feat generate 新增色阶调整 亮度调整

app/service/generate_image/utils/image_processing.py

@@ -207,6 +207,87 @@ def generate_category_recognition(image, gender):
     return category, scores, image
 
 
+import cv2
+import numpy as np
+
+
+def autoLevels(img, cutoff=0.1):
+    channels = img.shape[2]  # h,w,ch
+    table = np.zeros((1, 256, 3), np.uint8)
+    for ch in range(channels):
+        # cutoff=0.1, 计算 0.1%, 99.9% 分位的灰度值
+        low = np.percentile(img[:, :, ch], q=cutoff)  # ch 通道, cutoff=0.1, 0.1 分位的灰度值
+        high = np.percentile(img[:, :, ch], q=100 - cutoff)  # 99.9 分位的灰度值, [0, high] 占比99.9%
+        # 输入动态线性拉伸
+        Sin = min(max(low, 0), high - 2)  # Sin, 黑场阈值, 0<=Sin<Hin
+        Hin = min(high, 255)  # Hin, 白场阈值, Sin<Hin<=255
+        difIn = Hin - Sin
+        V1 = np.array([(min(max(255 * (i - Sin) / difIn, 0), 255)) for i in range(256)])
+        # 灰场伽马调节
+        gradMed = np.median(img[:, :, ch])  # 拉伸前的中值
+        Mt = V1[int(gradMed)] / 128.  # 拉伸后的映射值
+        V2 = 255 * np.power(V1 / 255, 1 / Mt)  # 伽马调节
+        # 输出线性拉伸
+        Sout, Hout = 5, 250  # Sout 输出黑场阈值, Hout 输出白场阈值
+        difOut = Hout - Sout
+        table[0, :, ch] = np.array([(min(max(Sout + difOut * V2[i] / 255, 0), 255)) for i in range(256)])
+    return cv2.LUT(img, table)
+
+
+def luminance_adjust(alpha, img):
+    if alpha > 0:
+        img_out = img * (1 - alpha) + alpha * 255.0
+    else:
+        img_out = img * (1 + alpha)
+
+    return np.array(img_out, dtype='uint8')
+
+    # 14.14 Photoshop 自动色阶调整算法
+
+
 if __name__ == '__main__':
+    # Photoshop 自动色阶调整算法
+    img = cv2.imread("2.png", flags=1)  # 读取彩色图像
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  # 转换为灰度图像
+    print("cutoff={}, minG={}, maxG={}".format(0.0, gray.min(), gray.min()))
+
+    # 色阶手动调整
+    # equManual = levelsAdjust(img, 63, 205, 0.8, 10, 245)  # 手动调节
+    # 色阶自动调整
+    cutoff = 1.0  # 截断比例, 建议范围 [0.0,1.0]
+    # cv2.imwrite("source.png", img)
+    equAuto = autoLevels(img, cutoff)
+    # cv2.imwrite("levels.png", equAuto)
+    luminance = luminance_adjust(0.3, equAuto)
+    # cv2.imwrite("luminance.png", luminance)
+    #
+    # # 将图像转换为灰度
+    # gray = cv2.cvtColor(luminance, cv2.COLOR_BGR2GRAY)
+    #
+    # # 使用Canny边缘检测算法检测图像的边缘
+    # edges = cv2.Canny(gray, 150, 200)
+    #
+    # # 对边缘进行膨胀操作，增强轮廓
+    # kernel = np.ones((1, 1), np.uint8)
+    # dilated_edges = cv2.dilate(edges, kernel, iterations=1)
+    #
+    # # 创建一个与原始图像相同大小的空白图像
+    # # result = np.zeros_like(luminance)
+    #
+    # # 将增强后的轮廓叠加到原始图像上
+    # luminance[dilated_edges != 0] = (255, 255, 255)
+
+    remove_bg_img = remove_background(luminance)
+    # cv2.imwrite("remove_bg_img.png", remove_bg_img)
+
+    print(1)
+    cv2.imshow("source", img)
+    cv2.imshow("levels", equAuto)
+    cv2.imshow("luminance", luminance)
+    # cv2.imshow("dilated_edges", luminance)
+    cv2.imshow("remove_bg_img", remove_bg_img)
+
+    cv2.waitKey(0)
+
     image = cv2.imread("1.png")
     remove_background(image)