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| ## 处理pred结果的.json文件,画图 | |
| import matplotlib.pyplot as plt | |
| import cv2 | |
| import numpy as np | |
| import random | |
| def plot_img_and_mask(img, mask, index,epoch,save_dir): | |
| classes = mask.shape[2] if len(mask.shape) > 2 else 1 | |
| fig, ax = plt.subplots(1, classes + 1) | |
| ax[0].set_title('Input image') | |
| ax[0].imshow(img) | |
| if classes > 1: | |
| for i in range(classes): | |
| ax[i+1].set_title(f'Output mask (class {i+1})') | |
| ax[i+1].imshow(mask[:, :, i]) | |
| else: | |
| ax[1].set_title(f'Output mask') | |
| ax[1].imshow(mask) | |
| plt.xticks([]), plt.yticks([]) | |
| # plt.show() | |
| plt.savefig(save_dir+"/batch_{}_{}_seg.png".format(epoch,index)) | |
| def show_seg_result(img, result, index, epoch, save_dir=None, is_ll=False,palette=None,is_demo=False,is_gt=False): | |
| # img = mmcv.imread(img) | |
| # img = img.copy() | |
| # seg = result[0] | |
| if palette is None: | |
| palette = np.random.randint( | |
| 0, 255, size=(3, 3)) | |
| palette[0] = [0, 0, 0] | |
| palette[1] = [0, 255, 0] | |
| palette[2] = [255, 0, 0] | |
| palette = np.array(palette) | |
| assert palette.shape[0] == 3 # len(classes) | |
| assert palette.shape[1] == 3 | |
| assert len(palette.shape) == 2 | |
| if not is_demo: | |
| color_seg = np.zeros((result.shape[0], result.shape[1], 3), dtype=np.uint8) | |
| for label, color in enumerate(palette): | |
| color_seg[result == label, :] = color | |
| else: | |
| color_area = np.zeros((result[0].shape[0], result[0].shape[1], 3), dtype=np.uint8) | |
| # for label, color in enumerate(palette): | |
| # color_area[result[0] == label, :] = color | |
| color_area[result[0] == 1] = [0, 255, 0] | |
| color_area[result[1] ==1] = [255, 0, 0] | |
| color_seg = color_area | |
| # convert to BGR | |
| color_seg = color_seg[..., ::-1] | |
| # print(color_seg.shape) | |
| color_mask = np.mean(color_seg, 2) | |
| img[color_mask != 0] = img[color_mask != 0] * 0.5 + color_seg[color_mask != 0] * 0.5 | |
| # img = img * 0.5 + color_seg * 0.5 | |
| img = img.astype(np.uint8) | |
| img = cv2.resize(img, (1280,720), interpolation=cv2.INTER_LINEAR) | |
| if not is_demo: | |
| if not is_gt: | |
| if not is_ll: | |
| cv2.imwrite(save_dir+"/batch_{}_{}_da_segresult.png".format(epoch,index), img) | |
| else: | |
| cv2.imwrite(save_dir+"/batch_{}_{}_ll_segresult.png".format(epoch,index), img) | |
| else: | |
| if not is_ll: | |
| cv2.imwrite(save_dir+"/batch_{}_{}_da_seg_gt.png".format(epoch,index), img) | |
| else: | |
| cv2.imwrite(save_dir+"/batch_{}_{}_ll_seg_gt.png".format(epoch,index), img) | |
| return img | |
| def plot_one_box(x, img, color=None, label=None, line_thickness=None): | |
| # Plots one bounding box on image img | |
| tl = line_thickness or round(0.0001 * (img.shape[0] + img.shape[1]) / 2) + 1 # line/font thickness | |
| color = color or [random.randint(0, 255) for _ in range(3)] | |
| c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3])) | |
| cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA) | |
| # if label: | |
| # tf = max(tl - 1, 1) # font thickness | |
| # t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0] | |
| # c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3 | |
| # cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA) # filled | |
| # print(label) | |
| # cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA) | |
| if __name__ == "__main__": | |
| pass | |