同一张图片，为什么换个读取流程就认错了？

"""Original 2x2 RGB teaching fixture; not an image decoder or trained classifier."""
import json
import math
from pathlib import Path

CONTRACT = {"version": "toy-rgb-v1", "shape": [2, 2, 3], "layout": "HWC",
            "channels": ["R", "G", "B"], "input_range": [0, 255],
            "scale_divisor": 255, "feature_order": ["mean_R", "mean_G", "mean_B"],
            "class_names": ["below_rule", "above_rule"]}
IMAGE = [[[255, 0, 0], [128, 64, 0]], [[64, 128, 255], [0, 0, 128]]]
EXPECTED = [447 / 1020, 192 / 1020, 383 / 1020]  # Independent hand-counted channel sums.


def validate_image(image):
    if not isinstance(image, list) or len(image) != 2:
        raise ValueError("expected two rows")
    for row in image:
        if not isinstance(row, list) or len(row) != 2:
            raise ValueError("expected two pixels per row")
        for pixel in row:
            if not isinstance(pixel, list) or len(pixel) != 3:
                raise ValueError("expected RGB triple")
            if any(type(value) is not int or not 0 <= value <= 255 for value in pixel):
                raise ValueError("expected uint8-like integers")


def preprocess(image, channel_order=(0, 1, 2), divisor=255):
    validate_image(image)
    return [sum(pixel[c] for row in image for pixel in row) / (4 * divisor)
            for c in channel_order]


def validate_features(features):
    if len(features) != 3 or any(not math.isfinite(x) or not 0 <= x <= 1 for x in features):
        raise ValueError("three finite features in [0,1] required")


def golden_check(features):
    validate_features(features)
    if not all(math.isclose(a, b, rel_tol=0, abs_tol=1e-12) for a, b in zip(features, EXPECTED)):
        raise ValueError("golden feature mismatch")


def validate_contract(actual):
    if actual != CONTRACT:
        raise ValueError("preprocessing or class metadata differs from the saved contract")


def toy_score(features):
    # Fixed arithmetic chosen to expose a mismatch, not learned weights or probability.
    return 2 * features[0] - features[2] - 0.4


def must_reject(action):
    try:
        action()
    except ValueError:
        return
    raise AssertionError("bad contract accepted")


training_path = preprocess(IMAGE)
serving_path = preprocess(IMAGE)
golden_check(training_path)
golden_check(serving_path)
assert training_path == serving_path

swapped = preprocess(IMAGE, channel_order=(2, 1, 0))
unscaled = preprocess(IMAGE, divisor=1)
validate_features(swapped)  # Shape and range alone do not notice RGB/BGR confusion.
must_reject(lambda: golden_check(swapped))
must_reject(lambda: validate_features(unscaled))
assert toy_score(training_path) > 0 > toy_score(swapped)

class_id = int(toy_score(training_path) >= 0)
correct_name = CONTRACT["class_names"][class_id]
wrong_order = list(reversed(CONTRACT["class_names"]))
wrong_name = wrong_order[class_id]
assert correct_name != wrong_name
validate_contract(dict(CONTRACT))
must_reject(lambda: validate_contract({**CONTRACT, "class_names": wrong_order}))
must_reject(lambda: validate_image([[[0, 0, 0]]]))
must_reject(lambda: validate_image([[[256, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0]]]))
must_reject(lambda: validate_image([[[float("nan"), 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0]]]))

report = {"scope": "synthetic RGB fixture and fixed arithmetic, no image library or training",
          "contract": CONTRACT, "golden_image": IMAGE, "expected_features": EXPECTED,
          "correct": {"features": training_path, "score": toy_score(training_path), "class": correct_name},
          "rgb_bgr_error": {"features": swapped, "score": toy_score(swapped), "range_check_passes": True},
          "missing_scale_error": {"features": unscaled, "range_check_passes": False},
          "class_order_error": {"unchanged_class_id": class_id, "wrong_name": wrong_name}}
Path("preprocess-report.json").write_text(json.dumps(report, indent=2), encoding="utf-8")
print("correct_features", [round(x, 6) for x in training_path])
print("scores", round(toy_score(training_path), 6), round(toy_score(swapped), 6))
print("same_class_id_different_name", correct_name, wrong_name)
print("PASS: golden fixture and three mismatch checks; preprocess-report.json saved")