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import base64
import json
import os
import requests
import time
import pytest
from io import BytesIO
from pathlib import Path
from PIL import Image
from tests.inference.integration_tests.regression_test import compare_detection_response
PIXEL_TOLERANCE = 2
CONFIDENCE_TOLERANCE = 0.005
TIME_TOLERANCE = 0.75
api_key = os.environ.get("API_KEY")
port = os.environ.get("PORT", 9001)
base_url = os.environ.get("BASE_URL", "http://localhost")
def bool_env(val):
if isinstance(val, bool):
return val
return val.lower() in ["true", "1", "t", "y", "yes"]
def infer_request_with_image_url(
test, port=9001, api_key="", base_url="http://localhost", batch_size=1
):
payload = {
"model_id": f"{test['project']}/{test['version']}",
"image": [
{
"type": "url",
"value": test["image_url"],
}
]
* batch_size,
"confidence": test["confidence"],
"iou_threshold": test["iou_threshold"],
"api_key": api_key,
}
return (
requests.post(
f"{base_url}:{port}/infer/{test['type']}",
json=payload,
),
"url",
)
def infer_request_with_base64_image_dif_size(
test, port=9001, api_key="", base_url="http://localhost", batch_size=1
):
sizes = [
(155, 73),
(125, 43),
(165, 82),
(115, 36),
(134, 58),
(115, 40),
(164, 91),
(200, 88),
(137, 60),
(155, 77),
(144, 65),
(132, 49),
(140, 52),
(178, 75),
(155, 61),
(147, 56),
]
images = []
for i in range(batch_size):
buffered = BytesIO()
im = test["pil_image"]
im = im.resize(sizes[i])
im.save(buffered, quality=100, format="PNG")
img_str = base64.b64encode(buffered.getvalue())
img_str = img_str.decode("ascii")
images.append(img_str)
payload = {
"model_id": f"{test['project']}/{test['version']}",
"image": [
{
"type": "base64",
"value": img_str,
}
for img_str in images
],
"confidence": test["confidence"],
"iou_threshold": test["iou_threshold"],
"api_key": api_key,
}
return (
requests.post(
f"{base_url}:{port}/infer/{test['type']}",
json=payload,
),
"base64_diffsize",
)
def infer_request_with_base64_image_dif_size_fixed(
test, port=9001, api_key="", base_url="http://localhost", batch_size=1
):
sizes = [
(155, 73),
(125, 43),
(165, 82),
(115, 36),
(134, 58),
(115, 40),
(164, 91),
(200, 88),
(137, 60),
(155, 77),
(144, 65),
(132, 49),
(140, 52),
(178, 75),
(155, 61),
(147, 56),
]
images = []
for i in range(batch_size):
buffered = BytesIO()
im = test["pil_image"]
im = im.resize(sizes[i])
im.save(buffered, quality=100, format="PNG")
img_str = base64.b64encode(buffered.getvalue())
img_str = img_str.decode("ascii")
images.append(img_str)
payload = {
"model_id": f"{test['project']}/{test['version']}",
"image": [
{
"type": "base64",
"value": img_str,
}
for img_str in images
],
"confidence": test["confidence"],
"iou_threshold": test["iou_threshold"],
"api_key": api_key,
"fix_batch_size": True,
}
return (
requests.post(
f"{base_url}:{port}/infer/{test['type']}",
json=payload,
),
"base64_diffsize",
)
def infer_request_with_base64_image(
test, port=9001, api_key="", base_url="http://localhost", batch_size=1
):
buffered = BytesIO()
test["pil_image"].save(buffered, quality=100, format="PNG")
img_str = base64.b64encode(buffered.getvalue())
img_str = img_str.decode("ascii")
payload = {
"model_id": f"{test['project']}/{test['version']}",
"image": [
{
"type": "base64",
"value": img_str,
}
]
* batch_size,
"confidence": test["confidence"],
"iou_threshold": test["iou_threshold"],
"api_key": api_key,
}
return (
requests.post(
f"{base_url}:{port}/infer/{test['type']}",
json=payload,
),
"base64",
)
with open(os.path.join(Path(__file__).resolve().parent, "batch_tests.json"), "r") as f:
TESTS = json.load(f)
INFER_RESPONSE_FUNCTIONS = [
infer_request_with_image_url,
infer_request_with_base64_image,
infer_request_with_base64_image_dif_size,
infer_request_with_base64_image_dif_size_fixed,
]
DETECTION_TEST_PARAMS = []
for test in TESTS:
if "expected_response" in test:
for res_func in INFER_RESPONSE_FUNCTIONS:
DETECTION_TEST_PARAMS.append((test, res_func))
@pytest.mark.parametrize("test,res_function", DETECTION_TEST_PARAMS)
def test_detection(test, res_function):
try:
try:
pil_image = Image.open(
requests.get(test["image_url"], stream=True).raw
).convert("RGB")
test["pil_image"] = pil_image
except Exception as e:
raise ValueError(f"Unable to load image from URL: {test['image_url']}")
response, image_type = res_function(
test, port, api_key=os.getenv(f"{test['project'].replace('-','_')}_API_KEY")
)
try:
response.raise_for_status()
except requests.exceptions.HTTPError as e:
raise ValueError(f"Failed to make request to {res_function.__name__}: {e}")
try:
data = response.json()
except ValueError:
raise ValueError(f"Invalid JSON response: {response.text}")
try:
assert "expected_response" in test
except AssertionError:
raise ValueError(
f"Invalid test: {test}, Missing 'expected_response' field in test."
)
try:
assert image_type in test["expected_response"]
except AssertionError:
raise ValueError(
f"Invalid test: {test}, Missing 'expected_response' field for image type {image_type}."
)
if not bool_env(os.getenv("FUNCTIONAL", False)):
for d, test_data in zip(data, test["expected_response"][image_type]):
compare_detection_response(
d,
test_data,
type=test["type"],
multilabel=test.get("multi_label", False),
)
print(
"\u2713"
+ f" Test {test['project']}/{test['version']} passed with {res_function.__name__}."
)
except Exception as e:
raise Exception(f"Error in test {test['description']}: {e}")
@pytest.fixture(scope="session", autouse=True)
def setup():
try:
res = requests.get(f"{base_url}:{port}")
res.raise_for_status()
success = True
except:
success = False
waited = 0
while not success:
print("Waiting for server to start...")
time.sleep(5)
waited += 5
try:
res = requests.get(f"{base_url}:{port}")
res.raise_for_status()
success = True
except:
success = False
if waited > 30:
raise Exception("Test server failed to start")
if __name__ == "__main__":
# This helps figure out which test is failing
for i, param in enumerate(DETECTION_TEST_PARAMS):
print(i, param[0]["project"], param[0]["version"], param[1].__name__)
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