first commit

Author: aferral

main.py

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+import os
+import cv2
+import numpy as np
+from utils.bundleAjust import bundleAdjustment
+from utils.dense import denseMatch, denseReconstruction, outputPly
+from utils.fundamental import default, implementacionRansac
+from utils.getPose import getPose
+from utils.graph import createGraph, triangulateGraph, showGraph, visualizeDense
+from utils.mergeGraph import mergeG, removeOutlierPts
+from utils.paresDescript import getPairSIFT
+
+#Creditos a % SFMedu: Structrue From Motion for Education Purpose
+# % Written by Jianxiong Xiao (MIT License) el codigo se base en este
+
+
+def mergeAllGraph(gL,imsize):
+    graphMerged = gL[0]
+    # merge de vistas parciales
+    for i in range(len(gL) - 1):
+        graphMerged = updateMerged(graphMerged, gL[i+1],imageSize)
+    return graphMerged
+def updateMerged(gA,gB,imsize):
+    gt = mergeG(gA, gB)
+    gt = triangulateGraph(gt, imsize)
+    gt = bundleAdjustment(gt, False)
+    gt = removeOutlierPts(gt, 10)
+    gt = bundleAdjustment(gt)
+    return gt
+
+if __name__ == "__main__":
+    maxSize = 640 #maxima resolucion de imagen
+    carpetaImagenes = 'C:/Users/andres/Desktop/SFMedu2/images/cr/'
+    # carpetaImagenes = "C:/Users/andres/Desktop/datasetImagenes/Reduced/jirafa/"
+    # carpetaImagenes = "C:/Users/andres/Desktop/datasetImagenes/Reduced/apple/"
+    debug = True
+    outName = "apple"
+
+    algoMatrizFundamental = implementacionRansac
+
+    #declaraciones
+
+    graphList = []
+
+    #Cargar imagenes
+    listaArchivos = os.listdir(carpetaImagenes)
+    tiposValidos = ['jpg','png','JPG'] #tipos validos de imagenes
+    listaImages = filter(lambda x : x.split('.')[-1] in tiposValidos,listaArchivos )
+
+
+
+    #Intentar conseguir la distancia focal
+    # TODO agregar calculo este valor deberia funcionar con imagenes 480x640 focalLen 4mm
+    f=719.5459
+
+    #Carga las imagenes
+    listaImages = map(lambda x : cv2.imread(carpetaImagenes+x),listaImages)
+
+    imageSize = listaImages[0].shape
+    print "Dimensiones originales ",imageSize
+    #todo Escala la imagen si pasa de maxSize
+    if imageSize[0] > maxSize:
+        print "Escalando"
+        print "Size image ",imageSize," max size ",maxSize
+        #480 640 funciona
+        listaImages = map(lambda x: np.transpose(cv2.resize(x,(640,480)),axes=[1,0,2]), listaImages)
+        imageSize = listaImages[0].shape
+        print "Result size ",imageSize
+
+    #calculo de matriz K
+    K = np.eye(3)
+    K[0][0] = f
+    K[1][1] = f
+
+    graphList = [0 for i in range(len(listaImages)-1)]
+    #calcula pares a partir de SIFT u otro descriptor local
+    #Se calculan como imagenes sucesivas
+    print "Inicia calculo de pares SIFT"
+    for i in range(len(listaImages)-1):
+        keypointsA,keypointsB = getPairSIFT(listaImages[i],listaImages[i+1],show=debug)
+
+
+        #Calcular la matriz fundamental o la matriz escencial
+        #TODO conseguir las demas
+        if type(keypointsA[0]) == np.ndarray:
+            assert(len(keypointsA.shape) == 2)
+            assert (len(keypointsB.shape) == 2)
+            pointsA = keypointsA
+            pointsB = keypointsB
+        else:
+            pointsA = np.array([(keypointsA[idx].pt) for idx in range(len(keypointsA))]).reshape(-1, 1, 2)
+            pointsB = np.array([(keypointsB[idx].pt) for idx in range(len(keypointsB))]).reshape(-1, 1, 2)
+        pointsA = pointsA[:,[1,0]]
+        pointsB = pointsB[:, [1, 0]]
+
+        F = np.array(algoMatrizFundamental(pointsA,pointsB))
+        Fmat = F[0]
+        K = np.array(K)
+        E = np.dot(np.transpose(K),np.dot(Fmat,K))
+
+        # Conseguir pose de las camaras
+        Rtbest = getPose(E,K, np.hstack([pointsA,pointsB]),imageSize)
+
+        #Crear grafico
+        graphList[i] = createGraph(i,i+1,K, pointsA, pointsB, Rtbest, f)
+
+        #Triangular
+        graphList[i] = triangulateGraph(graphList[i],imageSize)
+
+        #visualizar grafico
+        # showGraph(graphList[i],imageSize)
+
+        #Bundle ajustement
+        graphList[i]=bundleAdjustment(graphList[i])
+
+        #Visualiza con mejoras
+        # showGraph(graphList[i], imageSize)
+
+    gM = mergeAllGraph(graphList,imageSize)
+    print "Merge de grafos finalizado"
+    #Visualizar resultado parcial
+    showGraph(gM,imageSize)
+    #Dense matching
+    for i in range(len(listaImages)-1):
+        graphList[i] = denseMatch(graphList[i],listaImages[i],
+                                  listaImages[i+1], imageSize, imageSize)
+
+    print "Dense match finalizado"
+    print "Inicializando dense Triangulation"
+    #Dense reconstruction
+    for i in range(len(listaImages) - 1):
+        graphList[i] = denseReconstruction(graphList[i], gM,K,imageSize)
+    print "Dense reconstruct finalizado"
+    data = visualizeDense(graphList, gM, imageSize)
+
+    outputPly(data,outName)
+
+
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