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import datetime
import numpy as np
import os
from PIL import Image
import pytest
from pytest import fixture
from typing import Tuple, List
from cv2 import imread, cvtColor, COLOR_BGR2RGB
from skimage.metrics import structural_similarity as ssim
"""
This test suite compares images in 2 directories by file name
The directories are specified by the command line arguments --baseline_dir and --test_dir
"""
# ssim: Structural Similarity Index
# Returns a tuple of (ssim, diff_image)
def ssim_score(img0: np.ndarray, img1: np.ndarray) -> Tuple[float, np.ndarray]:
score, diff = ssim(img0, img1, channel_axis=-1, full=True)
# rescale the difference image to 0-255 range
diff = (diff * 255).astype("uint8")
return score, diff
# Metrics must return a tuple of (score, diff_image)
METRICS = {"ssim": ssim_score}
METRICS_PASS_THRESHOLD = {"ssim": 0.95}
class TestCompareImageMetrics:
@fixture(scope="class")
def test_file_names(self, args_pytest):
test_dir = args_pytest['test_dir']
fnames = self.gather_file_basenames(test_dir)
yield fnames
del fnames
@fixture(scope="class", autouse=True)
def teardown(self, args_pytest):
yield
# Runs after all tests are complete
# Aggregate output files into a grid of images
baseline_dir = args_pytest['baseline_dir']
test_dir = args_pytest['test_dir']
img_output_dir = args_pytest['img_output_dir']
metrics_file = args_pytest['metrics_file']
grid_dir = os.path.join(img_output_dir, "grid")
os.makedirs(grid_dir, exist_ok=True)
for metric_dir in METRICS.keys():
metric_path = os.path.join(img_output_dir, metric_dir)
for file in os.listdir(metric_path):
if file.endswith(".png"):
score = self.lookup_score_from_fname(file, metrics_file)
image_file_list = []
image_file_list.append([
os.path.join(baseline_dir, file),
os.path.join(test_dir, file),
os.path.join(metric_path, file)
])
# Create grid
image_list = [[Image.open(file) for file in files] for files in image_file_list]
grid = self.image_grid(image_list)
grid.save(os.path.join(grid_dir, f"{metric_dir}_{score:.3f}_{file}"))
# Tests run for each baseline file name
@fixture()
def fname(self, baseline_fname):
yield baseline_fname
del baseline_fname
def test_directories_not_empty(self, args_pytest):
baseline_dir = args_pytest['baseline_dir']
test_dir = args_pytest['test_dir']
assert len(os.listdir(baseline_dir)) != 0, f"Baseline directory {baseline_dir} is empty"
assert len(os.listdir(test_dir)) != 0, f"Test directory {test_dir} is empty"
def test_dir_has_all_matching_metadata(self, fname, test_file_names, args_pytest):
# Check that all files in baseline_dir have a file in test_dir with matching metadata
baseline_file_path = os.path.join(args_pytest['baseline_dir'], fname)
file_paths = [os.path.join(args_pytest['test_dir'], f) for f in test_file_names]
file_match = self.find_file_match(baseline_file_path, file_paths)
assert file_match is not None, f"Could not find a file in {args_pytest['test_dir']} with matching metadata to {baseline_file_path}"
# For a baseline image file, finds the corresponding file name in test_dir and
# compares the images using the metrics in METRICS
@pytest.mark.parametrize("metric", METRICS.keys())
def test_pipeline_compare(
self,
args_pytest,
fname,
test_file_names,
metric,
):
baseline_dir = args_pytest['baseline_dir']
test_dir = args_pytest['test_dir']
metrics_output_file = args_pytest['metrics_file']
img_output_dir = args_pytest['img_output_dir']
baseline_file_path = os.path.join(baseline_dir, fname)
# Find file match
file_paths = [os.path.join(test_dir, f) for f in test_file_names]
test_file = self.find_file_match(baseline_file_path, file_paths)
# Run metrics
sample_baseline = self.read_img(baseline_file_path)
sample_secondary = self.read_img(test_file)
score, metric_img = METRICS[metric](sample_baseline, sample_secondary)
metric_status = score > METRICS_PASS_THRESHOLD[metric]
# Save metric values
with open(metrics_output_file, 'a') as f:
run_info = os.path.splitext(fname)[0]
metric_status_str = "PASS ✅" if metric_status else "FAIL ❌"
date_str = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
f.write(f"| {date_str} | {run_info} | {metric} | {metric_status_str} | {score} | \n")
# Save metric image
metric_img_dir = os.path.join(img_output_dir, metric)
os.makedirs(metric_img_dir, exist_ok=True)
output_filename = f'{fname}'
Image.fromarray(metric_img).save(os.path.join(metric_img_dir, output_filename))
assert score > METRICS_PASS_THRESHOLD[metric]
def read_img(self, filename: str) -> np.ndarray:
cvImg = imread(filename)
cvImg = cvtColor(cvImg, COLOR_BGR2RGB)
return cvImg
def image_grid(self, img_list: list[list[Image.Image]]):
# imgs is a 2D list of images
# Assumes the input images are a rectangular grid of equal sized images
rows = len(img_list)
cols = len(img_list[0])
w, h = img_list[0][0].size
grid = Image.new('RGB', size=(cols*w, rows*h))
for i, row in enumerate(img_list):
for j, img in enumerate(row):
grid.paste(img, box=(j*w, i*h))
return grid
def lookup_score_from_fname(self,
fname: str,
metrics_output_file: str
) -> float:
fname_basestr = os.path.splitext(fname)[0]
with open(metrics_output_file, 'r') as f:
for line in f:
if fname_basestr in line:
score = float(line.split('|')[5])
return score
raise ValueError(f"Could not find score for {fname} in {metrics_output_file}")
def gather_file_basenames(self, directory: str):
files = []
for file in os.listdir(directory):
if file.endswith(".png"):
files.append(file)
return files
def read_file_prompt(self, fname:str) -> str:
# Read prompt from image file metadata
img = Image.open(fname)
img.load()
return img.info['prompt']
def find_file_match(self, baseline_file: str, file_paths: List[str]):
# Find a file in file_paths with matching metadata to baseline_file
baseline_prompt = self.read_file_prompt(baseline_file)
# Do not match empty prompts
if baseline_prompt is None or baseline_prompt == "":
return None
# Find file match
# Reorder test_file_names so that the file with matching name is first
# This is an optimization because matching file names are more likely
# to have matching metadata if they were generated with the same script
basename = os.path.basename(baseline_file)
file_path_basenames = [os.path.basename(f) for f in file_paths]
if basename in file_path_basenames:
match_index = file_path_basenames.index(basename)
file_paths.insert(0, file_paths.pop(match_index))
for f in file_paths:
test_file_prompt = self.read_file_prompt(f)
if baseline_prompt == test_file_prompt:
return f |