Merge branch 'main' of https://git.zhaozuohong.vip/mower-ng/mower-ng

森空岛获取基本信息.py

@@ -0,0 +1,49 @@
+import json
+
+import requests
+
+from mower.utils import config
+from mower.utils.path import get_path
+from mower.utils.skland import (
+    get_binding_list,
+    get_cred_by_token,
+    get_sign_header,
+    header,
+    log,
+)
+
+
+class cultivate:
+    solver_name = "森空岛数据获取"
+
+    def __init__(self):
+        self.record_path = get_path("@app/tmp/cultivate.json")
+        self.reward = []
+        self.sign_token = ""
+        self.all_recorded = True
+
+    def start(self):
+        item = config.conf.skland_info[0]
+        if item.isCheck:
+            a = get_cred_by_token(log(item))
+            self.save_param(a)
+            print(a)
+            for i in get_binding_list(self.sign_token):
+                body = {"gameId": 1, "uid": i.get("uid")}
+                ingame = f"https://zonai.skland.com/api/v1/game/player/info?uid={i.get('uid')}"
+                resp = requests.get(
+                    ingame,
+                    headers=get_sign_header(ingame, "get", body, self.sign_token),
+                    timeout=5,
+                ).json()
+                print(resp)
+                with open(self.record_path, "w", encoding="utf-8") as file:
+                    json.dump(resp, file, ensure_ascii=False, indent=4)
+
+    def save_param(self, cred_resp):
+        header["cred"] = cred_resp["cred"]
+        self.sign_token = cred_resp["token"]
+
+
+a = cultivate()
+a.start()

水平拼接orb flann 自动识别重叠区域 无接缝修复.py

@@ -0,0 +1,142 @@
+import cv2
+import numpy as np
+from glob import glob
+
+
+class ImageStitcher:
+    def __init__(self, folder_path=None):
+        self.images = []  # 存储图像的列表
+        self.offsets = []  # 存储每对图像的偏移量
+        if folder_path:
+            self.load_images(folder_path)  # 如果提供了文件夹路径，则加载图像
+
+    def load_images(self, folder_path):
+        """加载指定文件夹中的所有PNG图像，并裁剪掉顶部300像素。"""
+        self.images = []
+        for item in glob(f"{folder_path}/*.png"):
+            img = cv2.imread(item)[300:]
+            if img is not None:
+                self.images.append(img)  # 裁剪掉顶部300像素
+            else:
+                print(f"Warning: Unable to load image {item}")
+
+    def calculate_offsets(self):
+        """计算每对相邻图像的偏移量。"""
+        if len(self.images) < 2:
+            raise ValueError("At least two images are required to calculate offsets.")
+
+        # 初始化 ORB 检测器
+        orb = cv2.ORB_create()
+
+        # 初始化 FLANN 匹配器（针对二进制描述符）
+        FLANN_INDEX_LSH = 6  # LSH 算法适用于二进制描述符
+        index_params = dict(
+            algorithm=FLANN_INDEX_LSH,
+            table_number=6,  # 哈希表数量
+            key_size=12,  # 哈希键大小
+            multi_probe_level=1,  # 多探针级别
+        )
+        search_params = dict(checks=20)  # 控制搜索精度，值越大越精确但越慢
+        flann = cv2.FlannBasedMatcher(index_params, search_params)
+
+        # 预计算所有图像的关键点和描述符
+        keypoints_and_descriptors = []
+        for img in self.images:
+            keypoints, descriptors = orb.detectAndCompute(img, None)
+            keypoints_and_descriptors.append((keypoints, descriptors))
+
+        self.offsets = []  # 清空偏移量
+        for i in range(len(self.images) - 1):
+            keypoints1, descriptors1 = keypoints_and_descriptors[i]
+            keypoints2, descriptors2 = keypoints_and_descriptors[i + 1]
+
+            # 使用 FLANN 进行特征匹配
+            matches = flann.knnMatch(descriptors1, descriptors2, k=2)
+
+            # 过滤匹配点，使用 Lowe's ratio test
+            good_matches = []
+            for match_pair in matches:
+                if len(match_pair) == 2:  # 确保每个匹配对有两个点
+                    m, n = match_pair
+                    if m.distance < 0.8 * n.distance:  # Lowe's ratio test
+                        good_matches.append(m)
+
+            # 至少需要 4 个匹配点来计算单应性矩阵
+            if len(good_matches) >= 4:
+                src_pts = np.float32(
+                    [keypoints1[m.queryIdx].pt for m in good_matches]
+                ).reshape(-1, 1, 2)
+                dst_pts = np.float32(
+                    [keypoints2[m.trainIdx].pt for m in good_matches]
+                ).reshape(-1, 1, 2)
+
+                # 计算单应性矩阵
+                H, _ = cv2.findHomography(dst_pts, src_pts, cv2.RANSAC, 5.0)
+
+                # 计算 curr_img 的四个角在 prev_img 坐标系中的位置
+                h, w = self.images[i + 1].shape[:2]
+                corners = np.float32(
+                    [[0, 0], [0, h - 1], [w - 1, h - 1], [w - 1, 0]]
+                ).reshape(-1, 1, 2)
+                warped_corners = cv2.perspectiveTransform(corners, H)
+
+                # 计算偏移量
+                offset_x = int(warped_corners[0][0][0])  # 左上角的 x 坐标
+                offset_y = int(warped_corners[0][0][1])  # 左上角的 y 坐标
+                self.offsets.append((offset_x, offset_y))
+            else:
+                raise ValueError(
+                    f"Not enough matches found between image {i} and image {i + 1}."
+                )
+
+    def stitch_images(self):
+        """根据偏移量统一拼接所有图像。"""
+        if not self.images:
+            raise ValueError("No images provided for stitching.")
+        if not self.offsets:
+            self.calculate_offsets()  # 如果没有计算偏移量，则先计算
+
+        # 计算每张图像在最终拼接图像中的绝对位置
+        absolute_positions = [(0, 0)]  # 第一张图像的绝对位置为 (0, 0)
+        for i in range(1, len(self.images)):
+            prev_x, prev_y = absolute_positions[i - 1]
+            offset_x, offset_y = self.offsets[i - 1]
+            absolute_positions.append((prev_x + offset_x, prev_y + offset_y))
+
+        # 计算最终拼接图像的大小
+        min_x = min(pos[0] for pos in absolute_positions)
+        min_y = min(pos[1] for pos in absolute_positions)
+        max_x = max(
+            pos[0] + img.shape[1] for pos, img in zip(absolute_positions, self.images)
+        )
+        max_y = max(
+            pos[1] + img.shape[0] for pos, img in zip(absolute_positions, self.images)
+        )
+
+        # 创建空白图像用于存储拼接结果
+        stitched_image = np.zeros((max_y - min_y, max_x - min_x, 3), dtype=np.uint8)
+
+        # 将每张图像放置到正确的位置
+        for img, (x, y) in zip(self.images, absolute_positions):
+            x -= min_x
+            y -= min_y
+            stitched_image[y : y + img.shape[0], x : x + img.shape[1]] = img
+
+        return stitched_image
+
+
+def main():
+    folder_path = "./img"  # 图像文件夹路径
+
+    # 创建 ImageStitcher 实例并加载图像
+    stitcher = ImageStitcher(folder_path)
+
+    # 拼接图像
+    stitched_image = stitcher.stitch_images()
+
+    # 保存结果
+    cv2.imwrite("result.png", stitched_image)
+
+
+if __name__ == "__main__":
+    main()