.

Main.py

@@ -0,0 +1,63 @@
+import time
+from runMe import *
+from busProjectTest import runTest
+import sys
+import os
+
+class my_time:
+
+    def tic(self):
+        self.t = time.time()
+
+    def toc(self):
+        self.elapsed = float(time.time()) - float(self.t)
+        s = "elapsed time is %0.3f seconds" % self.elapsed
+        print(s)
+        return self.elapsed
+
+i = 0
+saveDir = None
+while(i < len(sys.argv)):
+    arg = sys.argv[i]
+    print(arg)
+    if('-myAnns' in arg):
+        myAnnFileName = sys.argv[i + 1]
+        myAnnFileName = os.path.join(os.getcwd(), myAnnFileName)
+    if ('-anns' in arg):
+        annFileNameGT = sys.argv[i + 1]
+        annFileNameGT = os.path.join(os.getcwd(), annFileNameGT)
+    if('-buses' in arg):
+        busDir = sys.argv[i + 1]
+        busDir = os.path.join(os.getcwd(), busDir)
+    if ('-saveDir' in arg):
+        saveDir = sys.argv[i + 1]
+        saveDir = os.path.join(os.getcwd(), saveDir)
+        if(not os.path.exists(saveDir)):
+            os.mkdir(saveDir)
+    if('-h' in arg):
+        print('This script compares the ground truth (GT) and the estimated detections\nUsage: \n-myAnns <estimated annotations file>')
+        print('-anns <real annotations file> \n-buses <directory of the training images>')
+        print('-saveDir <output directory> use this option if you want to save the images with the annotations')
+        print('example:\npython busProjectTest.py -myAnns myannotations.txt -anns anotationsTrain.txt -buses busesDir -saveDir Figures')
+        print('-h - show this message and exit')
+        sys.exit()
+    i += 1
+
+if(i == 0):
+    print('This script compares the ground truth (GT) and the estimated detections\nUsage: \n-myAnns <estimated annotations file>')
+    print('-anns <real annotations file> \n-buses <directory of the training images>')
+    print('-saveDir <output directory> use this option if you want to save the images with the annotations')
+    print('example:\npython busProjectTest.py -myAnns myannotations.txt -anns anotationsTrain.txt -buses busesDir -saveDir Figures')
+    print('-h - show this message and exit')
+    sys.exit()
+
+t = my_time()
+
+t.tic()
+
+run(myAnnFileName, busDir)
+
+elapsed = t.toc()
+
+runTest(annFileNameGT, myAnnFileName, busDir , saveDir, elapsed)
+

Original Pictures/originalAnnotationsTrain.txt

@@ -0,0 +1,60 @@
+DSCF1013.JPG:[1217,1690,489,201,1],[1774,1619,475,224,2],[2313,1566,460,228,3],[1284,1832,497,231,4],[1879,1798,486,228,5],[2429,1742,475,228,6]
+DSCF1015.JPG:[641,1342,1181,892,3],[2053,1022,1122,735,6]
+DSCF1016.JPG:[1067,1843,1114,613,4],[1954,1278,1021,561,6],[2392,717,964,635,3]
+DSCF1017.JPG:[1834,698,789,422,4]
+DSCF1021.JPG:[1991,755,923,546,5]
+DSCF1023.JPG:[1909,1166,785,452,2]
+DSCF1025.JPG:[985,1570,1252,710,6]
+DSCF1029.JPG:[129,1648,1271,733,6],[1456,1076,1122,834,2],[2552,826,1058,804,1]
+DSCF1032.JPG:[144,1200,983,467,5],[2339,1125,916,456,6]
+DSCF1037.JPG:[1210,1712,1469,643,1]
+DSCF1038.JPG:[1210,1764,1754,796,2]
+DSCF1040.JPG:[462,1813,976,546,2],[1445,1791,871,482,1],[2305,1779,785,463,4]
+DSCF1042.JPG:[2021,1682,1013,504,5]
+DSCF1044.JPG:[1460,1200,946,523,6]
+DSCF1048.JPG:[443,1230,665,314,4],[1498,1181,665,295,5],[2511,1099,703,310,3]
+DSCF1049.JPG:[480,998,789,388,4],[1718,1192,740,396,5],[2758,1364,706,347,3]
+DSCF1050.JPG:[454,766,804,366,5],[1767,807,792,366,3]
+DSCF1053.JPG:[925,1536,1006,516,1],[2010,1510,1013,523,6]
+DSCF1056.JPG:[469,957,1234,632,2],[1700,953,1219,662,6]
+DSCF1059.JPG:[1221,994,1103,706,2]
+DSCF1061.JPG:[488,1648,1361,792,6],[1191,1203,1144,632,2]
+DSCF1064.JPG:[2399,358,811,519,3],[585,740,927,579,2]
+DSCF1065.JPG:[705,1031,811,306,5],[2193,908,766,306,1]
+DSCF1066.JPG:[1019,1121,718,332,1],[2025,1121,695,321,5]
+DSCF1068.JPG:[750,343,1316,501,5],[2459,1533,957,527,1]
+DSCF1070.JPG:[1617,1413,1028,583,2],[1528,990,878,523,6]
+DSCF1072.JPG:[746,1211,916,549,3],[1905,1465,968,534,2]
+DSCF1074.JPG:[1127,1319,1742,964,2]
+DSCF1077.JPG:[308,1282,1110,620,6],[1700,1342,1133,624,1]
+DSCF1081.JPG:[1475,1282,714,396,4]
+DSCF1082.JPG:[996,579,957,534,5],[2118,601,946,542,4]
+DSCF1086.JPG:[716,975,609,332,4],[1471,971,602,336,2],[2175,1005,594,344,1],[2878,990,602,359,3]
+DSCF1088.JPG:[1232,440,789,422,5],[1326,1525,654,370,2]
+DSCF1090.JPG:[1920,784,673,370,1]
+DSCF1092.JPG:[424,1293,867,538,4],[1636,1521,890,516,5],[2481,1028,751,504,3]
+DSCF1094.JPG:[1378,1035,991,583,4]
+DSCF1097.JPG:[1326,642,1155,635,5]
+DSCF1099.JPG:[1112,556,1114,564,6]
+DSCF1101.JPG:[1015,1072,916,504,2]
+DSCF1103.JPG:[1475,541,691,359,6],[1168,1880,849,516,5]
+DSCF1104.JPG:[1715,493,684,329,5],[1632,1783,774,426,1]
+DSCF1105.JPG:[1090,1016,1312,755,2]
+DSCF1106.JPG:[1202,1076,1155,624,2]
+DSCF1109.JPG:[1053,878,1178,650,3]
+DSCF1110.JPG:[686,743,1159,654,3],[1920,792,1174,643,1]
+DSCF1111.JPG:[1890,867,1024,576,6],[671,874,1043,583,2]
+DSCF1114.JPG:[1535,1177,994,508,6]
+DSCF1116.JPG:[1539,1458,998,523,5]
+DSCF1120.JPG:[1236,1274,1324,706,3]
+DSCF1122.JPG:[1479,949,1009,542,5]
+DSCF1125.JPG:[978,930,654,415,1],[1198,1394,815,482,6],[2406,1252,792,460,2]
+DSCF1129.JPG:[1408,1772,792,501,6]
+DSCF1130.JPG:[1516,1708,755,471,1]
+DSCF1132.JPG:[2751,1005,489,250,1],[2283,1278,893,523,4],[174,1663,1406,740,5]
+DSCF1133.JPG:[1352,1192,1140,841,1]
+DSCF1135.JPG:[1801,1233,605,359,6]
+DSCF1137.JPG:[1879,1136,849,478,2]
+DSCF1140.JPG:[1722,594,789,284,6]
+DSCF1141.JPG:[1793,665,781,276,2]
+DSCF1142.JPG:[914,1200,886,388,2],[2010,1211,878,388,1]

busProjectModifiedTest.py

@@ -0,0 +1,194 @@
+import os
+import sys
+import matplotlib
+import matplotlib.pyplot as plt
+import matplotlib.patches as patches
+import numpy as np
+import ast
+
+import imageio
+
+
+#fontdict = {'fontsize':15, 'weight':'bold'}
+#plt.switch_backend('Qt5Agg')
+
+class IMAGE:
+
+    def __init__(self):
+        self.ROIS = []
+
+    def set_image(self, im):
+        self.I = imageio.imread(im)
+        self.imShape = self.I.shape
+
+    def clear_ROIS(self):
+        self.ROIS = []
+
+    def add_ROI(self, pos):
+        self.ROIS.append(pos)
+
+    def show_ROI(self, title, edgecolor, numGT, text, saveDir = None):
+        fig, ax = plt.subplots(1)
+        ax.imshow(self.I)
+        if(not isinstance(edgecolor,list) and len(self.ROIS) > 0):
+            edgecolor = [edgecolor] * len(self.ROIS)
+        for i in range(0,numGT):
+            ROI = self.ROIS[i]
+            rect = patches.Rectangle((ROI[0], ROI[1]), ROI[2], ROI[3], linewidth = 1, edgecolor = edgecolor[i], facecolor='none')
+            ax.add_patch(rect)
+        for i in range(numGT,len(self.ROIS)):
+            ROI = self.ROIS[i]
+            rect = patches.Rectangle((ROI[0], ROI[1]), ROI[2], ROI[3], linewidth = 1, edgecolor = edgecolor[i], facecolor='none', linestyle = '--')
+            ax.add_patch(rect)
+        if(saveDir is None):
+            ax.text(15,160,text, fontdict = fontdict, bbox={'facecolor':'yellow', 'edgecolor':'yellow','alpha':0.5, 'pad':2})
+        else:
+            ax.text(15, 300, text, fontdict=fontdict,bbox={'facecolor': 'yellow', 'edgecolor': 'yellow', 'alpha': 0.5, 'pad': 2})
+        plt.title(title)
+        if(not saveDir is None):
+            plt.savefig(os.path.join(saveDir, title), dpi = 500)
+        plt.close()
+
+    def close(self):
+        plt.close()
+
+def IOU(boxAList, boxBList):
+    Th = 0.69
+    iou = []
+    matches = {}
+    tp = 0
+    fp = len(boxBList)
+    missed = len(boxAList)
+    for i in range(len(boxAList)):
+        boxA = boxAList[i][:4]
+        iou_ = []
+        for j in range(len(boxBList)):
+            boxB = boxBList[j][:4]
+            if(not ((boxB[0] <= boxA[0] <= boxB[0] + boxB[2]) or (boxA[0] <= boxB[0] <= boxA[0] + boxA[2]))):
+                iou_.append(0.0)
+                continue
+            xA = max(boxA[0], boxB[0])
+            yA = max(boxA[1], boxB[1])
+            xB = min(boxA[0] + boxA[2], boxB[0] + boxB[2])
+            yB = min(boxA[1] + boxA[3], boxB[1] + boxB[3])
+            interArea = (xB - xA + 1) * (yB - yA + 1)
+            boxAArea = (boxA[2] + 1)*(boxA[3] + 1)
+            boxBArea = (boxB[2] + 1)*(boxB[3] + 1)
+            iou_.append(interArea / float(boxAArea + boxBArea - interArea))
+        maxIou = max(iou_)
+        maxIouIndex = iou_.index(max(iou_))
+        iou.append(maxIou)
+        if (maxIouIndex in matches and maxIou > iou[matches[maxIouIndex]]):
+            if (iou[matches[maxIouIndex]] > Th and boxAList[matches[maxIouIndex]][4] == boxBList[maxIouIndex][4]):
+                pass
+            elif(maxIou > Th and boxAList[i][4] == boxBList[maxIouIndex][4]):
+                tp += 1
+                missed -= 1
+                fp -= 1
+            matches[maxIouIndex] = i
+        if(not maxIouIndex in matches):
+            matches[maxIouIndex] = i
+            if(maxIou > Th and boxAList[i][4] == boxBList[maxIouIndex][4]):
+                tp += 1
+                missed -= 1
+                fp -= 1
+    return tp, fp, missed, iou
+
+def runTest(annFileNameGT, myAnnFileName, busDir , saveDir = None, elapsed = None, toPlt = False):
+
+    image = IMAGE()
+    objectsColors = {'g':'1', 'y':'2', 'w':'3', 's':'4', 'b':'5', 'r':'6'}
+    objectsColorsInv = {v: k for k, v in objectsColors.items()}
+    objectsColorsForShow = {'g':'g', 'y':'y', 'w':'w', 's':'tab:gray', 'b':'b', 'r':'r'}
+
+    writtenAnnsLines = {}
+    annFileEstimations = open(myAnnFileName, 'r')
+    annFileGT = open(annFileNameGT, 'r')
+    writtenAnnsLines['Ground_Truth'] = (annFileGT.readlines())
+    writtenAnnsLines['Estimation'] = (annFileEstimations.readlines())
+
+    TP = 0
+    FP = 0
+    MISS = 0
+
+    for i in range(len(writtenAnnsLines['Ground_Truth'])):
+
+        lineGT = writtenAnnsLines['Ground_Truth'][i].replace(' ','')
+        colors = []
+        imName = lineGT.split(':')[0]
+        lineE = [x for x in writtenAnnsLines['Estimation'] if imName == x.split(':')[0]]
+        if(len(lineE) == 0):
+            lineE = imName + ':'
+        else:
+            lineE = lineE[0]
+        bus = os.path.join(busDir, imName)
+        image.set_image(bus)
+        image.clear_ROIS()
+        annsGT = lineGT[lineGT.index(':') + 1:].replace('\n', '')
+        annsE = lineE[lineE.index(':') + 1:].replace('\n', '')
+        annsGT = ast.literal_eval(annsGT)
+        if (not isinstance(annsGT, tuple)):
+            annsGT = [annsGT]
+        for ann in annsGT:
+            image.add_ROI(ann[:4])
+            colorTag = objectsColorsInv[str(ann[4])]
+            colors.append(objectsColorsForShow[colorTag])
+        numGT = len(annsGT)
+        if('[' in lineE):
+            annsE = ast.literal_eval(annsE)
+            if (not isinstance(annsE, tuple)):
+                annsE = [annsE]
+            for ann in annsE:
+                image.add_ROI(ann[:4])
+                colorTag = objectsColorsInv[str(ann[4])]
+                colors.append(objectsColorsForShow[colorTag])
+            tp, fp, missed, iou = IOU(annsGT, annsE)
+        else:
+            tp = 0
+            fp = 0
+            numGT = 0
+            missed = len(annsGT)
+            iou = []
+        TP += tp
+        FP += fp
+        MISS += missed
+        print('Image : {}, TP: {} FP: {} MISSED : {}'.format(imName, tp, fp, missed))
+        iouStr = ','.join(['{0:.2f}'.format(x) for x in iou])
+        text = 'IOU Scores : ' + iouStr + '\nTP = {}, FP = {}, Missed = {} '.format(tp, fp, missed)
+        if toPlt:
+          image.show_ROI(edgecolor = colors, title = imName, numGT = numGT , text = text, saveDir = saveDir)
+
+    if(TP == 0):
+        F1Score = 0
+    else:
+        precision = TP/(TP + FP)
+        recall = TP/(TP + MISS)
+        F1Score = 2*(precision * recall)/(precision + recall)
+    strToWrite = 'Total detections = {}/{}\nTotal False Positives = {}\nTotal missed = {}'.format(TP, TP+MISS, FP, MISS)
+    strToWrite += '\nF1 SCORE : {0:.3f}'.format(F1Score)
+    if(not elapsed is None):
+        strToWrite += '\nTime elapsed : {0:.2f} seconds'.format(elapsed)
+    print(strToWrite)
+    if toPlt:
+      fig, ax = plt.subplots(1)
+      plt.title('Results', fontdict = {'fontsize':20})
+      im = np.zeros((10,26,3), dtype=np.uint8)
+      im[:,:,2] = 221
+      im[:,:,1] = 114
+      im[:,:,0] = 102
+      ax.imshow(im)
+      ax.text(4,7, strToWrite, style='italic', fontdict = {'fontsize':50, 'weight':'bold'})
+      mng = plt.get_current_fig_manager()
+      mng.window.showMaximized()
+      plt.show()
+      plt.close()
+      FIG, ax = plt.subplots(1)
+      plt.title('Results', fontdict = {'fontsize':20})
+      ax.imshow(im)
+      ax.text(2,8, strToWrite, style='italic', fontdict = {'fontsize':20, 'weight':'bold'})
+      if(saveDir is None):
+          saveDir = os.path.join(os.getcwd(), 'Output')
+          if(not os.path.exists(saveDir)):
+              os.mkdir(saveDir)
+      plt.savefig(os.path.join(saveDir,'Results.png'), dpi = 600)
+    return F1Score, precision, recall, TP, FP, MISS