cv2函数实践-图像处理（中心外扩的最佳RoI/根据两个坐标点求缩放+偏移后的RoI/滑窗切片/VOC的颜色+调色板）

本文主要是介绍cv2函数实践-图像处理（中心外扩的最佳RoI/根据两个坐标点求缩放+偏移后的RoI/滑窗切片/VOC的颜色+调色板），希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

中心外扩的最佳RoI（裁图）

指定中心点和裁图宽高，获得裁图位置xyxy坐标（最佳），便于在图像裁剪。

def get_best_crop_position_of_center(center_xy, img_w, img_h, crop_w, crop_h):pt = center_xyx0, y0 = max(0, pt[0] - crop_w // 2), max(0, pt[1] - crop_h // 2)  # 左上角 >= (0,0)x1, y1 = min(x0 + crop_w, img_w), min(y0 + crop_h, img_h)  # 右下角return [int(x1-crop_w), int(y1-crop_h), int(x1), int(y1)]

根据两个坐标点求缩放+偏移后的RoI

def get_xyxy_scale_shift(pt1, pt2, scale_xy=1.0, shift=0, imgW=0, imgH=0):"""给定两个坐标点，返回缩放+偏移后的RoI坐标:param pt1, pt2: 两个坐标点:param scale_xy: 缩放比例，还原到原图:param shift: 短边的放大偏移量(长边不变):param imgW: RoI坐标限宽:param imgH: RoI坐标限高"""x0, y0, x1, y1 = min(pt1[0], pt2[0]), min(pt1[1], pt2[1]), max(pt1[0], pt2[0]), max(pt1[1], pt2[1])  # 左上, 右下x0, y0, x1, y1 = round(x0 * scale_xy), round(y0 * scale_xy), round(x1 * scale_xy), round(y1 * scale_xy)  # 缩放，取整if x1 - x0 == y1 - y0:x0, x1 = x0 - shift, x1 + shifty0, y1 = y0 - shift, y1 + shiftelif x1 - x0 < y1 - y0:x0, x1 = x0 - shift, x1 + shiftelse:y0, y1 = y0 - shift, y1 + shiftif imgW > 0:x0 = min(max(0, x0), imgW)x1 = min(max(0, x1), imgW)if imgH > 0:y0 = min(max(0, y0), imgH)y1 = min(max(0, y1), imgH)return int(x0+0.5), int(y0+0.5), int(x1+0.5), int(y1+0.5)

上面函数可以应用在图像上画矩形框，

def draw_RoI(img: np.ndarray, pt1, pt2, scale_xy=1.0, shift=0, color=None, thickness=None):if color is None: color = (0,255,0)imgH, imgW = img.shape[:2]x0, y0, x1, y1 = get_xyxy_scale_shift(pt1, pt2, scale_xy, shift, imgW, imgH)cv2.rectangle(img, (x0, y0), (x1, y1), color, thickness)return x0, y0, x1, y1

自定义RGB2BGR颜色解析小函数

def rgb2bgr(rgb):if isinstance(rgb, (list, tuple)):rgb_list = []for val in rgb:if isinstance(val, str) and val.strip() != '':rgb_list.append(int(val.strip()))elif isinstance(val, int):rgb_list.append(val)return rgb_list[::-1]elif isinstance(rgb, str):bgr = [int(val.strip()) for val in rgb.split(',') if val.strip() != ''][::-1]return bgrelse:raise ValueError("error in converting RGB[" + str(rgb) + "] to BGR")

滑窗切片（sliding window crops）

指定横向和纵向的Windows数，自适应计算每个Window的宽和高，以及滑窗步长，居中对齐，返回每个Window的坐标。

def make_grids(img, grid_x, grid_y, dx=0, dy=0):"""make grids in x-axis and y-axis指定横向和纵向的Windows数，自适应计算每个Window的宽和高，居中对齐，返回每个Window的坐标Args:img: ndarraygrid_x: number of grids in x-axis，指定横向窗口数grid_y: number of grids in y-axis，指定纵向窗口数dx: shrinking size in x-axis，横向窗口间隔的一半dy: shrinking size in y-axis，纵向窗口间隔的一半Returns:[[grid_box]], wheregrid_box = (upleft_pt, downright_pt) = ((x0, y0), (x1, y1))"""grid_boxs = []h, w = img.shape[:2]left_pad, up_pad = (w % grid_x) // 2, (h % grid_y) // 2box_w, box_h = w // grid_x, h // grid_yfor hi in range(grid_y):row_boxs = [((left_pad+wi*box_w+dx, up_pad+hi*box_h+dy),(left_pad+(wi+1)*box_w-dx, up_pad+(hi+1)*box_h-dy))for wi in range(grid_x)]grid_boxs.append(row_boxs)return grid_boxsdef make_grids_sliding(img, grid_x, grid_y, box_w, box_h):"""指定横向和纵向的Windows数 以及窗口大小，有overlapping的滑窗，左右上下紧贴边Args:img: ndarraygrid_x: number of grids in x-axis，指定横向窗口数grid_y: number of grids in y-axis，指定纵向窗口数box_w: width of each box，窗口横向宽度box_h: height of each box，窗口纵向高度Returns:[[grid_box]], wheregrid_box = (upleft_pt, downright_pt) = ((x0, y0), (x1, y1))Examples:[:append]grid_boxs = make_grids_sliding(srcImg, 4, 3, 1280, 1280)for idy, row_boxs in enumerate(grid_boxs):for idx, ((x0, y0), (x1, y1)) in enumerate(row_boxs):cv2.circle(srcImg, ((x0+x1)//2, (y0+y1)//2), 20, color, -1)[:extend]grid_boxs = make_grids_sliding(srcImg, 4, 3, 1280, 1280)for (x0, y0), (x1, y1) in grid_boxs:cv2.circle(srcImg, ((x0+x1)//2, (y0+y1)//2), 20, color, -1)"""grid_boxs = []h, w = img.shape[:2]# box_h, box_w = min(h, box_h), min(w, box_w)  # 保证：窗口大小 <= 原图大小lt_x0y0, rd_x0y0 = (0, 0), (max(0, w-box_w), max(0, h-box_h))  # 左上角窗口、右下角窗口的左上角坐标x0linspace = [int(x0) for x0 in np.linspace(lt_x0y0[0], rd_x0y0[0], grid_x)]y0linspace = [int(y0) for y0 in np.linspace(lt_x0y0[1], rd_x0y0[1], grid_y)]for y0 in y0linspace:row_boxs = [((x0, y0), (x0+box_w, y0+box_h))for x0 in x0linspace]  # 左上角、右下角grid_boxs.extend(row_boxs)  # .appendreturn grid_boxsif __name__ == '__main__':srcImg = np.zeros((2000, 2000, 3), dtype=np.uint8)grid_boxs = make_grids_sliding(srcImg, 2, 2, 1280, 1280)print(grid_boxs)# 在crop_srcImg上滑动窗口裁图，将grid_boxs从crop_srcImg映射回srcImgpx0, py0 = 10, 10for idy, row_boxs in enumerate(grid_boxs):(x0, y0), (x1, y1) = row_boxsgrid_boxs[idy] = ((x0 + px0, y0 + py0), (x1 + px0, y1 + py0))print(grid_boxs)

VOC的颜色+调色板

通过位运算，巧妙地生成有梯度（相差128个灰度值）的RGB颜色表，相比打表可快多了。

def create_pascal_label_colormap():"""PASCAL VOC 分割数据集的类别标签颜色映射label colormap返回:可视化分割结果的颜色映射ColormapExamples:colormap = create_pascal_label_colormap()color = colormap[idx].tolist()  # [b, g, r]# 分割结果label.shape=(1024,1024)，渲染图vis.shape=(1024,1024,3)vis = colormap[label]"""colormap = np.zeros((256, 3), dtype=int)ind = np.arange(256, dtype=int)for shift in reversed(range(8)):for channel in range(3):colormap[:, channel] |= ((ind >> channel) & 1) << shiftind >>= 3return colormap

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