Upload 2 files

files/ExampleUsage.py

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+try:
+    import subprocess
+except:
+    print('"subprocess" module is not available, exiting...')
+    exit()
+try:
+    import sys
+except:
+    print('"sys" module is not available, exiting...')
+    exit()
+try:
+    import time
+except:
+    print('"time" module is not available, exiting...')
+    exit()
+try:
+    import os
+except:
+    print('"os" module is not available, exiting...')
+    exit()
+try:
+    import numpy as np
+except:
+    subprocess.check_call([sys.executable, "-m", "pip", "install", "numpy"])
+    import numpy as np
+try:
+    import pyautogui
+except:
+    subprocess.check_call([sys.executable, "-m", "pip", "install", "pyautogui"])
+    import pyautogui
+try:
+    import dxcam
+except:
+    subprocess.check_call([sys.executable, "-m", "pip", "install", "dxcam"])
+    import dxcam
+try:
+    import torch
+except:
+    subprocess.check_call([sys.executable, "-m", "pip", "install", "torch"])
+    import torch
+try:
+    import cv2
+except:
+    subprocess.check_call([sys.executable, "-m", "pip", "install", "opencv-python"])
+    import cv2
+try:
+    import pathlib
+except:
+    subprocess.check_call([sys.executable, "-m", "pip", "install", "pathlib"])
+    import pathlib
+
+temp = pathlib.PosixPath
+pathlib.PosixPath = pathlib.WindowsPath
+
+current_file_path = os.path.dirname(os.path.abspath(__file__))
+model_path = os.path.join(current_file_path, 'best.pt')
+model = torch.hub.load('ultralytics/yolov5', 'custom', path=model_path, force_reload=True)
+
+screen_width, screen_height = pyautogui.size()
+camera = dxcam.create(device_idx=0,output_color="RGB")
+
+cv2.namedWindow('YOLOv5 Detection', cv2.WINDOW_NORMAL)
+cv2.resizeWindow('YOLOv5 Detection', 960, 540)
+cv2.setWindowProperty('YOLOv5 Detection', cv2.WND_PROP_TOPMOST, 1)
+
+while True:
+    start_time = time.time()
+    frame = camera.grab(region=(0, 0, screen_width, screen_height))
+    if frame is None: continue
+    rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)  
+
+    results = model(rgb_frame)
+
+    boxes = results.pandas().xyxy[0]
+
+    for _, box in boxes.iterrows():
+        label = box['name']
+        score = box['confidence']
+        x, y, w, h = int(box['xmin']), int(box['ymin']), int(box['xmax'] - box['xmin']), int(box['ymax'] - box['ymin'])
+        
+        if label in ['map']:
+            cv2.rectangle(rgb_frame, (x, y), (x + w, y + h), (0, 255, 255), 3)
+            cv2.putText(rgb_frame, f"{score:.2f}", (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 1.5, (0, 255, 255), 2, cv2.LINE_AA)
+        if label in ['arrow']:
+            cv2.rectangle(rgb_frame, (x, y), (x + w, y + h), (255, 0, 0), 3)
+            cv2.putText(rgb_frame, f"{score:.2f}", (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 1.5, (255, 0, 0), 2, cv2.LINE_AA)
+
+    fps = round(1 / (time.time() - start_time), 1)
+    cv2.putText(rgb_frame, f"fps: {fps}", (20, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 255, 255), 2, cv2.LINE_AA)         
+    cv2.imshow('YOLOv5 Detection', rgb_frame)
+    cv2.resizeWindow('YOLOv5 Detection', 854, 480)
+    
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break
+
+cv2.destroyAllWindows()

files/HowToUse.txt

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+The model contains 2 classes: "map" and "arrow"
+You can run the ExampleUsage.py to test the model.