Add batch method and try to use k means

doc/orb解析/orb解析.md

@@ -72,6 +72,7 @@ S_{\theta} = R_{\theta}S
 $$
 
 其中的$S$为选取的点对（$x_n$与$y_n$是一个点对两个点的灰度值），$R_{\theta}$为旋转不变性（公式（4））求得的旋转角度。
+
 $$
 S =\begin{pmatrix}x_1&x_2&\cdots&x_{2n} \\ y_1&y_2&\cdots&y_{2n}\end{pmatrix}
 $$
@@ -176,6 +177,7 @@ HarrisResponses(const Mat& img, const std::vector<Rect>& layerinfo,
 ```
 
 代码最后几行即为计算我们公式(1)的具体计算，其中用到了这样的公式,即在矩阵$\boldsymbol{M}=\begin{bmatrix}A&C\\C&B\end{bmatrix}$中：
+
 $$
 det\boldsymbol{M} = \lambda_1\lambda_2=AB-C^2
 $$
@@ -293,4 +295,4 @@ computeOrbDescriptors( const Mat& imagePyramid, const std::vector<Rect>& layerIn
 
 
 
-（三十二个为一个描述子，图中并未截全一个描述子）
+（三十二个为一个描述子，图中并未截全一个描述子）s

py/batch_stich.py

@@ -0,0 +1,44 @@
+# -*- coding: utf-8 -*-
+"""
+Created on 2018-04-10 20:11:11
+@Author: ZHAO Lingfeng
+@Version : 0.0.1
+"""
+import glob
+import os
+
+import cv2
+from stich import Matcher, Sticher, Method
+
+
+def main():
+    import time
+    # main()
+    os.chdir(os.path.dirname(__file__))
+
+    number = 9
+    file1 = "../resource/{}-right*.jpg".format(number)
+    file2 = "../resource/{}-left.jpg".format(number)
+
+    start_time = time.time()
+    for method in (Method.SIFT, Method.ORB):
+
+        for f in glob.glob(file1):
+            print(f)
+            name = f.replace('right', method.name)
+            # print(file2, name)
+
+            img2 = cv2.imread(file2)
+            img1 = cv2.imread(f)
+            matcher = Matcher(img1, img2, method=method)
+            matcher.match(max_match_lenth=20, show_match=False,)
+            sticher = Sticher(img1, img2, matcher)
+            sticher.stich(show_result=False)
+            cv2.imwrite(name, sticher.image)
+            print("Time: ", time.time() - start_time)
+            # print("M: ", sticher.M)
+    print('\a')
+
+
+if __name__ == "__main__":
+    main()

py/k_means.py

@@ -0,0 +1,100 @@
+# -*- coding: utf-8 -*-
+"""
+Created on 2018-04-02 22:12:12
+@Author: ZHAO Lingfeng
+@Version : 0.0.1
+"""
+from typing import Tuple
+
+import cv2
+import numpy as np
+
+
+def difference(array: np.ndarray):
+    x = []
+    for i in range(len(array) - 1):
+        x.append(array[i + 1] - array[i])
+
+    return np.array(x)
+
+
+def find_peek(array: np.ndarray):
+    peek = difference(difference(array))
+    # print(peek)
+    peek_pos = np.argmax(peek) + 2
+    return peek_pos
+
+
+def k_means(points: np.ndarray):
+    """返回一个数组经kmeans分类后的k值以及标签，k值由计算拐点给出
+
+    Args:
+        points (np.ndarray): 需分类数据
+
+    Returns:
+        Tuple[int, np.ndarry]: k值以及标签数组
+    """
+
+    # Define criteria = ( type, max_iter = 10 , epsilon = 1.0 )
+    criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
+
+    # Set flags (Just to avoid line break in the code)
+    flags = cv2.KMEANS_RANDOM_CENTERS
+    length = []
+    max_k = min(10, points.shape[0])
+    for k in range(2, max_k + 1):
+        avg = 0
+        for i in range(5):
+            compactness, _, _ = cv2.kmeans(
+                points, k, None, criteria, 10, flags)
+            avg += compactness
+        avg /= 5
+        length.append(avg)
+
+    peek_pos = find_peek(length)
+    k = peek_pos + 2
+    # print(k)
+    return k, cv2.kmeans(points, k, None, criteria, 10, flags)[1]  # labels
+
+
+def get_group_center(points1: np.ndarray, points2: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+    """输入两个相对应的点对数组，返回经kmeans优化后的两个数组
+
+    Args:
+        points1 (np.ndarray): 数组一
+        points2 (np.ndarray): 数组二
+
+    Returns:
+        Tuple[np.ndarray, np.ndarray]: 两数组
+    """
+
+    k, labels = k_means(points1)
+    labels = labels.flatten()
+    selected_centers1 = []
+    selected_centers2 = []
+    for i in range(k):
+        center1 = np.mean(points1[labels == i], axis=0)
+        center2 = np.mean(points2[labels == i], axis=0)
+        # center1 = points1[labels == i][0]
+        # center2 = points2[labels == i][0]
+
+        selected_centers1.append(center1)
+        selected_centers2.append(center2)
+
+    selected_centers1, selected_centers2 = np.array(
+        selected_centers1), np.array(selected_centers2)
+
+    # return selected_centers1, selected_centers2
+    # return np.append(selected_centers1, points1, axis=0), np.append(selected_centers2, points2, axis=0)
+    return points1, points2
+
+
+def main():
+    x = np.array([[1, 1], [1, 2], [2, 2], [3, 3]], dtype=np.float32)
+    y = np.array([[1, 1], [1, 2], [2, 2], [3, 3]], dtype=np.float32)
+    print(get_group_center(x, y))
+    pass
+
+
+if __name__ == "__main__":
+    main()

py/stich.py

@@ -14,6 +14,8 @@
 import cv2
 import numpy as np
 
+import k_means
+
 
 def show_image(image: np.ndarray) -> None:
     from PIL import Image
@@ -102,6 +104,8 @@ def match(self, max_match_lenth=20, threshold=0.04, show_match=False):
             self._descriptors1, self._descriptors2), key=lambda x: x.distance)
 
         match_len = min(len(self.match_points), max_match_lenth)
+        if self.method == Method.ORB:
+            threshold = 20
         min_distance = max(2 * self.match_points[0].distance, threshold)
 
         for i in range(match_len):
@@ -130,22 +134,38 @@ def match(self, max_match_lenth=20, threshold=0.04, show_match=False):
         # print(image_points1)
 
 
+def get_weighted_points(image_points: np.ndarray):
+
+    # print(k_means.k_means(image_points))
+    # exit(0)
+
+    average = np.average(image_points, axis=0)
+
+    max_index = np.argmax(np.linalg.norm((image_points - average), axis=1))
+    return np.append(image_points, np.array([image_points[max_index]]), axis=0)
+
+
 class Sticher:
 
-    def __init__(self, image1: np.ndarray, image2: np.ndarray, matcher: Matcher):
+    def __init__(self, image1: np.ndarray, image2: np.ndarray, matcher: Matcher, use_kmeans=False):
         """输入图像和匹配，对图像进行拼接
         目前采用简单矩阵匹配和平均值拼合
 
         Args:
             image1 (np.ndarray): 图像一
             image2 (np.ndarray): 图像二
             matcher (Matcher): 匹配结果
+            use_kmeans (bool): 是否使用kmeans 优化点选择
         """
 
         self.image1 = image1
         self.image2 = image2
-        self.image_points1 = matcher.image_points1
-        self.image_points2 = matcher.image_points2
+        if use_kmeans:
+            self.image_points1, self.image_points2 = k_means.get_group_center(
+                matcher.image_points1, matcher.image_points2)
+        else:
+            self.image_points1, self.image_points2 = (
+                matcher.image_points1, matcher.image_points2)
 
         self.M = np.eye(3)
 
@@ -165,7 +185,8 @@ def stich(self, show_result=True, show_match_point=True):
         # print(self.get_transformed_size())
         width = int(max(right, self.image2.shape[1]) - min(left, 0))
         height = int(max(bottom, self.image2.shape[0]) - min(top, 0))
-        # print(width, height)
+        print(width, height)
+        width, height = min(width, 10000), min(height, 10000)
 
         # 移动矩阵
         adjustM = np.array(
@@ -186,11 +207,11 @@ def stich(self, show_result=True, show_match_point=True):
             for point in self.image_points1:
                 point = self.get_transformed_position(tuple(point))
                 point = tuple(map(int, point))
-                cv2.circle(self.image, point, 10, (20, 20, 255))
+                cv2.circle(self.image, point, 10, (20, 20, 255), 5)
             for point in self.image_points2:
                 point = self.get_transformed_position(tuple(point), M=adjustM)
                 point = tuple(map(int, point))
-                cv2.circle(self.image, point, 8, (20, 200, 20))
+                cv2.circle(self.image, point, 8, (20, 200, 20), 5)
         if show_result:
             show_image(self.image)
 
@@ -209,7 +230,7 @@ def blend(self, image1: np.ndarray, image2: np.ndarray) -> np.ndarray:
         # result[0:image2.shape[0], 0:self.image2.shape[1]] = self.image2
         # result = np.maximum(transformed, result)
 
-        # result = cv2.addWeighted(transformed, 0.5, result, 0.5, 1)
+        # result = cv2.addWeighted(image1, 0.5, image2, 0.5, 1)
         result = self.average(image1, image2)
 
         return result
@@ -235,7 +256,8 @@ def average(self, image1: np.ndarray, image2: np.ndarray) -> np.ndarray:
         )
         # 重叠处用平均值
         result[overlap] = np.average(
-            np.array([image1[overlap], image2[overlap]]), axis=0) .astype(np.uint8)
+            np.array([image1[overlap], image2[overlap]]), axis=0
+        ) .astype(np.uint8)
         # 非重叠处采选最大值
         not_overlap = np.logical_not(overlap)
         result[not_overlap] = np.maximum(
@@ -365,12 +387,14 @@ def main():
     os.chdir(os.path.dirname(__file__))
 
     start_time = time.time()
-    img1 = cv2.imread("../resource/3-left.jpg")
-    img2 = cv2.imread("../resource/3-right.jpg")
+    img2 = cv2.imread("../resource/15-left.jpg")
+    img1 = cv2.imread("../resource/15-right.jpg")
     matcher = Matcher(img1, img2, Method.ORB)
-    matcher.match(show_match=True)
-    sticher = Sticher(img1, img2, matcher)
+    matcher.match(max_match_lenth=20, show_match=True,)
+    sticher = Sticher(img1, img2, matcher, False)
     sticher.stich()
-    cv2.imwrite('../resource/3-orb.jpg', sticher.image)
+
+    # cv2.imwrite('../resource/15-orb.jpg', sticher.image)
+
     print("Time: ", time.time() - start_time)
     print("M: ", sticher.M)