import cv2
import numpy as np
import os
import glob
import sys
import math
# -------------------------- 1. 环境准备与参数设定 --------------------------
# 创建失真图保存目录
output_dir = "distorted_images"
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# 自定义SSIM计算函数（使用OpenCV实现）
def compute_ssim(img1, img2, win_size=11, data_range=255):
"""
使用OpenCV实现的结构相似性指数(SSIM)计算
替代scikit-image的SSIM实现
"""
# 确保图像是单通道灰度图
if len(img1.shape) > 2:
img1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
if len(img2.shape) > 2:
img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
# 转换图像为浮点类型
img1 = img1.astype(np.float64)
img2 = img2.astype(np.float64)
# SSIM常量
C1 = (0.01 * data_range) ** 2
C2 = (0.03 * data_range) ** 2
# 计算均值
kernel = cv2.getGaussianKernel(win_size, 1.5)
kernel = np.outer(kernel, kernel.transpose())
mu1 = cv2.filter2D(img1, -1, kernel)
mu2 = cv2.filter2D(img2, -1, kernel)
# 计算方差和协方差
mu1_sq = mu1 ** 2
mu2_sq = mu2 ** 2
mu1_mu2 = mu1 * mu2
sigma1_sq = cv2.filter2D(img1 ** 2, -1, kernel) - mu1_sq
sigma2_sq = cv2.filter2D(img2 ** 2, -1, kernel) - mu2_sq
sigma12 = cv2.filter2D(img1 * img2, -1, kernel) - mu1_mu2
# 计算SSIM
ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))
return np.mean(ssim_map)
# 查找基准图像
def find_base_image():
# 支持的图像格式
image_extensions = ['png', 'jpg', 'jpeg', 'bmp', 'tiff', 'webp']
base_images = []
# 查找所有可能的图像文件
for ext in image_extensions:
base_images.extend(glob.glob(f'*.{ext}'))
if not base_images:
raise FileNotFoundError("❌ 未找到基准图像！请确保当前目录下有图像文件")
# 优先选择名称包含"base"或"reference"的图像
for img in base_images:
if 'base' in img.lower() or 'reference' in img.lower():
return img
# 如果没有明确标记的基准图，使用第一个找到的图像
return base_images[0]
# 获取基准图像路径
base_image_path = find_base_image()
print(f"✅ 找到基准图像: {base_image_path}")
# 读取基准图像
base_image = cv2.imread(base_image_path)
if base_image is None:
raise ValueError(f"❌ 无法读取图像: {base_image_path}")
# 转换为灰度图用于SSIM计算
base_image_gray = cv2.cvtColor(base_image, cv2.COLOR_BGR2GRAY)
IMG_HEIGHT, IMG_WIDTH = base_image_gray.shape
PIXEL_MAX = 255 # 8位图像像素最大值
SSIM_WINDOW_SIZE = 11 # SSIM默认滑动窗口大小
# 失真场景参数（根据图像尺寸动态调整）
ROTATE_ANGLE = 3 # 几何倾斜角度（°）
SALT_PEPPER_DENSITY = 0.1 # 椒盐噪声密度（10%）
SCRATCH_SIZE = (max(10, IMG_WIDTH//100), max(10, IMG_HEIGHT//100)) # 局部划痕尺寸
SATURATION_AREAS = [ # 高反光饱和区域（左上角坐标+宽高）
(IMG_WIDTH//4, IMG_HEIGHT//4, IMG_WIDTH//10, IMG_HEIGHT//10),
(IMG_WIDTH//2, IMG_HEIGHT//3, IMG_WIDTH//10, IMG_HEIGHT//10),
(IMG_WIDTH*3//4, IMG_HEIGHT*2//3, IMG_WIDTH//10, IMG_HEIGHT//10)
]
# -------------------------- 2. 6类失真图生成函数（新增枕型变形）--------------------------
def generate_distorted_images(base_img, base_gray):
distorted_imgs = {} # 存储失真图：key=失真类型，value=(图像, 保存路径)
# （1）几何倾斜（3°旋转）
h, w = base_gray.shape
M_rotate = cv2.getRotationMatrix2D((w//2, h//2), ROTATE_ANGLE, 1)
img_rotate = cv2.warpAffine(base_img, M_rotate, (w, h), borderValue=(255, 255, 255))
path_rotate = os.path.join(output_dir, "y1_geometric_rotation.jpg")
cv2.imwrite(path_rotate, img_rotate)
distorted_imgs["几何倾斜"] = (img_rotate, path_rotate)
# （2）高反光像素饱和（指定区域像素=255）
img_saturation = base_img.copy()
for (x, y, w_sea, h_sea) in SATURATION_AREAS:
# 创建白色矩形区域
cv2.rectangle(img_saturation, (x, y), (x+w_sea, y+h_sea), (255, 255, 255), -1)
path_saturation = os.path.join(output_dir, "y2_high_reflection_saturation.jpg")
cv2.imwrite(path_saturation, img_saturation)
distorted_imgs["高反光饱和"] = (img_saturation, path_saturation)
# （3）椒盐噪声（10%密度）
img_salt_pepper = base_img.copy()
noise_mask = np.random.choice([0, 1, 2], size=base_gray.shape[:2],
p=[SALT_PEPPER_DENSITY/2, SALT_PEPPER_DENSITY/2, 1-SALT_PEPPER_DENSITY])
# 椒噪声（暗斑）
img_salt_pepper[noise_mask == 0] = (0, 0, 0)
# 盐噪声（亮斑）
img_salt_pepper[noise_mask == 1] = (255, 255, 255)
path_salt_pepper = os.path.join(output_dir, "y3_salt_pepper_noise.jpg")
cv2.imwrite(path_salt_pepper, img_salt_pepper)
distorted_imgs["椒盐噪声"] = (img_salt_pepper, path_salt_pepper)
# （4）局部小划痕（黑色划痕）
img_scratch = base_img.copy()
scratch_x = IMG_WIDTH // 2 # 划痕中心x坐标
scratch_y = IMG_HEIGHT // 2 # 划痕中心y坐标
x1 = max(0, scratch_x - SCRATCH_SIZE[0] // 2)
y1 = max(0, scratch_y - SCRATCH_SIZE[1] // 2)
x2 = min(IMG_WIDTH, scratch_x + SCRATCH_SIZE[0] // 2)
y2 = min(IMG_HEIGHT, scratch_y + SCRATCH_SIZE[1] // 2)
cv2.rectangle(img_scratch, (x1, y1), (x2, y2), (0, 0, 0), -1) # 黑色划痕
path_scratch = os.path.join(output_dir, "y4_local_scratch.jpg")
cv2.imwrite(path_scratch, img_scratch)
distorted_imgs["局部小划痕"] = (img_scratch, path_scratch)
# （5）透视变形（底部宽、顶部窄）
# 原始四边形顶点（左上、右上、右下、左下）
src_pts = np.float32([
[100, 100], [w-100, 100],
[w-100, h-100], [100, h-100]
])
# 透视变换后顶点（顶部收缩，底部不变）
dst_pts = np.float32([
[w//2 - w//8, 100], [w//2 + w//8, 100],
[w-100, h-100], [100, h-100]
])
M_persp = cv2.getPerspectiveTransform(src_pts, dst_pts)
img_perspective = cv2.warpPerspective(base_img, M_persp, (w, h), borderValue=(255, 255, 255))
path_perspective = os.path.join(output_dir, "y5_perspective_distortion.jpg")
cv2.imwrite(path_perspective, img_perspective)
distorted_imgs["透视变形"] = (img_perspective, path_perspective)
# （6）新增：枕型变形（Pincushion Distortion）
img_pincushion = base_img.copy()
# 创建枕型变形映射
map_x = np.zeros((h, w), dtype=np.float32)
map_y = np.zeros((h, w), dtype=np.float32)
# 中心点坐标
cx, cy = w//2, h//2
# 枕型变形参数（可调整）
strength = 0.0005 # 变形强度
for y in range(h):
for x in range(w):
# 计算相对中心点的距离（归一化到[-1,1]）
dx = (x - cx) / cx
dy = (y - cy) / cy
# 计算径向距离
r = math.sqrt(dx*dx + dy*dy)
# 枕型变形公式：向中心凹陷
factor = 1 + strength * r**4
# 应用变换
map_x[y, x] = cx + factor * (x - cx)
map_y[y, x] = cy + factor * (y - cy)
# 应用重映射
img_pincushion = cv2.remap(base_img, map_x, map_y, cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT, borderValue=(255, 255, 255))
path_pincushion = os.path.join(output_dir, "y6_pincushion_distortion.jpg")
cv2.imwrite(path_pincushion, img_pincushion)
distorted_imgs["枕型变形"] = (img_pincushion, path_pincushion)
print(f"✅ 6类失真图已全部保存至：{output_dir}")
return distorted_imgs
# -------------------------- 3. 评分计算（SSIM+模拟主观评分） --------------------------
def calculate_scores(base_gray, distorted_imgs):
scores = []
# 模拟主观评分（基于工程实际感知）
subjective_scores = {
"几何倾斜": 3.0,
"高反光饱和": 2.8,
"椒盐噪声": 2.5,
"局部小划痕": 4.2,
"透视变形": 2.9,
"枕型变形": 3.2 # 新增枕型变形的主观评分
}
for dist_type, (dist_img, dist_path) in distorted_imgs.items():
# 转换为灰度图用于SSIM计算
dist_gray = cv2.cvtColor(dist_img, cv2.COLOR_BGR2GRAY)
# 计算SSIM评分（使用自定义的OpenCV实现）
ssim_score = compute_ssim(base_gray, dist_gray, win_size=SSIM_WINDOW_SIZE, data_range=PIXEL_MAX)
ssim_score = round(ssim_score, 2) # 保留2位小数
subj_score = subjective_scores.get(dist_type, 3.0) # 模拟主观评分
scores.append({
"失真类型": dist_type,
"SSIM评分": ssim_score,
"模拟主观评分（5分制）": subj_score,
"失真图路径": dist_path
})
# 添加基准图评分
base_ssim = compute_ssim(base_gray, base_gray, data_range=PIXEL_MAX)
scores.insert(0, {
"失真类型": "基准图像",
"SSIM评分": round(base_ssim, 2),
"模拟主观评分（5分制）": 5.0,
"失真图路径": base_image_path
})
return scores
# -------------------------- 4. 结果可视化输出 --------------------------
def print_score_table(scores):
print("\n" + "="*100)
print("📊 SSIM评分与主观评分对比表")
print("="*100)
# 格式化输出表头
print(f"{'失真类型':<10} {'SSIM评分':<10} {'模拟主观评分（5分制）':<20} {'图像路径':<60}")
print("-"*100)
for item in scores:
print(
f"{item['失真类型']:<10} {item['SSIM评分']:<10} {item['模拟主观评分（5分制）']:<20} {item['失真图路径']:<60}"
)
print("="*100 + "\n")
# -------------------------- 5. 主流程执行 --------------------------
if __name__ == "__main__":
try:
# 步骤1：生成6类失真图
distorted_images = generate_distorted_images(base_image, base_image_gray)
# 步骤2：计算评分
score_table = calculate_scores(base_image_gray, distorted_images)
# 步骤3：输出结果
print_score_table(score_table)
# 步骤4：打印SSIM局限性总结
print("⚠️ 基于评分结果的SSIM局限性总结：")
print("1. 几何倾斜/透视变形：SSIM评分通常较高（≥0.9），但主观评分较低（~3.0）→ 无法有效识别几何失真；")
print("2. 高反光饱和：SSIM评分较高（~0.88），但主观评分较低（~2.8）→ 像素饱和导致结构相似性被高估；")
print("3. 椒盐噪声：SSIM评分中等（~0.75），主观评分较低（~2.5）→ 对稀疏噪声敏感性不足；")
print("4. 局部小划痕：SSIM评分很高（~0.98），主观评分较高（~4.2）→ 局部小缺陷被全局平均掩盖；")
print("5. 枕型变形：SSIM评分中等（~0.85），主观评分中等（~3.2）→ 对几何形变敏感度不足。")
except Exception as e:
print(f"❌ 程序执行出错: {str(e)}")
print("请确保：")
print("1. 当前目录存在图像文件（支持格式：png, jpg, jpeg, bmp, tiff, webp）")
print("2. OpenCV库已正确安装")
print("3. 有足够的磁盘空间保存生成的失真图像")
现有的代码运行，第六类失真图像基本没变化，请调整相关参数，使得枕型变形明显，给出完整代码，并说明对哪些参数进行了调整。
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本文介绍了透视变形的概念，包括其特点及两种形式：扩展变形与压缩变形，并通过实例解释了距离如何影响变形的程度。
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