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

Automation Testing Tutorials

Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.