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