second commit

CrsLYL02025.py

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+
+import json
+
+import os
+import numpy as np
+import cv2
+import time
+
+def set_camera_resolution(cap, width, height):
+    """
+    设置摄像头的分辨率。
+
+    参数:
+    - cap: cv2.VideoCapture 对象
+    - width: 目标宽度
+    - height: 目标高度
+    """
+    # 设置摄像头的宽度和高度
+    cap.set(cv2.CAP_PROP_FRAME_WIDTH, width)
+    cap.set(cv2.CAP_PROP_FRAME_HEIGHT, height)
+
+    # 获取实际设置的宽度和高度
+    actual_width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
+    actual_height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
+
+    print(f"设置的分辨率: 宽度={width}, 高度={height}")
+    print(f"实际的分辨率: 宽度={actual_width}, 高度={actual_height}")
+
+def show_mask(mask, ax, random_color=False):
+    if random_color:
+        color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)
+    else:
+        color = np.array([30/255, 144/255, 255/255, 0.6])
+    h, w = mask.shape[-2:]
+    mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)
+    ax.imshow(mask_image)
+
+def findchesslen(image):
+    # 转换为灰度图像
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    
+    # 棋盘格规格，例如8x8内部角点（根据你的实际棋盘规格修改）
+    board_size = (3, 4)  # 对于8x8的棋盘，内部角点数为7x7
+    ret, corners = cv2.findChessboardCorners(gray, board_size, None)
+
+    frameshow = image.copy()
+    cv2.drawChessboardCorners(frameshow, board_size, corners, ret)
+    cv2.imshow('Chessboard Corners', frameshow)
+
+    if ret:  # 如果成功找到角点
+        distances = []
+        
+        for row in range(board_size[1]):  # 遍历每一行
+            for col in range(board_size[0] - 1):  # 对于每行中的每个角点，计算到下一个角点的距离
+                idx = row * board_size[0] + col
+                p1 = corners[idx][0]
+                p2 = corners[idx + 1][0]
+                distance = np.linalg.norm(p1 - p2)
+                distances.append(distance)
+                print(f"角点 {idx} 到 {idx+1} 的距离: {distance:.2f} 像素")
+                
+        for col in range(board_size[0]):  # 遍历每一列
+            for row in range(board_size[1] - 1):  # 对于每列中的每个角点，计算到下一个角点的距离
+                idx = row * board_size[0] + col
+                p1 = corners[idx][0]
+                p2 = corners[idx + board_size[0]][0]
+                distance = np.linalg.norm(p1 - p2)
+                distances.append(distance)
+                print(f"角点 {idx} 到 {idx+board_size[0]} 的距离: {distance:.2f} 像素")
+
+        # 计算平均边长
+        avg_distance = np.mean(distances)
+        return avg_distance 
+    else: 
+        return -1
+
+
+def getseeds(image,dislen=89.01):
+    frame = image
+      #  将图像从BGR转换为HSV
+    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
+
+    # 定义橘红色在HSV颜色空间中的范围
+    # 色调(H)：橘红色大致在0-20之间
+    # 饱和度(S)：通常较高的值表示更纯的颜色
+    # 亮度(V)：根据具体需求调整，这里我们选择较低的阈值以包含较暗的部分
+    lower_orange_red = np.array([0, 180, 80])
+    upper_orange_red = np.array([30, 255, 195])
+
+    # 创建掩码
+    mask = cv2.inRange(hsv, lower_orange_red, upper_orange_red)
+
+    # 找到符合条件的像素坐标
+    orange_red_points = cv2.findNonZero(mask)
+
+    frameshow = frame.copy()
+        # 创建一个与原图大小相同的空白掩码
+    mask = np.zeros_like(frame[:, :, 0])  # 单通道掩码
+
+    if orange_red_points is not None:
+        for point in orange_red_points:
+            x, y = point[0]
+            # 在原图上画圆标记这些点
+            cv2.circle(frameshow, (x, y), 1, (0, 255, 0), -1)  # 使用绿色标记点
+            cv2.circle(mask, (x, y), 1, 255, -1)  # 使用白色标记点
+
+    # 查找轮廓
+    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+    if contours:
+        for max_contour in contours:
+            # 找到面积最大的轮廓
+        # max_contour = max(contours, key=cv2.contourArea)
+            
+            # 计算最小边界矩形（旋转）
+            rect = cv2.minAreaRect(max_contour)
+            box = cv2.boxPoints(rect)
+            box = np.int32(box)
+            
+            # 计算边界框的宽度和高度
+            width = int(rect[1][0])
+            height = int(rect[1][1])
+            
+            # 输出边界框的信息
+          #  print(f"Bounding Box Center: ({rect[0][0]:.2f}, {rect[0][1]:.2f})")
+          #  print(f"Bounding Box Width: {width}")
+          #  print(f"Bounding Box Height: {height}")
+
+            if dislen >20 and rect[1][0] > 25 and rect[1][1] > 25 :
+                ww = rect[1][0]*3.0/dislen
+                hh = rect[1][1]*3.0/dislen
+                # 格式化输出保留两位有效数字
+                ww_text = f"w=: {ww:.2f}"
+                hh_text = f"h=: {hh:.2f}"
+                # 获取边界框左上角的坐标
+            #  x, y = np.min(box, axis=0)  
+
+                # 计算中心点
+                x = int(np.mean(box[:, 0]))
+                y = int(np.mean(box[:, 1]))
+                print(x,y)
+                print(rect[1][0])
+                print(ww_text)
+
+            #  center_point = (center_x, center_y)
+
+                # 设置字体、缩放比例、颜色和厚度
+                font = cv2.FONT_HERSHEY_SIMPLEX
+                font_scale = 2
+                color = (0, 0, 255)  # BGR颜色格式，这里是蓝色
+                thickness = 2
+
+                # 显示宽度信息
+                cv2.putText(frameshow, ww_text, (x, y-10), font, font_scale, color, thickness)
+
+                # 显示高度信息
+                cv2.putText(frameshow, hh_text, (x, y - 40), font, font_scale, color, thickness)
+
+            # 在原图上绘制边界框
+                cv2.drawContours(frameshow, [box], 0, (0, 0, 255), 2)
+
+# 显示结果帧
+   # cv2.imshow('Orange Red Points and Bounding Box', frameshow)
+    return frameshow
+
+    # 显示结果帧
+    #cv2.imshow('Orange Red Points', frameshow)
+
+
+
+# 打开USB摄像头
+cap = cv2.VideoCapture(0) # 0是默认的摄像头ID，如果有多个摄像头可能需要调整
+# 设置目标分辨率
+target_width = 1920
+target_height = 1080
+set_camera_resolution(cap, target_width, target_height)
+
+if not cap.isOpened():
+    print("无法打开摄像头")
+else:
+
+    last_save_time = time.time()
+    interval = 10*20   # 10分钟间隔
+    save_dir="data"
+    save_path_format="frame_%m-%d_%H-%M.png"
+    save_path_format_det="frame_%m-%d_%H-%M_det.png"
+    if not os.path.exists(save_dir):
+        os.makedirs(save_dir)
+
+    
+    # 设置显示窗口的目标尺寸
+    display_width = 640
+    display_height = 360
+
+    while True:
+        ret, frame = cap.read()
+       ## height, width, channels = frame.shape[:3]  # 获取帧的高度、宽度和通道数
+        #print(f"Frame 尺寸: 宽度={width}, 高度={height}, 通道数={channels if len(frame.shape) == 3 else 1}")
+        if not ret:
+            print("无法获取帧")
+            break
+        if 0:
+            #print(frame)
+            dislen = findchesslen(frame)      
+            #dislen = 148.50
+            print(f"cell={dislen}")
+            cv2.waitKey(20)
+            continue
+        #continue
+        #print(frame) 
+        #dislen = findchesslen(frame)
+
+        dislen = 147.50
+        #print(f"cell={dislen}")
+       # frame_det = frame
+        frame_det = getseeds(frame,dislen)
+
+        rzframe = cv2.resize(frame, (display_width, display_height))
+        cv2.imshow('orgimage', rzframe)
+
+        rzframe_det = cv2.resize(frame_det, (display_width, display_height))
+        cv2.imshow('detimage', rzframe_det)
+
+
+        current_time = time.time()
+        if current_time - last_save_time > interval:
+            # 根据当前时间生成文件名
+            file_name = time.strftime(save_path_format, time.localtime(current_time))
+            file_name_det = time.strftime(save_path_format_det, time.localtime(current_time))
+
+            save_path = os.path.join(save_dir, file_name)
+            save_path_det = os.path.join(save_dir, file_name_det)
+
+            # 保存原始帧
+            cv2.imwrite(save_path, frame)
+            print(f"已保存帧到 {save_path}")
+
+            # 获取并保存检测结果帧
+            cv2.imwrite(save_path_det, frame_det)
+            print(f"已保存检测结果帧到 {save_path_det}")
+
+            last_save_time = current_time  # 更新最后保存时间为当前时间
+
+        # 显示结果帧
+      #  cv2.imshow('Chessboard Corners', frame)
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break
+    
+    cap.release()
+    cv2.destroyAllWindows()