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Python Yolo v5 6.2 Apex

Python 98.13% Shell 1.24% Dockerfile 0.63%

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python.yolo.apex.autoaim.helper's Issues

更新后出现 AttributeError: 'Conv' object has no attribute 'ki'错误

YOLOv5 2023-5-26 Python-3.9.16 torch-2.0.1+cu117 CUDA:0 (NVIDIA GeForce RTX 3060, 12287MiB)

Fusing layers...
Model summary: 213 layers, 7012822 parameters, 0 gradients
Traceback (most recent call last):
File "D:\GameAiTools\python.yolo.apex.autoaim.helper-master\detect.realtime.py", line 11, in
detector = Detector(weight)
File "C:\Users\ZD\anaconda3\envs\tmp_env_yolo\lib\site-packages\torch\utils_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "D:\GameAiTools\python.yolo.apex.autoaim.helper-master\toolkit.py", line 262, in init
self.model.warmup(imgsz=(1 if self.pt else bs, 3, *self.imgsz)) # warmup
File "D:\GameAiTools\python.yolo.apex.autoaim.helper-master\models\common.py", line 533, in warmup
return forward_call(*args, **kwargs)
File "D:\GameAiTools\python.yolo.apex.autoaim.helper-master\models\yolo.py", line 192, in forward
return self._forward_once(x, profile, visualize) # single-scale inference, train
File "D:\GameAiTools\python.yolo.apex.autoaim.helper-master\models\yolo.py", line 106, in _forward_once
y.append(x if m.ki in self.save else None) # save output
File "C:\Users\ZD\anaconda3\envs\tmp_env_yolo\lib\site-packages\torch\nn\modules\module.py", line 1614, in getattr
raise AttributeError("'{}' object has no attribute '{}'".format(
AttributeError: 'Conv' object has no attribute 'ki'

How should I fix this

ImportError: numpy.core.multiarray failed to import

您好，我能这么写用来检测友军吗，不知道为什么运行失败，大佬求帮

#敌我识别

try:
img = cv2.imread(img)

将图像转换为RGB格式

 img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)                                                                        
                                                                                                                                                                                                                                                                                                                                                                                                                                                                   
                                                                                                                                                                                                                                        # 定义蓝色和绿色的HSV范围                                                                                                       
 lower_blue = np.array([110, 50, 50])                                                                              
 upper_blue = np.array([130, 255, 255])                                                                            
 lower_green = np.array([50, 50, 50])                                                                              
 upper_green = np.array([70, 255, 255])

获取box的位置和大小

 x1 = target[3][0],y1 = target[3][1],x2 = target[3][2],y2 = target[3][3]                                           
 cx = (x1 + x2) / 2                                                                                                
 cy = (y1 + y2) / 2                                                                                                
 w = x2 - x1                                                                                                       
 h = y2 - y1                                                                                                       
 target = torch.stack((cx, cy, w, h), axis=-1)                                                                     
 print(f'{x1},{y1},{x2},{y2}')                                                                                     
                                                                                                                   
 x, y, w, h = (cx, cy, w, h)   #这段可能还有问题                                                                           
                                                                                                                   
                                                                                                                   
 roi = img[y - 100:y, x:x + w]      # 获取box上方100个y内的区域                                                             
                                                                                                                   
             # 将区域转换为HSV颜色空间                                                                                       
 hsv_roi = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)                                                                    
                                                                                                                   
 # 创建掩膜                                                                                                            
 mask_roi_blue = cv2.inRange(hsv_roi, lower_blue, upper_blue)                                                      
 mask_roi_green = cv2.inRange(hsv_roi, lower_green, upper_green)

except:
print("no")

如果检测到蓝色或绿色

if (cv2.countNonZero(mask_roi_blue) == 0) and (cv2.countNonZero(mask_roi_green) == 0)and data[lock] and target :
index, clazz, conf, sc, gc, sr, gr = target
if inner(sc):
cx, cy = data[center]
sx, sy = sc #移动十字准线以跟随目标对象
x = sx - cx
y = sy - cy
if data[pidc]:
px = -int(pidx(x))
move(px, y)
else:
ax = int(x * data[ads])
ay = int(y * data[ads])
move(ax, ay)

mrathena / python.yolo.apex.autoaim.helper Goto Github PK

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python.yolo.apex.autoaim.helper's Issues

将图像转换为RGB格式

获取box的位置和大小

如果检测到蓝色或绿色

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