# gameauto
## Core
### miniFindimage
"""找图函数示例
Args:
- template_path (Str/Mat): 图像地址或者opencv格式的图像 小图
- target_path (Str/Mat): 图像地址或者opencv格式的图像 大图
- region (list, optional): 模板匹配的区域左上角和右下角两点坐标[Xmin,Ymin,Xmax,Ymax]. Defaults to None.
- threshold (float, optional): 图像相似度. Defaults to 0.8.
- is_color (bool, optional): 是否匹配图像颜色. Defaults to False.
- color_threshold (int, optional): 颜色相似度. Defaults to 30.
- use_sift (bool, optional): 是否使用sift算法匹配. Defaults to False.
- is_click (bool, optional): 是否点击图像. Defaults to False.
Returns:
list/None: 图像所在区域[Xmin,Ymin,Xmax,Ymax]
"""
```python
#推荐使用方法
args={
"template_path":"./1.png",
"target_path":"./2.png",
}
miniFindImage(**args,is_click=True)
```
### miniRandomClick
```
随机点击 建议重写
Args:
- region (array): [x_min, y_min, x_max, y_max]
- point (tuple, optional): 坐标点. Defaults to None.
- method (int, optional): Defaults to 1.
1. 范围随机点击
2. 定点点击.
```
### miniSmlMove
曲线滑动
```
三次贝塞尔曲线滑动 建议重写
Args:
- qx (int): 起点
- qy (int):
- zx (int): 终点
- zy (int):
- time (int, optional): 滑动时长. Defaults to 500.
```
## YoloV3
ncnn加载yolov3模型
Args:
- param_path (str): yolov3模型param地址
- bin_path (str): yolov3模型bin地址
- class_names (list): 类名列表
- tiny (bool, optional): 是否启用tiny模型. Defaults to False.
- num_threads (int, optional): 启用线程数量. Defaults to 1.
- use_gpu (bool, optional): 是否启用gpu计算 Defaults to False.
### yolo_filter
模型计算结果筛选
Args:
- class_name (str): 筛选的类名
- class_names (list): 模型类名列表
- objects (list): 模型计算返回的结果
- min_prob (float, optional): 结果置信度阈值. Defaults to 0.0.
Returns:
- list: objects
- object:
- rect.x 左上角横坐标
- rect.y 左上角纵坐标
- rect.w 区域宽
- rect.h 区域高
- prob 置信度
- label 类名
### yolo使用方法
```python
m = cv2.imread("screenshotemulator-5554.png")
#加载模型
net = YoloV3(
param_path="yolov3-tiny_6700-opt.param",
bin_path="yolov3-tiny-opt.bin",
#类名此处记得将第一个元素留空,所有元素往后一个(ncnn的问题好像是(有空改改))
class_names=["", "exp", "hd", "jb"],
num_threads=4,
use_gpu=True,
)
#输入mat格式图像进行计算
objects = net(m)
#过滤结果
yolo_filter("exp",net.class_names,objects,0.8)
```
Raw data
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"description": "# gameauto\r\n## Core\r\n### miniFindimage\r\n \"\"\"\u627e\u56fe\u51fd\u6570\u793a\u4f8b\r\n Args:\r\n - template_path (Str/Mat): \u56fe\u50cf\u5730\u5740\u6216\u8005opencv\u683c\u5f0f\u7684\u56fe\u50cf \u5c0f\u56fe\r\n - target_path (Str/Mat): \u56fe\u50cf\u5730\u5740\u6216\u8005opencv\u683c\u5f0f\u7684\u56fe\u50cf \u5927\u56fe\r\n - region (list, optional): \u6a21\u677f\u5339\u914d\u7684\u533a\u57df\u5de6\u4e0a\u89d2\u548c\u53f3\u4e0b\u89d2\u4e24\u70b9\u5750\u6807[Xmin,Ymin,Xmax,Ymax]. Defaults to None.\r\n - threshold (float, optional): \u56fe\u50cf\u76f8\u4f3c\u5ea6. Defaults to 0.8.\r\n - is_color (bool, optional): \u662f\u5426\u5339\u914d\u56fe\u50cf\u989c\u8272. Defaults to False.\r\n - color_threshold (int, optional): \u989c\u8272\u76f8\u4f3c\u5ea6. Defaults to 30.\r\n - use_sift (bool, optional): \u662f\u5426\u4f7f\u7528sift\u7b97\u6cd5\u5339\u914d. Defaults to False.\r\n - is_click (bool, optional): \u662f\u5426\u70b9\u51fb\u56fe\u50cf. Defaults to False.\r\n \r\n Returns:\r\n list/None: \u56fe\u50cf\u6240\u5728\u533a\u57df[Xmin,Ymin,Xmax,Ymax]\r\n \"\"\"\r\n\r\n```python\r\n#\u63a8\u8350\u4f7f\u7528\u65b9\u6cd5\r\nargs={\r\n \"template_path\":\"./1.png\",\r\n \"target_path\":\"./2.png\",\r\n}\r\nminiFindImage(**args,is_click=True)\r\n```\r\n\r\n### miniRandomClick\r\n```\r\n\u968f\u673a\u70b9\u51fb \u5efa\u8bae\u91cd\u5199\r\n Args:\r\n - region (array): [x_min, y_min, x_max, y_max]\r\n - point (tuple, optional): \u5750\u6807\u70b9. Defaults to None.\r\n - method (int, optional): Defaults to 1.\r\n 1. \u8303\u56f4\u968f\u673a\u70b9\u51fb\r\n 2. \u5b9a\u70b9\u70b9\u51fb. \r\n \r\n```\r\n### miniSmlMove\r\n\u66f2\u7ebf\u6ed1\u52a8\r\n```\r\n\u4e09\u6b21\u8d1d\u585e\u5c14\u66f2\u7ebf\u6ed1\u52a8 \u5efa\u8bae\u91cd\u5199\r\n Args:\r\n - qx (int): \u8d77\u70b9\r\n - qy (int): \r\n - zx (int): \u7ec8\u70b9\r\n - zy (int): \r\n - time (int, optional): \u6ed1\u52a8\u65f6\u957f. Defaults to 500.\r\n```\r\n\r\n## YoloV3\r\n ncnn\u52a0\u8f7dyolov3\u6a21\u578b\r\n Args:\r\n - param_path (str): yolov3\u6a21\u578bparam\u5730\u5740\r\n - bin_path (str): yolov3\u6a21\u578bbin\u5730\u5740\r\n - class_names (list): \u7c7b\u540d\u5217\u8868\r\n - tiny (bool, optional): \u662f\u5426\u542f\u7528tiny\u6a21\u578b. Defaults to False.\r\n - num_threads (int, optional): \u542f\u7528\u7ebf\u7a0b\u6570\u91cf. Defaults to 1.\r\n - use_gpu (bool, optional): \u662f\u5426\u542f\u7528gpu\u8ba1\u7b97 Defaults to False.\r\n### yolo_filter\r\n \u6a21\u578b\u8ba1\u7b97\u7ed3\u679c\u7b5b\u9009\r\n Args:\r\n - class_name (str): \u7b5b\u9009\u7684\u7c7b\u540d\r\n - class_names (list): \u6a21\u578b\u7c7b\u540d\u5217\u8868\r\n - objects (list): \u6a21\u578b\u8ba1\u7b97\u8fd4\u56de\u7684\u7ed3\u679c\r\n - min_prob (float, optional): \u7ed3\u679c\u7f6e\u4fe1\u5ea6\u9608\u503c. Defaults to 0.0.\r\n\r\n Returns:\r\n - list: objects\r\n - object:\r\n - rect.x \u5de6\u4e0a\u89d2\u6a2a\u5750\u6807\r\n - rect.y \u5de6\u4e0a\u89d2\u7eb5\u5750\u6807 \r\n - rect.w \u533a\u57df\u5bbd\r\n - rect.h \u533a\u57df\u9ad8\r\n - prob \u7f6e\u4fe1\u5ea6\r\n - label \u7c7b\u540d\r\n\r\n### yolo\u4f7f\u7528\u65b9\u6cd5\r\n```python\r\n m = cv2.imread(\"screenshotemulator-5554.png\")\r\n #\u52a0\u8f7d\u6a21\u578b\r\n net = YoloV3(\r\n param_path=\"yolov3-tiny_6700-opt.param\",\r\n bin_path=\"yolov3-tiny-opt.bin\",\r\n #\u7c7b\u540d\u6b64\u5904\u8bb0\u5f97\u5c06\u7b2c\u4e00\u4e2a\u5143\u7d20\u7559\u7a7a,\u6240\u6709\u5143\u7d20\u5f80\u540e\u4e00\u4e2a(ncnn\u7684\u95ee\u9898\u597d\u50cf\u662f(\u6709\u7a7a\u6539\u6539))\r\n class_names=[\"\", \"exp\", \"hd\", \"jb\"],\r\n num_threads=4,\r\n use_gpu=True,\r\n )\r\n #\u8f93\u5165mat\u683c\u5f0f\u56fe\u50cf\u8fdb\u884c\u8ba1\u7b97\r\n objects = net(m)\r\n #\u8fc7\u6ee4\u7ed3\u679c\r\n yolo_filter(\"exp\",net.class_names,objects,0.8)\r\n```\r\n\r\n\r\n\r\n",
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