添加rpc

.gitignore

@@ -6,6 +6,7 @@ __pycache__/
 *.pth
 tmp.*
 */.ipynb_checkpoints/*
+test/
 
 logs/
 weights/

src/grpc/__init__.py

@@ -0,0 +1,9 @@
+# -*- coding: utf-8 -*-
+'''
+@Author: captainfffsama
+@Date: 2023-02-03 10:03:55
+@LastEditors: captainfffsama tuanzhangsama@outlook.com
+@LastEditTime: 2023-02-03 10:03:56
+@FilePath: /LoFTR/src/grpc/__init__.py
+@Description:
+'''

src/grpc/base_cfg.py

@@ -3,7 +3,7 @@
 @Author: captainfffsama
 @Date: 2023-02-02 16:40:37
 @LastEditors: captainfffsama tuanzhangsama@outlook.com
-@LastEditTime: 2023-02-02 16:42:41
+@LastEditTime: 2023-02-03 10:34:31
 @FilePath: /LoFTR/src/grpc/base_cfg.py
 @Description:
 '''
@@ -15,8 +15,19 @@ param = dict(grpc=dict(host='127.0.0.1',
                        max_workers=10,
                        max_send_message_length=100 * 1024 * 1024,
                        max_receive_message_length=100 * 1024 * 1024),
-             loftr=dict(ckpt_path="",device="cuda:0",thr=0.5,ransc_method="USAC_MAGSAC",ransc_thr=3,ransc_max_iter=2000,
+             loftr=dict(ckpt_path="",
-))
+                        img_size=(640, 480),
+                        device="cuda:0",
+                        thr=0.5,
+                        ransc_method="USAC_MAGSAC",
+                        ransc_thr=3,
+                        ransc_max_iter=2000,
+                        debug=False,
+                        debug_show_type=(
+                            "vis",
+                            "false",
+                            "true",
+                        )))
 
 
 def _update(dic1: dict, dic2: dict):
@@ -60,4 +71,3 @@ def merge_param(file_path: str):
         raise ValueError('{} is not support'.format(cfg_ext))
     else:
         globals()[func_name](file_path)
-

src/grpc/debug_tools.py

@@ -0,0 +1,174 @@
+# -*- coding: utf-8 -*-
+'''
+@Author: captainfffsama
+@Date: 2023-02-03 09:56:39
+@LastEditors: captainfffsama tuanzhangsama@outlook.com
+@LastEditTime: 2023-02-03 10:12:32
+@FilePath: /LoFTR/src/grpc/debug_tools.py
+@Description:
+'''
+import os
+from typing import Optional
+import math
+from copy import deepcopy
+import random
+
+import cv2
+import matplotlib.pyplot as plt
+import matplotlib.colors as mcolors
+import matplotlib.patches as patches
+import numpy as np
+import torch
+from torchvision.utils import make_grid
+
+
+COLOR_DICT=mcolors.CSS4_COLORS
+
+def get_color():
+    return random.sample(COLOR_DICT.keys(),1)[0]
+
+def normalization(data):
+    _range = np.max(data) - np.min(data)
+    return (data - np.min(data)) / _range *255
+
+
+def show_img(img_ori,text:Optional[str]=None,cvreader:bool=True,delay:float=0):
+    img=deepcopy(img_ori)
+    if isinstance(img, list) or isinstance(img, tuple):
+        img_num = len(img)
+        row_n = math.ceil(math.sqrt(img_num))
+        col_n = max(math.ceil(img_num / row_n), 1)
+        fig, axs = plt.subplots(row_n, col_n, figsize=(15 * row_n, 15 * col_n))
+        for idx, img_ in enumerate(img):
+            if isinstance(img_,torch.Tensor) or isinstance(img_,np.ndarray):
+                img_=show_tensor(img_,cvreader)
+            if 2 == len(axs.shape):
+                axs[idx % row_n][idx // row_n].imshow(img_)
+                axs[idx % row_n][idx // row_n].set_title(str(idx))
+            else:
+                axs[idx % row_n].imshow(img_)
+                axs[idx % row_n].set_title(str(idx))
+        if text:
+            plt.text(0,0,text,fontsize=15)
+        if delay <=0:
+            plt.show()
+        else:
+            plt.draw()
+            plt.pause(delay)
+            plt.close()
+    elif isinstance(img,torch.Tensor) or isinstance(img,np.ndarray):
+        img=show_tensor(img,cvreader)
+        plt.imshow(img)
+        if text:
+            plt.text(0,0,text,fontsize=15)
+        if delay <=0:
+            plt.show()
+        else:
+            plt.draw()
+            plt.pause(delay)
+            plt.close()
+    else:
+        if hasattr(img,'show'):
+            img.show()
+
+
+def show_tensor(img,cvreader):
+    if len(img.shape) == 4 and img.shape[0] !=1:
+        if isinstance(img,np.ndarray):
+            img=torch.Tensor(img)
+            img=make_grid(img)
+        else:
+            img=make_grid(img)
+        img: np.ndarray = img.detach().cpu().numpy().squeeze()
+    if isinstance(img,torch.Tensor):
+        img=img.detach().cpu().numpy().squeeze()
+    if 1==img.max():
+        img=img.astype(np.float)
+    if img.shape[0] == 1 or img.shape[0] == 3:
+        img = np.transpose(img, (1, 2, 0))
+    if img.min() <0 or img.max() >255:
+        img=normalization(img)
+        print("img have norm")
+        if img.shape[-1] == 3:
+            img=img.astype(np.uint8)
+    if cvreader and len(img.shape)==3:
+        img=img[:,:,::-1]
+    return img
+
+def draw_point(axs,kp:np.ndarray,color:str):
+    kp=kp.astype(int)
+    axs.plot(kp[:,0],kp[:,1],'o',color=color)
+
+def plot_kp(debug_info,show_flag=('fake','true','no_match',"vis"),debug_save=""):
+    fig, axs = plt.subplots(2, 1)
+    axs[0].set_xticks([])
+    axs[0].set_yticks([])
+    axs[0].imshow(debug_info.imgA[:,:,::-1])
+
+    axs[1].set_yticks([])
+    axs[1].set_yticks([])
+    axs[1].imshow(debug_info.imgB[:,:,::-1])
+
+    if "fake" in show_flag:
+        draw_point(axs[0],debug_info.pts_info.kp0_fake_match,'y')
+        draw_point(axs[1],debug_info.pts_info.kp1_fake_match,'y')
+        for kp_a,kp_b in zip(debug_info.pts_info.kp0_fake_match,debug_info.pts_info.kp1_fake_match):
+            color=get_color()
+            con =  patches.ConnectionPatch(
+                xyA=kp_a, coordsA=axs[0].transData,
+                xyB=kp_b, coordsB=axs[1].transData,
+                arrowstyle="-", shrinkB=5,color=color)
+            axs[1].add_artist(con)
+
+    if "true" in show_flag:
+        draw_point(axs[0],debug_info.pts_info.kp0_true_match,'g')
+        draw_point(axs[1],debug_info.pts_info.kp1_true_match,'g')
+        for kp_a,kp_b in zip(debug_info.pts_info.kp0_true_match,debug_info.pts_info.kp1_true_match):
+            color=get_color()
+            con =  patches.ConnectionPatch(
+                xyA=kp_a, coordsA=axs[0].transData,
+                xyB=kp_b, coordsB=axs[1].transData,
+                arrowstyle="-", shrinkB=5,color=color)
+            axs[1].add_artist(con)
+
+    if "vis" in show_flag:
+        fig2=plt.figure()
+        axe2=fig2.gca()
+        fix_img=generate_transform_pic((debug_info.imgB.shape[1],debug_info.imgB.shape[0]),debug_info.imgA,debug_info.H)
+        final_img=cv2.addWeighted(debug_info.imgB,0.6,fix_img,0.4,0)
+        axe2.imshow(final_img[:,:,::-1])
+
+
+    if isinstance(debug_save,str):
+        save_path,ext=os.path.splitext(debug_save)
+        fig.savefig(save_path+"nft.jpg",dpi=400,bbox_inches='tight')
+        fig2.savefig(save_path+"vis.jpg",dpi=400,bbox_inches='tight')
+        plt.clf()
+        plt.close("all")
+    else:
+        plt.clf()
+        plt.show()
+
+def convertpt2matrix(pts):
+    if isinstance(pts,list):
+        pts=np.array([x+[1] for x in pts])
+    if pts.shape[-1]==2:
+        expend_m=np.array([[1],[1],[1],[1]])
+        pts=np.concatenate((pts,expend_m),axis=1)
+    return pts
+
+def generate_transform_pic(imgb_shape,imgA,H):
+    imgA_warp = cv2.warpPerspective(imgA,
+                                    H, imgb_shape,
+                                    flags=cv2.INTER_LINEAR)
+
+    k_pts=[[0,0],[0,imgA.shape[0]-1],[imgA.shape[1]-1,imgA.shape[0]-1],[imgA.shape[1]-1,0]]
+
+    kptm=convertpt2matrix(k_pts)
+    warp_kpm=np.matmul(H,kptm.T)
+    warp_kpm=(warp_kpm/warp_kpm[2,:])[:2,:].astype(int).T.reshape((-1,1,2))
+    # FIXME:这里可能有问题,没做越界
+    cv2.polylines(imgA_warp,[warp_kpm],True,(0,0,255),thickness=5)
+    return imgA_warp
+
+

src/grpc/loftr_worker.py

@@ -5,23 +5,53 @@
 @Description:
 '''
 from collections import namedtuple
+from typing import Optional, Union
 
 import torch
 import cv2
 import numpy as np
 
 from src.loftr import LoFTR, default_cfg
+from .debug_tools import plot_kp
 
-DebugInfo = namedtuple(
+KeyPointsDebugInfo = namedtuple(
-    "DebugInfo",
+    "KeyPointsDebugInfo",
     ["kp0_fake_match", "kp1_fake_match", "kp0_true_match", "kp1_true_match"])
 
 
+class DebugInfoCollector(object):
+
+    def __init__(self,
+                 imgA: Optional[np.ndarray] = None,
+                 imgB: Optional[np.ndarray] = None,
+                 pts_info: Optional[KeyPointsDebugInfo] = None,
+                 H: Optional[np.ndarray] = None):
+        self.imgA = imgA
+        self.imgB = imgB
+        self.pts_info = pts_info
+        self.H = H
+
+    def clean(self):
+        self.imgA = None
+        self.imgB = None
+        self.pts_info = None
+        self.H = None
+
+    def __str__(self):
+        return "{}".format({
+            "imgA": self.imgA,
+            "imgB": self.imgB,
+            "pts_info": self.pts_info,
+            "H": self.H
+        })
+
+
 class LoFTRWorker(object):
 
     def __init__(self,
                  config,
                  ckpt_path,
+                 img_size=(640, 480),
                  device="cuda:0",
                  thr=0.5,
                  ransc_method="USAC_MAGSAC",
@@ -37,13 +67,16 @@ class LoFTRWorker(object):
         self.ransc_method = getattr(cv2, ransc_method)
         self.ransc_thr = ransc_thr
         self.ransc_max_iter = ransc_max_iter
+        self.img_size = img_size
 
-    def _imgdeal(self, img):
+    def _img2gray(self, img):
         if len(img.shape) == 3 and img.shape[-1] == 3:
             img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        return img
 
+    def _imgdeal(self, img):
         oh, ow = img.shape[:2]
-        img = cv2.resize(img, (640, 480))
+        img = cv2.resize(img, self.img_size)
         h, w = img.shape[:2]
         fix_matrix = np.array([[w / ow, 0, 0], [0, h / oh, 0], [0, 0, 1]])
         return img, fix_matrix
@@ -51,9 +84,19 @@ class LoFTRWorker(object):
     def _fix_H(self, fm0, fm1, H):
         return np.linalg.inv(fm0) @ H @ fm1
 
-    def __call__(self, img0, img1, debug=""):
+    def __call__(self,
-        img0, fm0 = self._imgdeal(img0)
+                 img0,
-        img1, fm1 = self._imgdeal(img1)
+                 img1,
+                 debug: Union[bool, str] = False,
+                 debug_show_type: tuple = (
+                     "vis",
+                     "false",
+                     "true",
+                 )):
+        img0_o, fm0 = self._imgdeal(img0)
+        img1_o, fm1 = self._imgdeal(img1)
+        img0 = self._img2gray(img0_o)
+        img1 = self._img2gray(img1_o)
         img0 = torch.from_numpy(img0)[None][None].cuda() / 255.
         img1 = torch.from_numpy(img1)[None][None].cuda() / 255.
 
@@ -69,10 +112,9 @@ class LoFTRWorker(object):
         mkpts0 = mkpts0[idx]
         mkpts1 = mkpts1[idx]
 
-        debug_info = None
         H = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype=np.float)
         if mkpts0.shape[0] < 4 or mkpts1.shape[0] < 4:
-            return self._fix_H(fm0, fm1, H), False, debug_info
+            return self._fix_H(fm0, fm1, H), False
 
         H, Mask = cv2.findHomography(mkpts0[:, :2],
                                      mkpts1[:, :2],
@@ -87,10 +129,12 @@ class LoFTRWorker(object):
             kp0_fake_matched = mkpts0[~Mask.astype(bool), :2]
             kp1_fake_matched = mkpts1[~Mask.astype(bool), :2]
 
-            debug_info = DebugInfo(kp0_fake_matched, kp1_fake_matched,
+            kpdi = KeyPointsDebugInfo(kp0_fake_matched, kp1_fake_matched,
-                                   kp0_true_matched, kp1_true_matched)
+                                      kp0_true_matched, kp1_true_matched)
 
+            debug_info = DebugInfoCollector(img0_o, img1_o, kpdi, H)
+            plot_kp(debug_info, show_flag=debug_show_type, debug_save=debug)
         if H is None:
-            return self._fix_H(fm0, fm1, H), False, debug_info
+            return self._fix_H(fm0, fm1, H), False
         else:
-            return self._fix_H(fm0, fm1, H), True, debug_info
+            return self._fix_H(fm0, fm1, H), True

src/grpc/server.py

@@ -2,7 +2,7 @@
 @Author: captainfffsama
 @Date: 2023-02-02 15:59:46
 @LastEditors: captainfffsama tuanzhangsama@outlook.com
-@LastEditTime: 2023-02-02 16:43:55
+@LastEditTime: 2023-02-03 10:35:44
 @FilePath: /LoFTR/src/grpc/server.py
 @Description:
 '''
@@ -18,25 +18,36 @@ from .utils import decode_img_from_proto, np2tensor_proto, img2pb_img
 
 
 class LoFTRServer(LoftrServicer):
+
     def __init__(self,
                  ckpt_path,
+                 img_size=(640, 480),
                  device="cuda:0",
                  thr=0.5,
                  ransc_method="USAC_MAGSAC",
                  ransc_thr=3,
                  ransc_max_iter=2000,
+                 debug=False,
+                 debug_show_type=(
+                     "vis",
+                     "false",
+                     "true",
+                 ),
                  *args,
                  **kwargs):
         super().__init__(*args, **kwargs)
         self.worker = LoFTRWorker(
             default_cfg,
             ckpt_path,
+            img_size,
             device,
             thr,
             ransc_method,
             ransc_thr,
             ransc_max_iter,
         )
+        self.debug = debug
+        self.debug_show_type = debug_show_type
 
     def getEssentialMatrix(self, request, context):
         imgA = decode_img_from_proto(request.imageA)
@@ -44,8 +55,8 @@ class LoFTRServer(LoftrServicer):
         if imgA is None or imgB is None:
             return loftr_pb2.GetEssentialMatrixReply(matrix=np2tensor_proto(
                 np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype=np.float)),
-                                                    status=-14)
+                                                     status=-14)
-        H, flag = self.worker(imgA, imgB)
+        H, flag = self.worker(imgA, imgB, self.debug, self.debug_show_type)
         status = 0 if flag else -14
         return loftr_pb2.GetEssentialMatrixReply(matrix=np2tensor_proto(H),
-                                                status=status)
+                                                 status=status)