优化参数

surround_view/param_settings.py

@@ -2,62 +2,94 @@ import os
 import cv2
 
 
+# 摄像头名称列表
 camera_names = ["front", "back", "left", "right"]
 
-# --------------------------------------------------------------------
+# 标定布向外扩展的尺寸，单位为像素，默认不修改
-# (shift_width, shift_height): how far away the birdview looks outside
-# of the calibration pattern in horizontal and vertical directions
 shift_w = 300
 shift_h = 300
 
-# size of the gap between the calibration pattern and the car
+# 标定布的长和宽
-# in horizontal and vertical directions
+cal_w = 3000
-inn_shift_w = 20
+cal_h = 3500
-inn_shift_h = 50
 
-# total width/height of the stitched image
+# 标定布四角的长和宽
-total_w = 800 + 2 * shift_w
+conner_w = 1000
-total_h = 960 + 2 * shift_h
+conner_h = 1000
 
-# four corners of the rectangular region occupied by the car
+# 车辆的长和宽
-# top-left (x_left, y_top), bottom-right (x_right, y_bottom)
+car_w = 300
-xl = shift_w + 180 + inn_shift_w
+car_h = 550
+
+# 车辆与标定布指定四角之间的间隙
+inn_shift_w = (cal_w - 2 * conner_w - car_w)//2
+inn_shift_h = (cal_h - 2 * conner_h - car_h)//2
+
+# 图片的总宽度和总高度
+total_w = cal_w + 2 * shift_w
+total_h = cal_h + 2 * shift_h
+
+# 车辆所占矩形区域的四个角坐标
+# 左上角 (x_left, y_top)，右下角 (x_right, y_bottom)
+xl = shift_w + conner_w + inn_shift_w
 xr = total_w - xl
-yt = shift_h + 200 + inn_shift_h
+yt = shift_h + conner_h + inn_shift_h
 yb = total_h - yt
 # --------------------------------------------------------------------
 
+# 各摄像头投影区域的尺寸
 project_shapes = {
-    "front": (total_w, yt),
+    "front": (total_w, yt),    # 前摄像头：(宽度, 高度)
-    "back":  (total_w, yt),
+    "back":  (total_w, yt),    # 后摄像头：(宽度, 高度)
-    "left":  (total_h, xl),
+    "left":  (total_h, xl),    # 左摄像头：(宽度, 高度)
-    "right": (total_h, xl)
+    "right": (total_h, xl)     # 右摄像头：(宽度, 高度)
 }
 
-# pixel locations of the four points to be chosen.
+# 待选择的四个点的像素位置
-# you must click these pixels in the same order when running
+# 运行 get_projection_map.py 脚本时，必须按相同顺序点击这些像素
-# the get_projection_map.py script
 project_keypoints = {
-    "front": [(shift_w + 200, shift_h),
+    "front": [(shift_w + 200, shift_h),          # 前摄像头的四个关键点坐标
               (shift_w + 2800, shift_h),
               (shift_w + 200, shift_h + 800),
               (shift_w + 2800, shift_h + 800)],
 
-    "back":  [(shift_w + 80, shift_h),
+    "back":  [(shift_w + 80, shift_h),           # 后摄像头的四个关键点坐标
               (shift_w + 320, shift_h),
               (shift_w + 80, shift_h + 200),
               (shift_w + 320, shift_h + 200)],
 
-    "left": [(shift_w + 80, shift_h),
+    "left": [(shift_w + 80, shift_h),            # 左摄像头的四个关键点坐标
               (shift_w + 320, shift_h),
               (shift_w + 80, shift_h + 200),
               (shift_w + 320, shift_h + 200)],
               
-    "right":  [(shift_h + 240, shift_w),
+    "right":  [(shift_h + 240, shift_w),         # 右摄像头的四个关键点坐标
               (shift_h + 560, shift_w),
               (shift_h + 240, shift_w + 120),
               (shift_h + 560, shift_w + 120)],
 }
 
+# 读取车辆图片并调整尺寸以匹配车辆所在区域
 car_image = cv2.imread(os.path.join(os.getcwd(), "images", "car.png"))
 car_image = cv2.resize(car_image, (xr - xl, yb - yt))
+
+# 输出所有参数
+print("--------------------------------------------------------------------")
+print(f"总宽度：{total_w}")
+print(f"总高度：{total_h}")
+print(f"车辆宽度：{car_w}")
+print(f"车辆高度：{car_h}")
+print(f"标定布宽度：{cal_w}")
+print(f"标定布高度：{cal_h}")
+print(f"标定布内四角宽度：{conner_w}")
+print(f"标定布内四角高度：{conner_h}")
+print(f"标定布外扩展宽度：{shift_w}")
+print(f"标定布外扩展高度：{shift_h}")
+print(f"车辆所在区域内间隙宽度：{inn_shift_w}")
+print(f"车辆所在区域内间隙高度：{inn_shift_h}")
+print(f"车辆所在区域左上角：({xl}, {yt})")
+print(f"车辆所在区域右下角：({xr}, {yb})")
+print(f"车辆所在区域四角坐标：{project_keypoints}")
+print(f"投影区域尺寸：{project_shapes}")
+
+print("--------------------------------------------------------------------")

surround_view/simple_gui.py

@@ -45,11 +45,24 @@ class PointSelector(object):
         self.window_width = image.shape[1]
         self.window_height = image.shape[0]
         self.scale = 1.0  # 缩放比例
+        self.last_window_size = (self.window_width, self.window_height)
 
     def draw_image(self):
         """
         Display the selected keypoints and draw the convex hull.
         """
+        # 检查窗口大小是否改变
+        current_width = cv2.getWindowImageRect(self.title)[2] if cv2.getWindowProperty(self.title, cv2.WND_PROP_VISIBLE) >= 1 else self.window_width
+        current_height = cv2.getWindowImageRect(self.title)[3] if cv2.getWindowProperty(self.title, cv2.WND_PROP_VISIBLE) >= 1 else self.window_height
+        
+        if current_width != self.last_window_size[0] or current_height != self.last_window_size[1]:
+            self.window_width = current_width
+            self.window_height = current_height
+            self.last_window_size = (current_width, current_height)
+            # 计算新的缩放比例
+            self.scale = min(current_width / self.original_image.shape[1], 
+                           current_height / self.original_image.shape[0])
+
         # 基于当前缩放比例调整点坐标
         scaled_keypoints = [
             (int(x * self.scale), int(y * self.scale)) 
@@ -91,14 +104,6 @@ class PointSelector(object):
             print(f"click ({orig_x}, {orig_y}) (scaled: ({x}, {y}))")
             self.keypoints.append((orig_x, orig_y))
             self.draw_image()
-        # 窗口大小改变事件
-        elif event == cv2.EVENT_RESIZE:
-            self.window_width = x
-            self.window_height = y
-            # 计算新的缩放比例
-            self.scale = min(x / self.original_image.shape[1], 
-                           y / self.original_image.shape[0])
-            self.draw_image()
 
     def loop(self):
         """
@@ -155,4 +160,4 @@ class PointSelector(object):
         mask = np.array(mask, dtype=np.uint8)
         new_mask = cv2.bitwise_and(new_image, new_image, mask=mask)
         cv2.addWeighted(image, 1.0, new_mask, 0.5, 0.0, image)
-        return image
+        return image

yaml/front.yaml

@@ -4,14 +4,14 @@ camera_matrix: !!opencv-matrix
    rows: 3
    cols: 3
    dt: d
-   data: [ 9.1297883483233306e+02, 0., 1.1530623178818100e+03, 0.,
+   data: [ 912.97883483233306, 0., 1153.06231788181, 0.,
-       9.3430332152657456e+02, 7.0322316308984716e+02, 0., 0., 1. ]
+       934.30332152657456, 703.22316308984716, 0., 0., 1. ]
 dist_coeffs: !!opencv-matrix
    rows: 4
    cols: 1
    dt: d
-   data: [ -2.2262094222158474e-01, 9.1910107741035543e-02,
+   data: [ -0.22262094222158474, 0.091910107741035543,
-       -1.1125663233319319e-01, 5.2563645197652560e-02 ]
+       -0.11125663233319319, 0.05256364519765256 ]
 resolution: !!opencv-matrix
    rows: 2
    cols: 1
@@ -21,17 +21,16 @@ project_matrix: !!opencv-matrix
    rows: 3
    cols: 3
    dt: d
-   data: [ -2.8283572994274103e-01, -1.2971399339352716e+00,
+   data: [ -5.4156645669996912, -9.3442896959392545, 7401.7924039391992,
-       1.0824292174928530e+03, 2.5824309209632131e-02,
+       0.29915864114461999, -10.301469369661787, 5333.8769633702723,
-       -1.3252387496909435e+00, 1.0023621246915676e+03,
+       2.208437292348208e-05, -0.0050239538618510439, 1. ]
-       3.7653658154558861e-05, -1.6884376226546168e-03, 1. ]
 scale_xy: !!opencv-matrix
    rows: 2
    cols: 1
    dt: f
-   data: [ 6.99999988e-01, 8.00000012e-01 ]
+   data: [ 0.699999988, 0.800000012 ]
 shift_xy: !!opencv-matrix
    rows: 2
    cols: 1
    dt: f
-   data: [ -150., -100. ]
+   data: [ -150., -500. ]