119 lines
3.3 KiB
Python
119 lines
3.3 KiB
Python
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import os
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import cv2
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camera_names = ["front", "back", "left", "right"]
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# --------------------------------------------------------------------
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# (shift_width, shift_height): 鸟瞰图在水平和垂直方向上超出标定图案的距离
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shift_w = 100
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shift_h = 100
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# 标定图案与车辆之间在水平和垂直方向上的间隙大小
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inn_shift_w = 8
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inn_shift_h = 30
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# 拼接图像的总宽度/高度
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total_w = 260 + 2 * shift_w
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total_h = 350 + 2 * shift_h
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# 计算车辆在全景图中的位置
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xl = shift_w + 55 + inn_shift_w
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xr = total_w - xl
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print(xl, xr)
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yt = shift_h + 55 + inn_shift_h
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yb = total_h - yt
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# --------------------------------------------------------------------
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project_shapes = {
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"front": (total_w, yt),
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"back": (total_w, yt),
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"left": (total_h, xl),
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"right": (total_h, xl)
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}
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# 要选取的四个像素点的位置。
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# 运行 get_projection_map.py 脚本时，必须按相同顺序点击这些像素点。
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project_keypoints = {
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"front": [(shift_w + 0, shift_h),
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(shift_w + 260, shift_h),
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(shift_w + 0, shift_h + 100),
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(shift_w + 260, shift_h + 100)],
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"back": [(shift_w + 0, shift_h),
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(shift_w + 260, shift_h),
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(shift_w + 0, shift_h + 80),
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(shift_w + 260, shift_h + 80)],
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"left": [(shift_h + 60, shift_w),
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(shift_h + 300, shift_w),
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(shift_h + 60, shift_w + 50),
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(shift_h + 300, shift_w + 50)],
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"right": [(shift_h + 80, shift_w),
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(shift_h + 350, shift_w),
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(shift_h + 80, shift_w + 50),
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(shift_h + 350, shift_w + 50)]
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}
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# car_image = cv2.imread(os.path.join(os.getcwd(), "images", "car.png"))
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# car_image = cv2.resize(car_image, (xr - xl, yb - yt))
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# 放大系数（调整这个值控制车辆图大小，1.0 = 原始大小，1.3 = 放大30%）
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CAR_SCALE = 1.3
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car_w = xr - xl
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car_h = yb - yt
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# 放大后的车辆图尺寸
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car_display_w = int(car_w * CAR_SCALE)
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car_display_h = int(car_h * CAR_SCALE)
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# 放大后车辆图在全景图中的起始坐标（居中对齐）
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car_x = xl - (car_display_w - car_w) // 2
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car_y = yt - (car_display_h - car_h) // 2
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def _make_car_image_rgba(path, w, h, tolerance=30):
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# 用 IMREAD_UNCHANGED 保留 PNG 透明通道
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img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
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if img is None:
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return np.zeros((h, w, 4), np.uint8)
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img = cv2.resize(img, (w, h))
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# 如果图片有 alpha 通道，直接用
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if img.shape[2] == 4:
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rgba = img.copy()
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else:
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# 没有 alpha 通道，白色转透明
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rgba = cv2.cvtColor(img, cv2.COLOR_BGR2BGRA)
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white_mask = (
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(rgba[:, :, 0] >= 255 - tolerance) &
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(rgba[:, :, 1] >= 255 - tolerance) &
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(rgba[:, :, 2] >= 255 - tolerance)
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)
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rgba[white_mask, 3] = 0
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# 边缘平滑
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alpha = rgba[:, :, 3].astype(np.uint8)
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alpha = cv2.GaussianBlur(alpha, (5, 5), 0)
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rgba[:, :, 3] = alpha
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return rgba
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import numpy as np
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car_image_path = os.path.join(os.getcwd(), "images", "car.png")
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# 原始尺寸（保持兼容）
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car_image = cv2.imread(car_image_path)
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if car_image is not None:
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car_image = cv2.resize(car_image, (car_w, car_h))
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# 放大版带透明通道
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car_image_rgba = _make_car_image_rgba(car_image_path, car_display_w, car_display_h)
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