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How to use drawBbox method in fMBT

Best Python code snippet using fMBT_python

animate_network_detections.py

Source:animate_network_detections.py

1# -*- coding: utf-8 -*-2import matplotlib.pyplot as plt3import matplotlib.animation as animation4import numpy as np5import pandas as pd6def animateDetections(df,frameNames,xlim=(0,960), ylim=(0,540)):7    colors = ['b','g','r','k']#plt.cm.inferno(np.linspace(0.2,0.9,4))8    fig = plt.figure(figsize=(5,3))9#    plt.tight_layout(pad=0)10    11    12    ax = fig.add_subplot(111, aspect='equal', autoscale_on=False, \13                         xlim=xlim,ylim=ylim)14    fig.set_tight_layout('tight')15#    gttp = drawBBox((0,0,0,0),'none')16#    17#    dettp = drawBBox((0,0,0,0),'none')18#    19#    fp = drawBBox((0,0,0,0),'none')20#    21#    fn = drawBBox((0,0,0,0),'none')22    def drawBBox(bbox,boxColor,label):23        x1,y1,x2,y2 = bbox24        ymin,ymax = ylim25        rect = plt.Rectangle((x1,ymax - y2), x2-x1, y2-y1,\26                             ec=boxColor,lw=1,fc='none', label = label)27        ax.add_patch(rect)28        29        return rect30    31    32    def removePatches():33        for p in reversed(ax.patches):34            p.remove()35    36    37    def init():38        gttp = drawBBox((0,0,0,0),colors[0],'GT')39        dettp = drawBBox((0,0,0,0),colors[2],'TP')40        fp = drawBBox((0,0,0,0),colors[3],'FP')41        fn = drawBBox((0,0,0,0),colors[1],'FN')42        plt.legend(fontsize=10,ncol=4,loc='upper center', bbox_to_anchor=(0.5,1.15))43        44        return gttp,dettp,fp,fn45    46    def convertToBBox(data):47        if data is not np.nan:48            data = eval(data)49        else:50            data = None51        return data52    53    def animationStep(imageName):54        print(imageName)55        frameDF = df.loc[df['imageName'] == imageName, :]56        57        removePatches()58        59        gttp = drawBBox((0,0,0,0),'none','GT')60        dettp = drawBBox((0,0,0,0),'none','TP')61        fp = drawBBox((0,0,0,0),'none','FP')62        fn = drawBBox((0,0,0,0),'none','FN')63        64        for i in frameDF.index:65            gtBBox =  convertToBBox(frameDF.loc[i,'GTBBox'])66            detBBox = convertToBBox(frameDF.loc[i,'DetBBox'])67            if int(frameDF.loc[i,'TP']) == 1:68                # true positive69                gttp = drawBBox(gtBBox,colors[0], 'GT')70                dettp = drawBBox(detBBox,colors[2], 'TP')71            elif int(frameDF.loc[i,'FP']) == 1:72                # false positive73                fp = drawBBox(detBBox,colors[3], 'FP')74            elif int(frameDF.loc[i,'FN']) == 1:75                # false negative76                fn = drawBBox(gtBBox,colors[1], 'FN')77            else:78                print('should not happen')79        80        return gttp,dettp,fp,fn81    ani = animation.FuncAnimation(fig,animationStep,frames=frameNames,blit=True,init_func=init)82    plt.close()83    return ani84def loadCSV(fileName):85    df = pd.read_csv(fileName,index_col=0)86    frameNames = sorted(list(set(df['imageName'])))87    return df,frameNames88if __name__ == '__main__':89    network = ['GOPR9027-mobilenet-124',\90               'GOPR9027-mobilenet-220',\91               'GOPR9027-yolov3-tiny-128',\92               'GOPR9027-yolov3-tiny-224']93    networksLogDF = pd.DataFrame(columns=['TP','FP','FN'])94    resultPath = '../videos/litter-recording/' 95    for n in network:96        fileName = resultPath + n +'/1r1c/analysis/logInfoDF.csv'97        df,frameNames = loadCSV(fileName)98        99        networksLogDF.loc[n.replace('GOPR9027-',''),:] = list(df.loc[:,'TP':'FN'].sum())100        101        102#        ani = animateDetections(df,frameNames)103#        ani.save(fileName.replace('logInfoDF.csv',n + '.mp4'), fps=50, dpi = 300)104#        plt.close()...

imitate.py

Source:imitate.py

1import pandas as pd2from PIL import Image3import torch4from torchvision import transforms5from utils.data_utils import txt_to_bboxinfo, DrawBBox6from utils.bbox_utils import bbox_format_transform, bbox_normalization, convert_yolov1_label, reshape_bbox,convert_yolov1label_to_bbox7from utils.train_utils import getconfigparser89parser = getconfigparser('./config/config.cfg')10CLASSES = parser.get("dataset-config", 'classes').split(',')11df = pd.read_csv('./object.csv')1213resize = (448, 448)  # å°ºåº¦åæ¢åçå¤§å°14item = 12  # æ ·æ¬åºå·15transformer = transforms.Compose([16    transforms.Resize(resize),17    transforms.ToTensor(),18])1920# å¾ççå¤ç(reshape+totensor)21img_file = Image.open(df['path'][item])  # å¾çPILå¯¹è±¡22h, w = img_file.height, img_file.width23x_train = transformer(img_file)24# bboxå¤ç(reshape,format_trans,normalization)25bboxes_init = txt_to_bboxinfo(df['object_txt_path'][item])  # ä½ç½®ä¿¡æ¯ :format(xa,ya,xb,yb)26scale = (resize[0] / h, resize[1] / w)27bboxes_reshape = [reshape_bbox(bbox, scale) for bbox in bboxes_init]28# DrawBBox(img_file.resize(resize),bboxes_reshape,mode=1)  #  ç¨drawbboxå½æ°æ£éªä¸ä¸reshapeçbboxåå¾çï¼åç°æ²¡æé®é¢29bboxes_trans = [bbox_format_transform(bbox, mode=1) for bbox in bboxes_reshape]30# DrawBBox(img_file.resize(resize),grid_num=(7,7),bboxes=bboxes_trans, mode=2,fig_save_path='./test.jpg')  #  ç¨drawbboxå½æ°æ£éªä¸ä¸æ ¼å¼è½¬æ¢çbboxåå¾çï¼åç°æ²¡æé®é¢31bboxes_normal = [bbox_normalization(bbox, resize) for bbox in bboxes_trans]  # å½ä¸åæ¯ä¸ºäºåå°è®¡ç®é¾åº¦å¹¶æ¹ä¾¿æä»¬åç»å°å¶è½¬ä¸º(px,py,dw,dh)çæ ¼å¼32# # ç®åçbboxæ¯(dx,dy,dw,dh)ââdh,dwæçæ¯bboxçé¿å®½ç¸å¯¹äºå¾ççç¸å¯¹é¿åº¦,dx,dyåç33for i in range(len(bboxes_normal)):34    bboxes_normal[i][0] = CLASSES.index(bboxes_normal[i][0])  # æ ç¾æ°å¼å3536y_train = convert_yolov1_label(bboxes_normal)  # (dx,dy,dw,dh) -> (px,py,dw,dh)    # ç¼ºç¹æå¨ï¼å¤ä¸ªç©ä½ä¸å¿ç¹è½å¨åä¸ä¸ªç½æ ¼åªè½æ£æµä¸ä¸ªé®é¢37y_train = torch.Tensor(y_train)383940y_train = y_train.permute((2, 0, 1))4142# å°é¢æµå¼è¿è¡è½¬æ¢-å¼å§è§£ç 4344y_train = y_train.permute((1, 2, 0))4546bboxes_p = convert_yolov1label_to_bbox(y_train) # (px,py,dw,dh) -> (dx,dy,dw,dh)474849bboxes_p[:,0]=bboxes_p[:,0]*resize[1]50bboxes_p[:,1]=bboxes_p[:,1]*resize[0]51bboxes_p[:,2]=bboxes_p[:,2]*resize[1]52bboxes_p[:,3]=bboxes_p[:,3]*resize[0]5354bboxes_p_out=[]55for bbox in bboxes_p:56    if bbox[4] == 1:57        classlist=list(bbox[5:].numpy())58        idlist=[]59        for i in range(len(classlist)):60            if classlist[i]==1:61                idlist.append(i)62        bboxes_p_out.append(idlist[:1]+list(bbox[0:4].numpy()))
...

FrameAnalyzer.py

Source:FrameAnalyzer.py

...7    def __init__(self):8        self.preProcessor = PreProcessor()9        self.mainProcessor = MainProcessor()10        self.postProcessor = PostProcessor()11        self.drawbBox = drawBbox()12        13    def exec(self, frame):14        batch = self.preProcessor.exec(frame)15        # preProcessor return img16        pred = self.mainProcessor.exec(batch)17        # mainProcessor return batch18        # core mainProcessor has no-info19        bbox = self.postProcessor.exec(pred)20        # postProcessor return frame_box21        print(frame)22        drawBbox.exec(frame, bbox)23        #draw bbox with (frame, bbox)24        return bbox25

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