水题堆3.A - A1 = ?

#include <stdio.h>
#include <stdlib.h>

int main()
{
    int n,i;
    double a0,an,s,k,c[3010],a1;
    while(scanf("%d",&n)!=EOF){
        scanf("%lf%lf",&a0,&an);
        for(i=1;i<=n;i++)scanf("%lf",&c[i]);
        k=2*n;
        s=0;
        for(i=1;i<=n;i++){
            s+=k*c[i];
            k=k-2;
        }
        a1=(an-s+n*a0)/(n+1);
        printf("%.2lf\n",a1);
    }
    return 0;
}

找规律,写了三项

A2=2A1+2C1-A0;

A3=3A1+4C1+2C2-2A0;

A4=4A1+6C1+4C2+2C3-3A0;

找出规律

AN=NA1+(2N-2)C1+(2N-4)C2+...+2CN-1-(N-1)A0;

每个通道都是独立的,最后的可视化图表对比应该是红蓝绿三个通道前后对比的图片,色差计算也是三通道分别计算色差,输出各自的偏差和总体分析, 第一个表也就是RGB目标值体现的信息量其实就是 target_red = (220, 0, 0) # 红色目标值target_green = (0, 220, 0) # 绿色目标值target_blue = (0, 0, 220) # 蓝色目标值并且是一个64*64的led显示屏,请根据我补充的信息修改原先的错误import numpy as np import pandas as pd import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D from scipy.ndimage import median_filter, percentile_filter from scipy.cluster.vq import kmeans2 from sklearn.metrics import pairwise_distances from tqdm import tqdm import warnings warnings.filterwarnings("ignore", category=RuntimeWarning) # ====================== 基础配置 ====================== TARGET_VALUE = 220 D65_WHITEPOINT = np.array([0.95047, 1.0, 1.08883]) # D65白点XYZ值 CELL_SIZE = 64 # 显示模块尺寸 GAMMA = 2.4 # ====================== 颜色空间转换函数 ====================== def gamma_correction(rgb, gamma=GAMMA, inverse=False): """Gamma校正处理""" if inverse: return np.where(rgb <= 0.04045, rgb/12.92, ((rgb+0.055)/1.055)**gamma) else: return np.where(rgb <= 0.0031308, 12.92*rgb, 1.055*(rgb**(1/gamma)) - 0.055) def rgb_to_xyz(rgb, rgb_space=&#39;bt2020&#39;): """自定义RGB到XYZ转换""" # BT.2020转换矩阵 if rgb_space == &#39;bt2020&#39;: M = np.array([ [0.4123908, 0.35758434, 0.18048079], [0.21263901, 0.71516868, 0.07219232], [0.01933082, 0.11919478, 0.95053215] ]) return np.dot(rgb, M.T) def xyz_to_lab(xyz): """自定义XYZ到Lab转换""" epsilon = 216/24389 # (6/29)^3 kappa = 24389/27 # (29/3)^3 xyz_n = xyz / D65_WHITEPOINT f_xyz = np.where(xyz_n > epsilon, xyz_n**(1/3), (kappa*xyz_n + 16)/116) L = 116 * f_xyz[:,1] - 16 a = 500 * (f_xyz[:,0] - f_xyz[:,1]) b = 200 * (f_xyz[:,1] - f_xyz[:,2]) return np.column_stack((L, a, b)) def lab_to_xyz(lab): """自定义Lab到XYZ转换""" epsilon = 216/24389 kappa = 24389/27 y = (lab[:,0] + 16) / 116 x = lab[:,1]/500 + y z = y - lab[:,2]/200 xyz = np.column_stack((x, y, z)) xyz = np.where(xyz**3 > epsilon, xyz**3, (116*xyz - 16)/kappa) return xyz * D65_WHITEPOINT import numpy as np def delta_e_ciede2000(lab1, lab2, kL=1.0, kC=1.0, kH=1.0): """ CIEDE2000色差公式完整实现 参数: lab1, lab2: 形状为(N,3)的numpy数组,包含CIE Lab颜色值 kL, kC, kH: 权重参数(默认1.0) 返回: deltaE: 形状为(N,)的色差数组 """ # 拆分Lab分量 L1, a1, b1 = lab1[:,0], lab1[:,1], lab1[:,2] L2, a2, b2 = lab2[:,0], lab2[:,1], lab2[:,2] # 步骤1:计算Cabar C1 = np.sqrt(a1**2 + b1**2) C2 = np.sqrt(a2**2 + b2**2) C_avg = (C1 + C2) / 2 # 步骤2:计算G因子 G = 0.5 * (1 - np.sqrt(C_avg**7 / (C_avg**7 + 25**7))) # 步骤3:调整a分量 a1_prime = a1 * (1 + G) a2_prime = a2 * (1 + G) # 步骤4:重新计算C&#39; C1_prime = np.sqrt(a1_prime**2 + b1**2) C2_prime = np.sqrt(a2_prime**2 + b2**2) C_prime_avg = (C1_prime + C2_prime) / 2 # 步骤5:计算h&#39; h1_prime = np.degrees(np.arctan2(b1, a1_prime)) % 360 h2_prime = np.degrees(np.arctan2(b2, a2_prime)) % 360 # 步骤6:计算Δh&#39; dh_prime = h2_prime - h1_prime dh_prime = np.where((C1_prime == 0) | (C2_prime == 0), 0, dh_prime) dh_prime = np.where(np.abs(dh_prime) <= 180, dh_prime, np.where(dh_prime > 180, dh_prime - 360, dh_prime + 360)) # 步骤7:计算ΔH&#39; dH_prime = 2 * np.sqrt(C1_prime * C2_prime) * np.sin(np.radians(dh_prime)/2) # 步骤8:计算h&#39;_avg h_prime_avg = np.where((C1_prime == 0) & (C2_prime == 0), 0, (h1_prime + h2_prime + np.where(np.abs(h1_prime - h2_prime) > 180, 360, 0)) / 2) # 步骤9:计算T补偿项 T = (1 - 0.17 * np.cos(np.radians(h_prime_avg - 30)) + 0.24 * np.cos(np.radians(2 * h_prime_avg)) + 0.32 * np.cos(np.radians(3 * h_prime_avg + 6)) - 0.20 * np.cos(np.radians(4 * h_prime_avg - 63))) # 步骤10:计算Δθ和RC delta_theta = 30 * np.exp(-((h_prime_avg - 275)/25)**2) R_C = 2 * np.sqrt(C_prime_avg**7 / (C_prime_avg**7 + 25**7)) R_T = -np.sin(np.radians(2 * delta_theta)) * R_C # 步骤11:计算补偿因子 S_L = 1 + (0.015 * ( (L1+L2)/2 - 50 )**2 ) / np.sqrt(20 + ( (L1+L2)/2 -50 )**2 ) S_C = 1 + 0.045 * C_prime_avg S_H = 1 + 0.015 * C_prime_avg * T # 步骤12:计算各分量差异 delta_L = L2 - L1 delta_C = C2_prime - C1_prime # 最终色差计算 term1 = (delta_L/(kL*S_L))**2 term2 = (delta_C/(kC*S_C))**2 term3 = (dH_prime/(kH*S_H))**2 term4 = R_T * (delta_C/(kC*S_C)) * (dH_prime/(kH*S_H)) deltaE = np.sqrt(term1 + term2 + term3 + term4) return deltaE # ====================== 数据加载 ====================== def load_data(file_path): """加载Excel数据""" # 读取目标值 target_df = pd.read_excel(file_path, sheet_name=&#39;RGB目标值&#39;) # 添加列名检查 print("目标值表的列名:", target_df.columns.tolist()) targets = { &#39;red&#39;: target_df[target_df[&#39;220.1&#39;] == TARGET_VALUE].values[0], &#39;green&#39;: target_df[target_df[&#39;220.2&#39;] == TARGET_VALUE].values[0], &#39;blue&#39;: target_df[target_df[&#39;220.3&#39;] == TARGET_VALUE].values[0] } # 读取测量数据 sheets = [&#39;R_R&#39;, &#39;R_G&#39;, &#39;R_B&#39;, &#39;G_R&#39;, &#39;G_G&#39;, &#39;G_B&#39;, &#39;B_R&#39;, &#39;B_G&#39;, &#39;B_B&#39;] raw_data = {} for sheet in sheets: raw_data[sheet] = pd.read_excel(file_path, sheet_name=sheet).values # 构建三维数据立方体 data_cube = np.zeros((63, CELL_SIZE, 3, 3)) # 填充红色通道测量数据 data_cube[:,:,:,0] = np.stack([raw_data[&#39;R_R&#39;], raw_data[&#39;R_G&#39;], raw_data[&#39;R_B&#39;]], axis=2) # 填充绿色通道测量数据 data_cube[:,:,:,1] = np.stack([raw_data[&#39;G_R&#39;], raw_data[&#39;G_G&#39;], raw_data[&#39;G_B&#39;]], axis=2) # 填充蓝色通道测量数据 data_cube[:,:,:,2] = np.stack([raw_data[&#39;B_R&#39;], raw_data[&#39;B_G&#39;], raw_data[&#39;B_B&#39;]], axis=2) return targets, data_cube # ====================== 预处理 ====================== def preprocess_data(data_cube): """数据预处理""" # Gamma校正 linear_data = gamma_correction(data_cube/255.0, inverse=True) # 中值滤波+百分位滤波 filtered_data = np.zeros_like(linear_data) for i in range(3): # R,G,B for j in range(3): # R,G,B分量 ch_data = linear_data[:,:,:,i] filtered_data[:,:,:,i] = median_filter(ch_data, size=(3,3,1)) filtered_data[:,:,:,i] = percentile_filter(filtered_data[:,:,:,i], 80, size=(5,5,1)) return filtered_data # ====================== 动态分区校正 ====================== def find_optimal_clusters(data, max_k=20): """使用肘部法则确定最佳分区数""" distortions = [] for k in tqdm(range(1, max_k+1), desc="计算最佳分区数"): centroids, labels = kmeans2(data, k, minit=&#39;++&#39;) distortion = np.mean(pairwise_distances(data, centroids, metric=&#39;euclidean&#39;)) distortions.append(distortion) # 计算二阶导数确定拐点 diff = np.diff(distortions, 2) optimal_k = np.argmax(diff) + 2 # 补偿二阶差分索引偏移 return min(optimal_k, max_k) def dynamic_partition_correction(lab_data): """自适应动态分区校正""" points = lab_data.reshape(-1, 3) # 自动确定最佳分区数 optimal_k = find_optimal_clusters(points) print(f"自动确定最佳分区数: {optimal_k}") # K-means分区 centroids, labels = kmeans2(points, optimal_k, minit=&#39;++&#39;) partition_mask = labels.reshape(CELL_SIZE, CELL_SIZE) # 计算每个分区的校正矩阵 correction_mats = {} for i in range(optimal_k): cluster_points = points[labels == i] if len(cluster_points) == 0: continue target = np.mean(cluster_points, axis=0) correction_mats[i] = np.linalg.lstsq(cluster_points, target, rcond=None)[0] return partition_mask, correction_mats # ====================== 异常像素补偿 ====================== def compensate_outliers(corrected_lab, deltaE_map, threshold=3.0): """基于色差的异常像素补偿""" mask = deltaE_map > threshold compensated = corrected_lab.copy() # 使用自适应窗口尺寸 for i in range(CELL_SIZE): for j in range(CELL_SIZE): if mask[i,j]: # 动态确定窗口大小 window_size = 3 if (i%8 < 6 and j%8 <6) else 5 i_min = max(0, i-window_size//2) i_max = min(CELL_SIZE, i+window_size//2+1) j_min = max(0, j-window_size//2) j_max = min(CELL_SIZE, j+window_size//2+1) # 加权中值滤波 neighborhood = corrected_lab[i_min:i_max, j_min:j_max] weights = 1.0 / (1e-6 + deltaE_map[i_min:i_max, j_min:j_max]) compensated[i,j] = np.average(neighborhood.reshape(-1,3), weights=weights.flatten(), axis=0) return compensated # ====================== 可视化模块 ====================== def plot_3d_gamut(lab_before, lab_after): """三维色域可视化""" fig = plt.figure(figsize=(12, 6)) # 校正前 ax1 = fig.add_subplot(121, projection=&#39;3d&#39;) ax1.scatter(lab_before[...,1], lab_before[...,2], lab_before[...,0], c=lab_before[...,0], cmap=&#39;viridis&#39;, alpha=0.5) ax1.set_xlabel(&#39;a*&#39;) ax1.set_ylabel(&#39;b*&#39;) ax1.set_zlabel(&#39;L*&#39;) ax1.set_title(&#39;校正前三维色域&#39;) # 校正后 ax2 = fig.add_subplot(122, projection=&#39;3d&#39;) sc = ax2.scatter(lab_after[...,1], lab_after[...,2], lab_after[...,0], c=lab_after[...,0], cmap=&#39;viridis&#39;, alpha=0.5) ax2.set_xlabel(&#39;a*&#39;) ax2.set_ylabel(&#39;b*&#39;) ax2.set_zlabel(&#39;L*&#39;) ax2.set_title(&#39;校正后三维色域&#39;) fig.colorbar(sc, ax=[ax1, ax2], label=&#39;亮度 L*&#39;) plt.tight_layout() return fig # ====================== 主流程 ====================== def main(): # 数据加载与预处理 targets, raw_data = load_data("B附件:RGB数值.xlsx") processed_data = preprocess_data(raw_data) # 颜色空间转换 xyz_data = rgb_to_xyz(processed_data.reshape(-1, 3)) lab_data = xyz_to_lab(xyz_data).reshape(CELL_SIZE, CELL_SIZE, 3) # 动态分区校正 partition_mask, correction_mats = dynamic_partition_correction(lab_data) # 应用校正 corrected_lab = np.zeros_like(lab_data) for i in range(CELL_SIZE): for j in range(CELL_SIZE): cluster_id = partition_mask[i,j] if cluster_id in correction_mats: corrected_lab[i,j] = np.dot(lab_data[i,j], correction_mats[cluster_id]) # 异常像素补偿 deltaE = delta_e_ciede2000(lab_data.reshape(-1,3), corrected_lab.reshape(-1,3)) deltaE_map = deltaE.reshape(CELL_SIZE, CELL_SIZE) final_lab = compensate_outliers(corrected_lab, deltaE_map) # 转换回RGB corrected_xyz = lab_to_xyz(final_lab.reshape(-1, 3)) corrected_rgb = gamma_correction(xyz_to_rgb(corrected_xyz)).reshape(CELL_SIZE, CELL_SIZE, 3) # 验证指标 final_deltaE = delta_e_ciede2000(lab_data.reshape(-1,3), final_lab.reshape(-1,3)) print(f"\n校正结果:") print(f"平均色差: {np.mean(final_deltaE):.4f}") print(f"最大色差: {np.max(final_deltaE):.4f}") print(f"色度均匀性: {np.std(final_deltaE.reshape(CELL_SIZE, CELL_SIZE)):.4f}") # 可视化 plt.figure(figsize=(18, 6)) # 色差热力图 plt.subplot(131) plt.imshow(deltaE_map, cmap=&#39;hot&#39;, vmin=0, vmax=5) plt.colorbar(label=&#39;ΔE2000&#39;) plt.title(&#39;校正色差分布&#39;) # 色域对比 plt.subplot(132) plt.scatter(lab_data[...,1], lab_data[...,2], c=&#39;r&#39;, alpha=0.3, label=&#39;校正前&#39;) plt.scatter(final_lab[...,1], final_lab[...,2], c=&#39;g&#39;, alpha=0.3, label=&#39;校正后&#39;) plt.xlabel(&#39;a*&#39;) plt.ylabel(&#39;b*&#39;) plt.title(&#39;二维色域对比&#39;) plt.legend() # 三维色域显示 plot_3d_gamut(lab_data, final_lab) plt.tight_layout() plt.savefig(&#39;calibration_result.png&#39;, dpi=300, bbox_inches=&#39;tight&#39;) if __name__ == &#39;__main__&#39;: main()
05-11
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值