数字图像处理行人检测与前景背景分割

1 files changed, 41 insertions, 0 deletions

diff --git a/dip/segmentation/grab_cut.py b/dip/segmentation/grab_cut.py
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+++ b/dip/segmentation/grab_cut.py
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+import cv2
+import numpy as np
+
+src = cv2.imread("sample_person.jpg")
+src = cv2.resize(src, (0, 0), fx=0.5, fy=0.5)
+
+# 交互式，返回 (x_min, y_min, w, h)
+r = cv2.selectROI('input', src, True)
+
+# roi区域
+roi = src[int(r[1]):int(r[1] + r[3]), int(r[0]):int(r[0] + r[2])]
+
+# 原图mask，与原图等大小
+mask = np.zeros(src.shape[:2], dtype=np.uint8)
+
+# 矩形roi
+rect = (int(r[0]), int(r[1]), int(r[2]), int(r[3]))  # 包括前景的矩形，格式为(x,y,w,h)
+
+# bg模型的临时数组
+bgdmodel = np.zeros((1, 65), np.float64)
+# fg模型的临时数组
+fgdmodel = np.zeros((1, 65), np.float64)
+
+cv2.grabCut(src, mask, rect, bgdmodel, fgdmodel, 11, mode=cv2.GC_INIT_WITH_RECT)
+
+print(np.unique(mask))
+# 提取前景和可能的前景区域
+mask2 = np.where((mask == 1) | (mask == 3), 255, 0).astype('uint8')
+
+print(mask2.shape)
+
+# 按位与 src & src == 0，得到的是二进制
+result = cv2.bitwise_and(src, src, mask=mask2)
+# cv2.imwrite('result.jpg', result)
+# cv2.imwrite('roi.jpg', roi)
+
+cv2.imshow('mask', mask2)
+cv2.imshow('roi', roi)
+cv2.imshow("result", result)
+cv2.waitKey(0)
+cv2.destroyAllWindows()