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31a46d7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 | """Run one packaged EfficientNet variant with the documented desktop app policy."""
import argparse
import hashlib
import json
from pathlib import Path
import numpy as np
from PIL import Image
from torchvision import transforms
ROOT = Path(__file__).resolve().parent
INPUT_SIZE = 224
HIGHLIGHT = 250
def read_json(path):
return json.loads(Path(path).read_text(encoding="utf-8"))
def digest(path):
return hashlib.sha256(Path(path).read_bytes()).hexdigest()
def raw_tensor(path, config):
transform = transforms.Compose(
[
transforms.Resize(
int(INPUT_SIZE / config["pretrained_cfg"]["crop_pct"]),
interpolation=transforms.InterpolationMode.BICUBIC,
),
transforms.CenterCrop(INPUT_SIZE),
]
)
with Image.open(path) as image:
return np.asarray(transform(image.convert("RGB")), dtype=np.float32)[None]
def crop_rgba(image, short_side):
width, height = image.size
scale = short_side / min(width, height)
width, height = int(width * scale), int(height * scale)
resized = image.convert("RGBA").resize((width, height), Image.Resampling.BICUBIC)
left = int((width - INPUT_SIZE) / 2 + 0.5)
top = int((height - INPUT_SIZE) / 2 + 0.5)
return np.asarray(resized.crop((left, top, left + INPUT_SIZE, top + INPUT_SIZE)))
def exposure_features(rgba):
rgb = rgba[:, :, :3].astype(np.uint32)
gray = (
19595 * rgb[:, :, 0] + 38470 * rgb[:, :, 1] + 7471 * rgb[:, :, 2] + 32768
) // 65536
alpha = rgba[:, :, 3].astype(np.float64)
total = float(alpha.sum())
if total == 0:
raise ValueError("Cannot measure an entirely transparent image")
return {
"mean_luminance": float((gray * alpha).sum() / total),
"highlight_fraction": float(((gray >= HIGHLIGHT) * alpha).sum() / total),
}
def exposure_state(features, config):
if features["mean_luminance"] < config["minimum_mean_luminance"]:
return "too_dark"
if (
features["mean_luminance"] > config["maximum_mean_luminance"]
and features["highlight_fraction"] > config["maximum_highlight_fraction"]
):
return "too_bright"
return "acceptable"
def image_quality(path, raw, calibration, brightness):
gray = np.asarray(
Image.fromarray(raw[0].astype(np.uint8)).convert("L"), dtype=np.float32
)
laplacian = (
gray[1:-1, 1:-1] * 4
- gray[2:, 1:-1]
- gray[:-2, 1:-1]
- gray[1:-1, 2:]
- gray[1:-1, :-2]
)
edge_variance = float(laplacian.var())
with Image.open(path) as image:
exposure = exposure_features(
crop_rgba(image, brightness["preprocessing"]["resize_short_side"])
)
state = exposure_state(exposure, brightness)
return dict(
edge_variance=edge_variance,
blur_passed=edge_variance >= calibration["quality"]["minimum_edge_variance"],
exposure_state=state,
**exposure,
)
def probabilities_tflite(folder, raw, calibration, threads):
from ai_edge_litert.interpreter import Interpreter
path = folder / "model.tflite"
if digest(path) != calibration["artifact_sha256"]:
raise ValueError("TFLite weights do not match their calibration")
interpreter = Interpreter(model_path=str(path), num_threads=threads)
interpreter.allocate_tensors()
inp = interpreter.get_input_details()[0]
out = interpreter.get_output_details()[0]
assert inp["shape"].tolist() == [1, INPUT_SIZE, INPUT_SIZE, 3]
interpreter.set_tensor(inp["index"], raw)
interpreter.invoke()
return interpreter.get_tensor(out["index"])[0]
def probabilities_pytorch(folder, raw, config, calibration, threads):
import timm
import torch
from safetensors.torch import load_file
torch.set_num_threads(threads)
path = folder / "model.safetensors"
if digest(path) != config["checkpoint_sha256"]:
raise ValueError("PyTorch checkpoint does not match its configuration")
model = timm.create_model(
config["architecture"], pretrained=False, num_classes=len(config["label_names"])
)
model.load_state_dict(load_file(str(path)), strict=True)
model.eval()
cfg = config["pretrained_cfg"]
tensor = torch.from_numpy(raw).permute(0, 3, 1, 2) / 255.0
tensor = (tensor - torch.tensor(cfg["mean"])[None, :, None, None]) / torch.tensor(
cfg["std"]
)[None, :, None, None]
with torch.inference_mode():
return (model(tensor) / calibration["temperature"]).softmax(1)[0].numpy()
def predict(image, variant="b2", backend="tflite", threads=4):
folder = ROOT if variant == "b2" else ROOT / "models" / variant
config = read_json(folder / "config.json")
calibration = read_json(folder / "model-config.json")["calibration"]
brightness = read_json(ROOT / "brightness-config.json")
raw = raw_tensor(image, config)
quality = image_quality(image, raw, calibration, brightness)
if backend == "tflite":
probabilities = probabilities_tflite(folder, raw, calibration, threads)
else:
probabilities = probabilities_pytorch(folder, raw, config, calibration, threads)
assert np.isfinite(probabilities).all()
np.testing.assert_allclose(probabilities.sum(), 1.0, atol=1e-4)
index = int(probabilities.argmax())
score = float(probabilities[index])
thresholds = calibration["class_thresholds"] + [1.01]
confident = calibration["confident_thresholds"] + [1.01]
accepted = (
index < 7
and score >= thresholds[index]
and quality["blur_passed"]
and quality["exposure_state"] == "acceptable"
)
state = (
"unclear"
if not accepted
else "confident"
if score >= confident[index]
else "possible"
)
return dict(
model=variant,
backend=backend,
predicted_label=config["label_names"][index],
score=score,
accepted=bool(accepted),
state=state,
installed_in_original_app=config["installed_in_original_app"],
quality=quality,
probabilities=dict(zip(config["label_names"], map(float, probabilities))),
)
def main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("image", type=Path)
parser.add_argument("--model", choices=["b0", "b1", "b2", "b3"], default="b2")
parser.add_argument("--backend", choices=["tflite", "pytorch"], default="tflite")
parser.add_argument("--threads", type=int, default=4)
args = parser.parse_args()
if args.threads < 1:
parser.error("--threads must be positive")
print(
json.dumps(
predict(args.image, args.model, args.backend, args.threads), indent=2
)
)
if __name__ == "__main__":
main()
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