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6 changed files with 270 additions and 21 deletions
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# -*- coding: utf-8 -*- |
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''' |
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@Author: captainfffsama |
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@Date: 2023-02-03 10:03:55 |
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@LastEditors: captainfffsama tuanzhangsama@outlook.com |
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@LastEditTime: 2023-02-03 10:03:56 |
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@FilePath: /LoFTR/src/grpc/__init__.py |
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@Description: |
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''' |
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# -*- coding: utf-8 -*- |
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''' |
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@Author: captainfffsama |
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@Date: 2023-02-03 09:56:39 |
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@LastEditors: captainfffsama tuanzhangsama@outlook.com |
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@LastEditTime: 2023-02-03 10:12:32 |
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@FilePath: /LoFTR/src/grpc/debug_tools.py |
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@Description: |
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''' |
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import os |
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from typing import Optional |
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import math |
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from copy import deepcopy |
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import random |
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import cv2 |
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import matplotlib.pyplot as plt |
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import matplotlib.colors as mcolors |
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import matplotlib.patches as patches |
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import numpy as np |
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import torch |
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from torchvision.utils import make_grid |
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COLOR_DICT=mcolors.CSS4_COLORS |
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def get_color(): |
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return random.sample(COLOR_DICT.keys(),1)[0] |
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def normalization(data): |
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_range = np.max(data) - np.min(data) |
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return (data - np.min(data)) / _range *255 |
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def show_img(img_ori,text:Optional[str]=None,cvreader:bool=True,delay:float=0): |
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img=deepcopy(img_ori) |
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if isinstance(img, list) or isinstance(img, tuple): |
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img_num = len(img) |
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row_n = math.ceil(math.sqrt(img_num)) |
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col_n = max(math.ceil(img_num / row_n), 1) |
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fig, axs = plt.subplots(row_n, col_n, figsize=(15 * row_n, 15 * col_n)) |
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for idx, img_ in enumerate(img): |
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if isinstance(img_,torch.Tensor) or isinstance(img_,np.ndarray): |
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img_=show_tensor(img_,cvreader) |
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if 2 == len(axs.shape): |
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axs[idx % row_n][idx // row_n].imshow(img_) |
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axs[idx % row_n][idx // row_n].set_title(str(idx)) |
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else: |
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axs[idx % row_n].imshow(img_) |
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axs[idx % row_n].set_title(str(idx)) |
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if text: |
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plt.text(0,0,text,fontsize=15) |
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if delay <=0: |
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plt.show() |
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else: |
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plt.draw() |
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plt.pause(delay) |
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plt.close() |
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elif isinstance(img,torch.Tensor) or isinstance(img,np.ndarray): |
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img=show_tensor(img,cvreader) |
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plt.imshow(img) |
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if text: |
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plt.text(0,0,text,fontsize=15) |
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if delay <=0: |
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plt.show() |
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else: |
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plt.draw() |
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plt.pause(delay) |
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plt.close() |
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else: |
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if hasattr(img,'show'): |
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img.show() |
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def show_tensor(img,cvreader): |
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if len(img.shape) == 4 and img.shape[0] !=1: |
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if isinstance(img,np.ndarray): |
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img=torch.Tensor(img) |
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img=make_grid(img) |
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else: |
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img=make_grid(img) |
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img: np.ndarray = img.detach().cpu().numpy().squeeze() |
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if isinstance(img,torch.Tensor): |
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img=img.detach().cpu().numpy().squeeze() |
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if 1==img.max(): |
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img=img.astype(np.float) |
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if img.shape[0] == 1 or img.shape[0] == 3: |
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img = np.transpose(img, (1, 2, 0)) |
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if img.min() <0 or img.max() >255: |
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img=normalization(img) |
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print("img have norm") |
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if img.shape[-1] == 3: |
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img=img.astype(np.uint8) |
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if cvreader and len(img.shape)==3: |
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img=img[:,:,::-1] |
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return img |
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def draw_point(axs,kp:np.ndarray,color:str): |
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kp=kp.astype(int) |
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axs.plot(kp[:,0],kp[:,1],'o',color=color) |
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def plot_kp(debug_info,show_flag=('fake','true','no_match',"vis"),debug_save=""): |
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fig, axs = plt.subplots(2, 1) |
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axs[0].set_xticks([]) |
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axs[0].set_yticks([]) |
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axs[0].imshow(debug_info.imgA[:,:,::-1]) |
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axs[1].set_yticks([]) |
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axs[1].set_yticks([]) |
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axs[1].imshow(debug_info.imgB[:,:,::-1]) |
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if "fake" in show_flag: |
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draw_point(axs[0],debug_info.pts_info.kp0_fake_match,'y') |
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draw_point(axs[1],debug_info.pts_info.kp1_fake_match,'y') |
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for kp_a,kp_b in zip(debug_info.pts_info.kp0_fake_match,debug_info.pts_info.kp1_fake_match): |
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color=get_color() |
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con = patches.ConnectionPatch( |
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xyA=kp_a, coordsA=axs[0].transData, |
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xyB=kp_b, coordsB=axs[1].transData, |
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arrowstyle="-", shrinkB=5,color=color) |
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axs[1].add_artist(con) |
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if "true" in show_flag: |
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draw_point(axs[0],debug_info.pts_info.kp0_true_match,'g') |
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draw_point(axs[1],debug_info.pts_info.kp1_true_match,'g') |
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for kp_a,kp_b in zip(debug_info.pts_info.kp0_true_match,debug_info.pts_info.kp1_true_match): |
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color=get_color() |
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con = patches.ConnectionPatch( |
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xyA=kp_a, coordsA=axs[0].transData, |
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xyB=kp_b, coordsB=axs[1].transData, |
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arrowstyle="-", shrinkB=5,color=color) |
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axs[1].add_artist(con) |
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if "vis" in show_flag: |
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fig2=plt.figure() |
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axe2=fig2.gca() |
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fix_img=generate_transform_pic((debug_info.imgB.shape[1],debug_info.imgB.shape[0]),debug_info.imgA,debug_info.H) |
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final_img=cv2.addWeighted(debug_info.imgB,0.6,fix_img,0.4,0) |
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axe2.imshow(final_img[:,:,::-1]) |
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if isinstance(debug_save,str): |
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save_path,ext=os.path.splitext(debug_save) |
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fig.savefig(save_path+"nft.jpg",dpi=400,bbox_inches='tight') |
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fig2.savefig(save_path+"vis.jpg",dpi=400,bbox_inches='tight') |
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plt.clf() |
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plt.close("all") |
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else: |
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plt.clf() |
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plt.show() |
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def convertpt2matrix(pts): |
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if isinstance(pts,list): |
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pts=np.array([x+[1] for x in pts]) |
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if pts.shape[-1]==2: |
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expend_m=np.array([[1],[1],[1],[1]]) |
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pts=np.concatenate((pts,expend_m),axis=1) |
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return pts |
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def generate_transform_pic(imgb_shape,imgA,H): |
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imgA_warp = cv2.warpPerspective(imgA, |
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H, imgb_shape, |
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flags=cv2.INTER_LINEAR) |
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k_pts=[[0,0],[0,imgA.shape[0]-1],[imgA.shape[1]-1,imgA.shape[0]-1],[imgA.shape[1]-1,0]] |
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kptm=convertpt2matrix(k_pts) |
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warp_kpm=np.matmul(H,kptm.T) |
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warp_kpm=(warp_kpm/warp_kpm[2,:])[:2,:].astype(int).T.reshape((-1,1,2)) |
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# FIXME:这里可能有问题,没做越界 |
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cv2.polylines(imgA_warp,[warp_kpm],True,(0,0,255),thickness=5) |
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return imgA_warp |
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