227 lines
8.2 KiB
Python
227 lines
8.2 KiB
Python
"""
|
||
从main.py提取的YOLO识别测试文件
|
||
使用与main.py相同的识别逻辑
|
||
"""
|
||
import cv2
|
||
from utils.get_image import GetImage
|
||
from ultralytics import YOLO
|
||
from config import config_manager
|
||
import os
|
||
|
||
# 检查模型文件是否存在
|
||
model_path = r"best.pt"
|
||
model0_path = r"best0.pt"
|
||
|
||
if not os.path.exists(model_path):
|
||
print(f"❌ 模型文件不存在: {model_path}")
|
||
exit(1)
|
||
if not os.path.exists(model0_path):
|
||
print(f"❌ 模型文件不存在: {model0_path}")
|
||
exit(1)
|
||
|
||
# 加载YOLO模型（与main.py保持一致）
|
||
try:
|
||
model = YOLO(model_path).to('cuda')
|
||
model0 = YOLO(model0_path).to('cuda')
|
||
print(f"✅ 模型加载成功: {model_path}")
|
||
print(f"✅ 模型加载成功: {model0_path}")
|
||
except Exception as e:
|
||
print(f"❌ 模型加载失败: {e}")
|
||
exit(1)
|
||
|
||
|
||
def yolo_shibie(im_PIL, detections, model):
|
||
"""
|
||
YOLO识别函数（与main.py中的实现完全一致）
|
||
:param im_PIL: PIL图像对象
|
||
:param detections: 检测结果字典
|
||
:param model: YOLO模型
|
||
:return: 更新后的detections字典
|
||
"""
|
||
results = model(im_PIL) # 目标检测
|
||
for result in results:
|
||
for i in range(len(result.boxes.xyxy)):
|
||
left, top, right, bottom = result.boxes.xyxy[i]
|
||
scalar_tensor = result.boxes.cls[i]
|
||
value = scalar_tensor.item()
|
||
label = result.names[int(value)]
|
||
if label == 'center' or label == 'next' or label == 'boss' or label == 'zhaozi':
|
||
player_x = int(left + (right - left) / 2)
|
||
player_y = int(top + (bottom - top) / 2) + 30
|
||
RW = [player_x, player_y]
|
||
detections[label] = RW
|
||
elif label == 'daojv' or label == 'gw':
|
||
player_x = int(left + (right - left) / 2)
|
||
player_y = int(top + (bottom - top) / 2) + 30
|
||
RW = [player_x, player_y]
|
||
detections[label].append(RW)
|
||
elif label == 'npc1' or label == 'npc2' or label == 'npc3' or label == 'npc4':
|
||
player_x = int(left + (right - left) / 2)
|
||
player_y = int(bottom) + 30
|
||
RW = [player_x, player_y]
|
||
detections[label] = RW
|
||
return detections
|
||
|
||
|
||
def main():
|
||
"""主函数"""
|
||
print("="*60)
|
||
print("YOLO识别测试（main.py逻辑）")
|
||
print("="*60)
|
||
|
||
# 从配置加载采集卡设置
|
||
active_group = config_manager.get_active_group()
|
||
|
||
if active_group is None:
|
||
print("⚠️ 没有活动的配置组，使用默认设置")
|
||
print("提示: 可以运行 python gui_config.py 设置配置")
|
||
cam_index = 0
|
||
width = 1920
|
||
height = 1080
|
||
use_model = model # 默认使用model
|
||
else:
|
||
print(f"📋 使用配置组: {active_group['name']}")
|
||
cam_index = active_group['camera_index']
|
||
width = active_group['camera_width']
|
||
height = active_group['camera_height']
|
||
use_model = model0 # 城镇中使用model0
|
||
print(f" 使用模型: model0 (best0.pt) - 用于城镇识别")
|
||
|
||
print(f" 采集卡索引: {cam_index}")
|
||
print(f" 分辨率: {width}x{height}")
|
||
print()
|
||
|
||
# 初始化采集卡
|
||
print("🔧 正在初始化采集卡...")
|
||
get_image = GetImage(
|
||
cam_index=cam_index,
|
||
width=width,
|
||
height=height
|
||
)
|
||
|
||
if get_image.cap is None:
|
||
print("❌ 采集卡初始化失败")
|
||
print("请检查：")
|
||
print("1. 采集卡是否正确连接")
|
||
print("2. 采集卡索引是否正确")
|
||
print("3. 采集卡驱动是否安装")
|
||
return
|
||
|
||
print("✅ 采集卡初始化成功")
|
||
print("\n快捷键:")
|
||
print(" 'q' 或 ESC - 退出")
|
||
print(" 'm' - 切换模型 (model/model0)")
|
||
print(" 'd' - 显示/隐藏检测信息")
|
||
print()
|
||
|
||
try:
|
||
frame_count = 0
|
||
show_detections = True # 是否显示检测信息
|
||
current_model = use_model # 当前使用的模型
|
||
current_model_name = "model0" if use_model == model0 else "model"
|
||
|
||
while True:
|
||
# 获取帧
|
||
frame_data = get_image.get_frame()
|
||
|
||
if frame_data is None:
|
||
print("⚠️ 无法获取帧，跳过...")
|
||
continue
|
||
|
||
# frame_data 是 [im_opencv_rgb, im_PIL] 格式
|
||
im_opencv_rgb, im_PIL = frame_data
|
||
|
||
if im_PIL is None:
|
||
print("⚠️ PIL图像为空，跳过...")
|
||
continue
|
||
|
||
# 初始化检测结果字典
|
||
detections = {
|
||
'center': None, 'next': None,
|
||
'npc1': None, 'npc2': None, 'npc3': None, 'npc4': None,
|
||
'boss': None, 'zhaozi': None,
|
||
'daojv': [], 'gw': []
|
||
}
|
||
|
||
# 执行YOLO检测（使用main.py的逻辑）
|
||
detections = yolo_shibie(im_PIL, detections, current_model)
|
||
|
||
# 获取绘制好框的图像用于显示
|
||
try:
|
||
results = current_model(im_PIL)
|
||
result = results[0]
|
||
frame_with_boxes_rgb = result.plot()
|
||
frame_with_boxes_bgr = cv2.cvtColor(frame_with_boxes_rgb, cv2.COLOR_RGB2BGR)
|
||
except Exception as e:
|
||
print(f"⚠️ 绘制检测框失败: {e}")
|
||
frame_with_boxes_bgr = cv2.cvtColor(im_opencv_rgb, cv2.COLOR_RGB2BGR)
|
||
|
||
# 在图像上显示检测信息
|
||
if show_detections:
|
||
# 显示模型名称
|
||
cv2.putText(frame_with_boxes_bgr, f"Model: {current_model_name}",
|
||
(10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
|
||
|
||
# 显示检测到的目标
|
||
y_offset = 60
|
||
detected_items = []
|
||
for key, value in detections.items():
|
||
if value is not None and value != []:
|
||
if key in ['daojv', 'gw']:
|
||
detected_items.append(f"{key}: {len(value)}个")
|
||
else:
|
||
detected_items.append(f"{key}: {value}")
|
||
|
||
if detected_items:
|
||
text = f"Detected: {', '.join(detected_items[:5])}" # 最多显示5个
|
||
if len(detected_items) > 5:
|
||
text += f" ... (+{len(detected_items)-5})"
|
||
cv2.putText(frame_with_boxes_bgr, text,
|
||
(10, y_offset), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 0), 2)
|
||
|
||
# 显示图像
|
||
cv2.imshow("YOLO Detection (main.py logic)", frame_with_boxes_bgr)
|
||
|
||
# 检查按键
|
||
key = cv2.waitKey(1) & 0xFF
|
||
if key in [27, ord('q'), ord('Q')]:
|
||
print("\n用户退出")
|
||
break
|
||
elif key == ord('m') or key == ord('M'):
|
||
# 切换模型
|
||
if current_model == model:
|
||
current_model = model0
|
||
current_model_name = "model0"
|
||
else:
|
||
current_model = model
|
||
current_model_name = "model"
|
||
print(f"切换模型: {current_model_name}")
|
||
elif key == ord('d') or key == ord('D'):
|
||
show_detections = not show_detections
|
||
print(f"显示检测信息: {'开启' if show_detections else '关闭'}")
|
||
|
||
frame_count += 1
|
||
if frame_count % 30 == 0: # 每30帧打印一次
|
||
print(f"📊 已处理 {frame_count} 帧 (模型: {current_model_name})")
|
||
# 打印有检测到的目标
|
||
detected_items = {k: v for k, v in detections.items() if v is not None and v != []}
|
||
if detected_items:
|
||
print(f" 检测到: {detected_items}")
|
||
|
||
except KeyboardInterrupt:
|
||
print("\n\n用户中断测试")
|
||
except Exception as e:
|
||
print(f"\n❌ 测试过程中发生错误: {e}")
|
||
import traceback
|
||
traceback.print_exc()
|
||
finally:
|
||
# 清理资源
|
||
get_image.release()
|
||
cv2.destroyAllWindows()
|
||
print("🔚 测试结束")
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|
||
|