEV api_prod = hound.cloud(MOCK_API_KEY, mode="prod") assert api_prod._url_detections == URL_DETECTIONS_PROD assert api_prod._url_recognitions == URL_RECOGNITIONS_PROD with pytest.raises(hound.SimplehoundException) as exc: hound.cloud(MOCK_API_KEY, mode="bad") assert str(exc.value) == "Mode bad is not allowed, must be dev or prod" def test_cloud_detect_good(): with requests_mock.Mocker() as mock_req: mock_req.post(URL_DETECTIONS_DEV, status_code=hound.HTTP_OK, json=DETECTIONS) api = hound.cloud(MOCK_API_KEY) detections = api.detect(MOCK_BYTES) assert detections == DETECTIONS def test_cloud_recognize_licenseplate_good(): with requests_mock.Mocker() as mock_req: mock_req.post( URL_RECOGNITIONS_DEV + "licenseplate", status_code=hound.HTTP_OK, json=RECOGNITIONS_LICENSEPLATE, ) api = hound.cloud(MOCK_API_KEY) recognitions = api.recognize(MOCK_BYTES, "licenseplate") assert recognitions == RECOGNITIONS_LICENSEPLATE def test_cloud_detect_bad_key(): with pytest.raises( hound.SimplehoundException ) as exc, requests_mock.Mocker() as mock_req: mock_req.post( URL_DETECTIONS_DEV, status_code=hound.BAD_API_KEY, json=DETECTIONS ) api = hound.cloud(MOCK_API_KEY) detections = api.detect(MOCK_BYTES) assert str(exc.value) == "Bad API key for Sighthound"