增加单应性变换

data/1.OpenCV初阶/4.几何变换和图像特征/2.单应性变换/rust_face.md

+# 还原挤地铁的参赛作品
+
+下图左边的参赛作品（图片来自网络）掉到地上，重新做太麻烦了，据说改了个《挤地铁》的名字获奖了。
+
+![](./rust_face_origin.jpeg)
+
+我们可以通过 OpenCV 单应性变换的方式帮助挤地铁的人脸还原。
+
+把没挤扁和挤扁的图的坐标标注出来
+
+![](./rust_face_rule.jpg)
+
+可以看到还原后的侧脸
+
+![](./rust_face_homography.jpeg)
+
+以下正确的实现是
+
+## 答案
+
+```python
+import cv2
+import numpy as np
+
+if __name__ == '__main__':
+    img_src = cv2.imread('rust_face_src.jpeg')
+    pts_src = np.array([[0, 0], [570, 0], [570, 1078], [0, 1078]])
+
+    img_dst = cv2.imread('rust_face_dest.jpeg')
+    pts_dst = np.array([[63, 285], [378, 224], [427, 689], [84, 820]])
+
+    dw, dh = img_dst.shape[1], img_dst.shape[0]
+
+    h, status = cv2.findHomography(pts_src, pts_dst)
+    img_out = cv2.warpPerspective(img_src, h, (dw, dh))
+
+    images = np.concatenate((img_src[0: dh, 0:dw, 0:3], img_dst, img_out), axis=1)
+    cv2.imshow('homography', images)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+```
+
+## 选项
+
+### 映射位置不匹配
+
+```python
+import cv2
+import numpy as np
+
+if __name__ == '__main__':
+    img_src = cv2.imread('rust_face_src.jpeg')
+    pts_src = np.array([[0, 0], [0, 1078], [570, 0], [570, 1078]])
+
+    img_dst = cv2.imread('rust_face_dest.jpeg')
+    pts_dst = np.array([[63, 285], [378, 224], [427, 689], [84, 820]])
+
+    dw, dh = img_dst.shape[1], img_dst.shape[0]
+
+    h, status = cv2.findHomography(pts_src, pts_dst)
+    img_out = cv2.warpPerspective(img_src, h, (dw, dh))
+
+    images = np.concatenate((img_src[0: dh, 0:dw, 0:3], img_dst, img_out), axis=1)
+    cv2.imshow('homography', images)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+```
+
+### 源图和目标图片错位
+
+```python
+import cv2
+import numpy as np
+
+if __name__ == '__main__':
+    img_src = cv2.imread('rust_face_src.jpeg')
+    pts_src = np.array([[0, 0], [0, 1078], [570, 0], [570, 1078]])
+
+    img_dst = cv2.imread('rust_face_dest.jpeg')
+    pts_dst = np.array([[63, 285], [378, 224], [427, 689], [84, 820]])
+
+    dw, dh = img_dst.shape[1], img_dst.shape[0]
+
+    h, status = cv2.findHomography(pts_dst, pts_src)
+    img_out = cv2.warpPerspective(img_src, h, (dw, dh))
+
+    images = np.concatenate((img_src[0: dh, 0:dw, 0:3], img_dst, img_out), axis=1)
+    cv2.imshow('homography', images)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+```
+
+### 目标长宽用错
+
+```python
+import cv2
+import numpy as np
+
+if __name__ == '__main__':
+    img_src = cv2.imread('rust_face_src.jpeg')
+    pts_src = np.array([[0, 0], [0, 1078], [570, 0], [570, 1078]])
+
+    img_dst = cv2.imread('rust_face_dest.jpeg')
+    pts_dst = np.array([[63, 285], [378, 224], [427, 689], [84, 820]])
+
+    dw, dh = img_dst.shape[1], img_dst.shape[0]
+
+    h, status = cv2.findHomography(pts_dst, pts_src)
+    img_out = cv2.warpPerspective(img_src, h, (dh, dw))
+
+    images = np.concatenate((img_src[0: dh, 0:dw, 0:3], img_dst, img_out), axis=1)
+    cv2.imshow('homography', images)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+```

data/1.OpenCV初阶/4.几何变换和图像特征/2.单应性变换/rust_face.py

+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+
+if __name__ == '__main__':
+    # img_src = cv2.imread('rust_face_src.jpeg')
+    # pts_src = np.array([[0, 0], [570, 0], [570, 1078], [0, 1078]])
+
+    # img_dst = cv2.imread('rust_face_dest.jpeg')
+    # pts_dst = np.array([[63, 285], [378, 224], [427, 689], [84, 820]])
+
+    img_src = cv2.imread('rust_face_src.jpeg')
+    pts_src = np.array([[0, 0], [0, 1078], [570, 0], [570, 1078]])
+
+    img_dst = cv2.imread('rust_face_dest.jpeg')
+    pts_dst = np.array([[63, 285], [84, 820], [378, 224], [427, 689]])
+
+    dw, dh = img_dst.shape[1], img_dst.shape[0]
+
+    h, status = cv2.findHomography(pts_src, pts_dst)
+    img_out = cv2.warpPerspective(img_src, h, (dw, dh))
+
+    images = np.concatenate(
+        (img_src[0: dh, 0:dw, 0:3], img_dst, img_out), axis=1)
+    cv2.imshow('homography', images)
+
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()