# -*- coding: utf-8 -*- """ Created on Sat Aug 31 07:22:52 2024 @author: limyu """ from ultralytics import YOLO from pypylon import pylon import cv2 import numpy as np import devices_TMC_USB as de import matplotlib.pyplot as plt import numpy as np import time def moveup1(): # 创建设备 TMC1 = de.TMC_USB() try: # 打开，并连接硬件 enum_dev = TMC1.tmc_device_count() print(f'设备数量为:{enum_dev}') handle = TMC1.tmc_open(enum_dev-1) print(f'设备句柄为:{handle}') # 读取设备状态 status = TMC1.tmc_get_status(handle, 0x00) print(f'设备状态为:{status}') #读取设备错误状态原因 buff1 = TMC1.tmc_get_error(handle, 128) print( f'设备错误状态原因：{buff1}') # 设置 板卡名称 TMC1.tmc_set_board_name(handle, 'waterfall',9) # 读取 板卡名称 name1 = TMC1.tmc_get_board_name(handle, 9) print(f'板卡名称:{name1}') # 加载/重置 轴参数 TMC1.tmc_init(handle, 0x00, "TSA100-B") # 设置轴控制状态 issuccess = TMC1.tmc_set_axis_enable(handle, 0x00, True) print(f'设置轴控制状态:{issuccess}') # 设置 运动单位 TMC1.tmc_set_unit(handle, 0x00, 2) # 读取 运动单位 unit=TMC1.tmc_get_unit(handle, 0x00) print(f'运动单位为：{unit}') #set initial speed TMC1.tmc_set_init_speed(handle, 0x00, 0.05) # get initial speed speed=TMC1.tmc_get_init_speed(handle, 0x00) print(f'initial speed is：{speed}') #set speed TMC1.tmc_set_speed(handle, 0x00, 0.05) # get speed speed=TMC1.tmc_get_speed(handle, 0x00) print(f'speed is：{speed}') # h归零 #TMC1.tmc_go_home(handle, 0x00) # 绝对移动 issuccess = TMC1.tmc_relative_move(handle, 0x00, 0.001) print(f'设备移动：{issuccess}') # 停止移动 TMC1.tmc_stop(handle, 0x00) finally: # 关闭设备 TMC1.tmc_close(handle) print("关闭设备") def movedown1(): # 创建设备 TMC1 = de.TMC_USB() try: # 打开，并连接硬件 enum_dev = TMC1.tmc_device_count() print(f'设备数量为:{enum_dev}') handle = TMC1.tmc_open(enum_dev-1) print(f'设备句柄为:{handle}') # 读取设备状态 status = TMC1.tmc_get_status(handle, 0x00) print(f'设备状态为:{status}') #读取设备错误状态原因 buff1 = TMC1.tmc_get_error(handle, 128) print( f'设备错误状态原因：{buff1}') # 设置 板卡名称 TMC1.tmc_set_board_name(handle, 'waterfall',9) # 读取 板卡名称 name1 = TMC1.tmc_get_board_name(handle, 9) print(f'板卡名称:{name1}') # 加载/重置 轴参数 TMC1.tmc_init(handle, 0x00, "TSA100-B") # 设置轴控制状态 issuccess = TMC1.tmc_set_axis_enable(handle, 0x00, True) print(f'设置轴控制状态:{issuccess}') # 设置 运动单位 TMC1.tmc_set_unit(handle, 0x00, 2) # 读取 运动单位 unit=TMC1.tmc_get_unit(handle, 0x00) print(f'运动单位为：{unit}') #set initial speed TMC1.tmc_set_init_speed(handle, 0x00, 0.05) # get initial speed speed=TMC1.tmc_get_init_speed(handle, 0x00) print(f'initial speed is：{speed}') #set speed TMC1.tmc_set_speed(handle, 0x00, 0.05) # get speed speed=TMC1.tmc_get_speed(handle, 0x00) print(f'speed is：{speed}') # h归零 #TMC1.tmc_go_home(handle, 0x00) # 绝对移动 issuccess = TMC1.tmc_relative_move(handle, 0x00, -0.001) print(f'设备移动：{issuccess}') # 停止移动 TMC1.tmc_stop(handle, 0x00) finally: # 关闭设备 TMC1.tmc_close(handle) print("关闭设备") def moveup(): print('move up') def movedown(): print('move down') # Load the model model = YOLO("C:\\Users\\yudian.lim\\Desktop\\tmc-usb sdk release\\Python\\mixed_power\\weights_01validation\\weights\\best.pt") # Define colors for 10 classes colors = [ (255, 0, 0), # Blue (0, 255, 0), # Green (0, 0, 255), # Red (255, 255, 0), # Cyan (255, 0, 255), # Magenta (0, 255, 255), # Yellow (128, 0, 0), # Maroon (0, 128, 0), # Dark Green (0, 0, 128), # Navy (128, 128, 0) # Olive ] # Connect to the camera camera = pylon.InstantCamera(pylon.TlFactory.GetInstance().CreateFirstDevice()) camera.Open() # Set parameters, if needed camera.Width = camera.Width.Max camera.Height = camera.Height.Max camera.StartGrabbing(pylon.GrabStrategy_LatestImageOnly) # Prepare an OpenCV window cv2.namedWindow('YOLOv8 Real-time Detection', cv2.WINDOW_NORMAL) dist_per_pixel = 579 / camera.Width.Value # Update this based on your camera's resolution def findwidth(cropped_array): col_shortened = cropped_array.shape[1]//3 shorted_array = cropped_array[:, col_shortened:2*col_shortened] max_index = np.argmax(shorted_array) row, col = np.unravel_index(max_index, shorted_array.shape) e = shorted_array[:, col] max_indices1 = np.where(e == e.max())[0] max_indices2 = np.where(e == int(e.max()*0.80))[0] max_indices3 = np.where(e == int(e.max()*0.85))[0] max_indices4 = np.where(e == int(e.max()*0.90))[0] max_indices5 = np.where(e == int(e.max()*0.95))[0] max_indices6 = np.where(e == int(e.max()*0.99))[0] max_indices = np.concatenate((max_indices1, max_indices2, max_indices3, max_indices4, max_indices5, max_indices6)) if len(max_indices) >1: beam_width = max(max_indices) - min(max_indices) if len(max_indices) ==1: beam_width = 0 if len(max_indices) ==0: beam_width = 0 beam_width = beam_width*dist_per_pixel return beam_width while camera.IsGrabbing(): grabResult = camera.RetrieveResult(5000, pylon.TimeoutHandling_ThrowException) if grabResult.GrabSucceeded(): # Access the image data image = grabResult.Array # Convert grayscale image to BGR for display if len(image.shape) == 2: # Check if the image is grayscale image_bgr = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR) else: image_bgr = image # Perform detection results = model(image_bgr) region_list = [] # Process results and draw bounding boxes for result in results: for box in result.boxes: # Convert tensor values to Python scalars x1, y1, x2, y2 = map(int, box.xyxy[0].tolist()) conf = float(box.conf[0]) cls = int(box.cls[0]) label = f"{model.names[cls]}:{conf:.2f}" region_list.append(model.names[cls]) # Select color based on class index color = colors[cls % len(colors)] # Crop the detected region from the image for width calculation cropped_region = image[y1:y2, x1:x2] # Use original grayscale image for analysis width = findwidth(cropped_region) # Draw the bounding box and label on the frame cv2.rectangle(image_bgr, (x1, y1), (x2, y2), color, 5) # Adjust the thickness as needed cv2.putText(image_bgr, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 4, color, 5) # Adjust font size and thickness # Display the calculated width on the image cv2.putText(image_bgr, f'{width:.2f}um', (x2-10, y2 + 10), cv2.FONT_HERSHEY_SIMPLEX, 4, color, 5) # Display the resulting frame cv2.imshow('YOLOv8 Real-time Detection', image_bgr) print(region_list) region_a_count = region_list.count('Region A') region_b_count = region_list.count('Region B') region_c_count = region_list.count('Region C') region_d_count = region_list.count('Region D') if region_a_count > int(len(region_list)/2) and region_b_count !=len(region_list): moveup1() # Get the text size to position it correctly (text_width, text_height), _ = cv2.getTextSize('moving up...', cv2.FONT_HERSHEY_SIMPLEX, 4, 5) text_x = 20 # X position for the text (10 pixels from the left) text_y = image_bgr.shape[0] - 200 # Y position for the text (10 pixels from the bottom) # Put the text on the image cv2.putText(image_bgr, 'moving up...', (text_x, text_y), cv2.FONT_HERSHEY_SIMPLEX, 6, (0, 255, 255), 8, cv2.LINE_AA) if region_b_count < int(len(region_list)*0.8): moveup1() # Get the text size to position it correctly (text_width, text_height), _ = cv2.getTextSize('moving up...', cv2.FONT_HERSHEY_SIMPLEX, 4, 5) text_x = 20 # X position for the text (10 pixels from the left) text_y = image_bgr.shape[0] - 200 # Y position for the text (10 pixels from the bottom) # Put the text on the image cv2.putText(image_bgr, 'moving up...', (text_x, text_y), cv2.FONT_HERSHEY_SIMPLEX, 6, (0, 255, 255), 8, cv2.LINE_AA) if region_c_count > int(len(region_list)/2) or region_d_count > int(len(region_list)/2) and region_b_count !=len(region_list): movedown1() # Get the text size to position it correctly (text_width, text_height), _ = cv2.getTextSize('moving down...', cv2.FONT_HERSHEY_SIMPLEX, 4, 5) text_x = 20 # X position for the text (10 pixels from the left) text_y = image_bgr.shape[0] - 200 # Y position for the text (10 pixels from the bottom) # Put the text on the image cv2.putText(image_bgr, 'moving down...', (text_x, text_y), cv2.FONT_HERSHEY_SIMPLEX, 6, (0, 255, 255), 8, cv2.LINE_AA) if region_b_count < int(len(region_list)*0.8): movedown1() # Get the text size to position it correctly (text_width, text_height), _ = cv2.getTextSize('moving down...', cv2.FONT_HERSHEY_SIMPLEX, 4, 5) text_x = 20 # X position for the text (10 pixels from the left) text_y = image_bgr.shape[0] - 200 # Y position for the text (10 pixels from the bottom) # Put the text on the image cv2.putText(image_bgr, 'moving down...', (text_x, text_y), cv2.FONT_HERSHEY_SIMPLEX, 6, (0, 255, 255), 8, cv2.LINE_AA) # Display the resulting frame cv2.imshow('YOLOv8 Real-time Detection', image_bgr) # Exit the loop if the 'q' key is pressed if cv2.waitKey(1) & 0xFF == ord('q'): break grabResult.Release() # Release the camera and close all OpenCV windows camera.StopGrabbing() camera.Close() cv2.destroyAllWindows()