白平衡：即白色的平衡，最早用于摄像领域技术，可以用来解决色彩还原和调处理的一系列问题。
网上参考别人python版白平衡的运算，索性自己优化了下代码。
用numpy矩阵运算取代原有的通道运算，提高运行速度。
tip：用python图像处理尽可能用numpy运算（分通道写法会使处理速度变慢，特别是较大图片），而在C++中的图像处理需要分通道处理。
下面是优化过整理过的代码：
def WhiteBlance(img, mode=1): """白平衡处理（默认为1均值、2完美反射、3灰度世界、4基于图像分析的偏色检测及颜色校正、5动态阈值）""" # 读取图像 b, g, r = cv2.split(img) # 均值变为三通道 h, w, c = img.shape if mode == 2: # 完美反射白平衡 ---- 依赖ratio值选取而且对亮度最大区域不是白色的图像效果不佳。 output_img = img.copy() sum_ = np.double() + b + g + r hists, bins = np.histogram(sum_.flatten(), 766, [0, 766]) Y = 765 num, key = 0, 0 ratio = 0.01 while Y >= 0: num += hists[Y] if num > h * w * ratio / 100: key = Y break Y = Y - 1 sumkey = np.where(sum_ >= key) sum_b, sum_g, sum_r = np.sum(b[sumkey]), np.sum(g[sumkey]), np.sum(r[sumkey]) times = len(sumkey[0]) avg_b, avg_g, avg_r = sum_b / times, sum_g / times, sum_r / times maxvalue = float(np.max(output_img)) output_img[:, :, 0] = output_img[:, :, 0] * maxvalue / int(avg_b) output_img[:, :, 1] = output_img[:, :, 1] * maxvalue / int(avg_g) output_img[:, :, 2] = output_img[:, :, 2] * maxvalue / int(avg_r) elif mode == 3: # 灰度世界假设 b_avg, g_avg, r_avg = cv2.mean(b)[0], cv2.mean(g)[0], cv2.mean(r)[0] # 需要调整的RGB分量的增益 k = (b_avg + g_avg + r_avg) / 3 kb, kg, kr = k / b_avg, k / g_avg, k / r_avg ba, ga, ra = b * kb, g * kg, r * kr output_img = cv2.merge([ba, ga, ra]) elif mode == 4: # 基于图像分析的偏色检测及颜色校正 I_b_2, I_r_2 = np.double(b) ** 2, np.double(r) ** 2 sum_I_b_2, sum_I_r_2 = np.sum(I_b_2), np.sum(I_r_2) sum_I_b, sum_I_g, sum_I_r = np.sum(b), np.sum(g), np.sum(r) max_I_b, max_I_g, max_I_r = np.max(b), np.max(g), np.max(r) max_I_b_2, max_I_r_2 = np.max(I_b_2), np.max(I_r_2) [u_b, v_b] = np.matmul(np.linalg.inv([[sum_I_b_2, sum_I_b], [max_I_b_2, max_I_b]]), [sum_I_g, max_I_g]) [u_r, v_r] = np.matmul(np.linalg.inv([[sum_I_r_2, sum_I_r], [max_I_r_2, max_I_r]]), [sum_I_g, max_I_g]) b0 = np.uint8(u_b * (np.double(b) ** 2) + v_b * b) r0 = np.uint8(u_r * (np.double(r) ** 2) + v_r * r) output_img = cv2.merge([b0, g, r0]) elif mode == 5: # 动态阈值算法 ---- 白点检测和白点调整 # 只是白点检测不是与完美反射算法相同的认为最亮的点为白点，而是通过另外的规则确定 def con_num(x): if x > 0: return 1 if x < 0: return -1 if x == 0: return 0 yuv_img = cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb) # YUV空间 (y, u, v) = cv2.split(yuv_img) max_y = np.max(y.flatten()) sum_u, sum_v = np.sum(u), np.sum(v) avl_u, avl_v = sum_u / (h * w), sum_v / (h * w) du, dv = np.sum(np.abs(u - avl_u)), np.sum(np.abs(v - avl_v)) avl_du, avl_dv = du / (h * w), dv / (h * w) radio = 0.5 # 如果该值过大过小，色温向两极端发展 valuekey = np.where((np.abs(u - (avl_u + avl_du * con_num(avl_u))) < radio * avl_du) | (np.abs(v - (avl_v + avl_dv * con_num(avl_v))) < radio * avl_dv)) num_y, yhistogram = np.zeros((h, w)), np.zeros(256) num_y[valuekey] = np.uint8(y[valuekey]) yhistogram = np.bincount(np.uint8(num_y[valuekey].flatten()), minlength=256) ysum = len(valuekey[0]) Y = 255 num, key = 0, 0 while Y >= 0: num += yhistogram[Y] if num > 0.1 * ysum: # 取前10%的亮点为计算值，如果该值过大易过曝光，该值过小调整幅度小 key = Y break Y = Y - 1 sumkey = np.where(num_y > key) sum_b, sum_g, sum_r = np.sum(b[sumkey]), np.sum(g[sumkey]), np.sum(r[sumkey]) num_rgb = len(sumkey[0]) b0 = np.double(b) * int(max_y) / (sum_b / num_rgb) g0 = np.double(g) * int(max_y) / (sum_g / num_rgb) r0 = np.double(r) * int(max_y) / (sum_r / num_rgb) output_img = cv2.merge([b0, g0, r0]) else: # 默认均值 ---- 简单的求均值白平衡法 b_avg, g_avg, r_avg = cv2.mean(b)[0], cv2.mean(g)[0], cv2.mean(r)[0] # 求各个通道所占增益 k = (b_avg + g_avg + r_avg) / 3 kb, kg, kr = k / b_avg, k / g_avg, k / r_avg b = cv2.addWeighted(src1=b, alpha=kb, src2=0, beta=0, gamma=0) g = cv2.addWeighted(src1=g, alpha=kg, src2=0, beta=0, gamma=0) r = cv2.addWeighted(src1=r, alpha=kr, src2=0, beta=0, gamma=0) output_img = cv2.merge([b, g, r]) output_img = np.uint8(np.clip(output_img, 0, 255)) return output_img
以下是白平衡效果图
(原图) (mode=1 均值) (mode=2 完美反射)
(mode=3 灰度世界) (mode=4 颜色校正) (mode=5 动态阈值)
处理的时间情况
1.均值：20ms
2.完美反射：20ms
3.灰度世界：20ms
4.基于图像分析的偏色检测及颜色校正：20ms
5.动态阈值算法:30ms
目前只针对网络上的算法进行运算优化，待进行图像分析及算法研究。