Delete files/ExampleUsage.py

files/ExampleUsage.py

@@ -1,96 +0,0 @@
-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()