feat: yolov5 未央自动爬楼 - 1 yolo不同版本训练的pt不能兼容使用

README.md

@@ -5,11 +5,22 @@
 详细教学请移步b站，有很详细的讲解：https://www.bilibili.com/video/BV18r4y1A7BF/
 
 
-1. 屏幕检测
-2. 怪物，材料的图像识别
-3. 控制打怪和捡材料
-4. 下一关
+视频收集数据集
+标注
+训练
+视频检测训练效果
 
+游戏主流程:
+1. 屏幕抓取，图像检测
+2. 怪物识别，和怪物距离n像素拍地板清怪，漏怪继续拍地板
+3. 捡发光的材料
+4. 小地图进入下一关
+5. boss房间，下一局
 
-1. 识别地图中怪物
-2. 寻找位置，释放技能，清理残余，寻找下一个门，进图
+
+测试流程:
+1.纯小号检测  1级地图
+2.未央爬楼视频检测，测试延迟同步操作是否能基础通过
+3.跨4子账号未央检测
+
+实战

opencv/bgra.py

@@ -0,0 +1,18 @@
+import cv2
+
+img = cv2.imread("lena.jpg")
+bgra = cv2.cvtColor(img, cv2.COLOR_BGR2BGRA)
+b, g, r, a = cv2.split(bgra)
+a[:, :] = 125
+bgra125 = cv2.merge([b, g, r, a])
+a[:, :] = 0
+bgra0 = cv2.merge([b, g, r, a])
+cv2.imshow("img", img)
+cv2.imshow("bgra", bgra)
+cv2.imshow("bgra125", bgra125)
+cv2.imshow("bgra0", bgra0)
+cv2.waitKey()
+cv2.destroyAllWindows()
+cv2.imwrite("bgra.png", bgra)
+cv2.imwrite("bgra125.png", bgra125)
+cv2.imwrite("bgra0.png", bgra0)

test/keyboard_test.py

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+import sys
+
+sys.path.append('../')
+from directkeys import PressKey, ReleaseKey, key_down, key_up
+from direction_move import move
+
+move('RIGHT')

test/weiyang.py

@@ -0,0 +1,106 @@
+import cv2
+import numpy as np
+import torch
+from models.experimental import attempt_load
+from utils.general import non_max_suppression, xyxy2xywh
+from grabscreen import grab_screen
+from directkeys import key_press
+from direction_move import move
+names = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
+        'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
+        'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
+        'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
+        'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
+        'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
+        'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
+        'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
+        'hair drier', 'toothbrush']
+
+
+# 1-8 自动爬楼
+
+weights = 'best.pt'  # yolo5 模型存放的位置
+device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu")
+half = device.type != 'cpu'
+conf_thres = 0.3  # NMS的置信度过滤
+iou_thres = 0.2  # NMS的IOU阈值
+classes = None
+agnostic_nms = False  # 不同类别的NMS时也参数过滤
+model = attempt_load(weights, device)
+
+# img0 = cv2.imread("shiwu.jpg")
+img0 = grab_screen((0, 0, 1280, 800))
+
+img = cv2.cvtColor(img0, cv2.COLOR_BGRA2BGR)
+img = img[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB
+img = np.ascontiguousarray(img)
+img = torch.from_numpy(img).to(device).unsqueeze(dim=0)
+img = img.half() if half else img.float()  # uint8 to fp16/32~
+img /= 255.0  # 0 - 255 to 0.0 - 1.0
+
+pred = model(img, augment=False)[0]
+# Apply NMS
+det = non_max_suppression(pred, conf_thres, iou_thres, classes=classes, agnostic=agnostic_nms)
+gn = torch.tensor(img0.shape)[[1, 0, 1, 0]]
+det = det[0]
+
+if det is not None and len(det):
+    # # Rescale boxes from img_size to im0 size
+    # det[:, :4] = scale_coords(img.shape[2:], det[:, :4], img0.shape).round()
+    #
+    # # Print results
+    # for c in det[:, -1].unique():
+    #     n = (det[:, -1] == c).sum()  # detections per class
+    img_object = []
+    cls_object = []
+    for idx, (*xyxy, conf, cls) in enumerate(reversed(det)):
+        # print(idx,conf,cls)
+        # if True:  # Write to file
+        #     xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
+        #     with open('giraffe_label.txt', 'a') as f:
+        #         f.write(('%g ' * 5 + '\n') % (cls, *xywh))  # label format
+
+        xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4))).view(-1).tolist()
+        cls = int(cls)
+        img_object.append(xywh)  # [[位置]]
+        cls_object.append(names[cls])  # [分类]
+        if names[cls] == 'giraffe':
+            hero_index = idx
+        # 游戏
+    thx = 30  # 捡东西时，x方向的阈值
+    thy = 30  # 捡东西时，y方向的阈值
+    attx = 150  # 攻击时，x方向的阈值
+    atty = 50  # 攻击时，y方向的阈值
+    skillDis = 800
+    skillDis = 400
+
+    if 'giraffe' in cls_object:
+        hero_xywh = img_object[hero_index]
+    # else:
+    #     continue
+    # 对屏幕中的monster 进行平均  最终一个
+    if 'orange' in cls_object:
+        # 打怪
+        for idx, (c, box) in enumerate(zip(cls_object, img_object)):
+            print(c, box)
+            dis = ((hero_xywh[0] - box[0]) ** 2 + (hero_xywh[1] - box[1]) ** 2) ** 0.5
+            # if dis < min_distance:
+            #     monster_box = box
+            #     monster_index = idx
+            #     min_distance = dis
+    elif 'material' in cls_object:
+        if 'option' in cls_object:
+            # 聚物捡东西  f2  xxx
+        else:
+            # 普通捡东西
+    elif 'boss' in cls_object:
+        # 打boss
+        key_press('g')
+    elif 'sinan' in cls_object:
+        # 下一局 出现思南选择界面  选择1-8阶随机一个思南  鼠标移动点击
+        #   中场清理背包
+        # if 1-8 in cls_object:
+        #     nextGame
+        # else:
+        #     shutDown and 通知爬楼完成,需要补货，清理背包  返回
+