def __annotate(self, frame, response):
AnnotationParserInstance = AnnotationParser()
#TODO: Make the choice of the service configurable
listOfRectanglesToDisplay = AnnotationParserInstance.getCV2RectanglesFromProcessingService1(response)
for rectangle in listOfRectanglesToDisplay:
cv2.rectangle(frame, (rectangle(0), rectangle(1)), (rectangle(2), rectangle(3)), (0,0,255),4)
return
def main(test_dir,anno_dir,c1,obj_type,step_size,savename):
test_set,runningTotal = testdb_loader(test_dir,anno_dir,obj_type)
save_res = open(os.path.join('results',savename+'_detection_experiment_C2.txt'),'wb')
save_res.write('Average Number of annotations per image: '+str(runningTotal)+'\n')
print "wrote"
print runningTotal
test_res = list()
true = 0
false = 0
total_truth = 0
precision = list()
recall = list()
all_objects = list()
truthDict = resultcheck.getTruthDictionary(test_set,test_dir,anno_dir,obj_type)
# get all ground truths for the test set
for test in test_set:
ground_truth = resultcheck.getGroundTruths(test,test_dir,anno_dir,obj_type)
all_objects.append((ground_truth,test))
# process all the images and save the tuples in form (bbox,dval,img)
results = windowing.main(c1,os.path.join(test_dir,test),step_size,9,True)
test_res.append(results[0])
print test_res
test_res = flattenResults(test_res)
calcPrecisionRecall(test_res,truthDict,test_dir,savename,save_res)
def testdb_loader(test_dir,anno_dir,obj_type):
anno_contents = os.listdir(anno_dir)
image_contents = os.listdir(test_dir)
# remove the ending tag value so we can easily look up the name of the file
image_names = [os.path.splitext(x)[0] for x in image_contents]
test_set = list()
i = 0
j = 0
runningTotal = 0
setSize = 100
while i < setSize:
num = len(resultcheck.getGroundTruths(image_contents[j],test_dir,anno_dir,obj_type)) # old call
#num = len(checker.getGroundTruths(anno_contents[j],test_dir,anno_dir,obj_type.strip())) #new call
runningTotal = runningTotal + num
print "Number of objects",num,i
if num > 0:
print "adding"
#annoTmp = os.path.splitext(anno_contents[j])[0]
#print annoTmp,image_contents[image_names.index(annoTmp)]
#test_set.append(image_contents[image_names.index(annoTmp)])
test_set.append(image_contents[j])
i += 1
j += 1
print runningTotal,setSize,i
return test_set,float(runningTotal)/setSize
def calcPrecisionRecall(flatList,truthDict,test_dir,savename,save_res):
sortedDetections = sorted(flatList,key=lambda plot: plot[1],reverse=True)
# get the number of objects in the set
total = 0
for x in truthDict:
total += len(truthDict[x])
print "Total number of objects", total
td = 0
fd = 0
precision = list()
recall = list()
dvals = list()
for elm in sortedDetections:
print elm
image = elm[2]
dval = elm[1]
detection = elm[0]
print detection
groundTruth = truthDict[image]
# check the detection of this element
img = Image.open(os.path.join(test_dir,image))
res = resultcheck.test_detection(groundTruth,detection,img,image,dval)
td += res[0]
fd += res[1]
img = res[2]
p = float(td)/float(td+fd)
r = float(td)/float(total)
precision.append(p)
recall.append(r)
dvals.append(dval)
save_res.write(str(p) +" "+str(r)+' '+str(dval)+"\n")
pl.plot(recall,precision)
pl.title("Precision Recall Graph C2 Cars")
pl.xlabel("Recall")
pl.ylabel("Precision")
pl.xlim([0.0,1.0])
pl.ylim([0.0,1.0])
pl.savefig(savename+"_C2.png")
print precision
print recall
def testdb_loader(test_dir,anno_dir,obj_type):
contents = os.listdir(test_dir)
test_set = list()
test_img = list()
i = 0
j = 0
while i < 15:
num = len(resultcheck.getGroundTruths(contents[j],test_dir,anno_dir,obj_type))
print "Number of objects",num
if num > 0:
print "ADD",num
test_set.append(contents[j])
test_img.append(contents[j])
i += 1
j += 1
return test_set,test_img
def testdb_loader(test_dir,anno_dir,obj_type):
contents = os.listdir(test_dir)
test_set = list()
i = 0
j = 0
runningTotal = 0
setSize = 100
while i < setSize:
num = len(resultcheck.getGroundTruths(contents[j],test_dir,anno_dir,obj_type))
#num = checker.getGroundTruths(contents[i],test_dir,anno_dir,obj_type)
runningTotal = runningTotal + num
print "Number of objects",num
if num > 0:
print "adding"
test_set.append(contents[j])
i += 1
j += 1
print runningTotal,setSize,i
return test_set,float(runningTotal)/setSize
def main(test_dir,anno_dir,c1,obj_type,step_size,savename):
save_res = open(os.path.join('results',savename+'_OldSetConfusion.txt'),'wb')
testSet,testImages = testdb_loader(test_dir,anno_dir,obj_type)
test_res = list()
all_det = list()
all_objects = list()
# get all ground truths for the test set
for test in testSet:
ground_truth = resultcheck.getGroundTruths(test,test_dir,anno_dir,obj_type)
all_objects.append((ground_truth,test))
# process all the images and save the tuples in form (bbox,dval,img)
results,all_test_results= windowing.main(c1,os.path.join(test_dir,test),step_size,24,True)
test_res.append(results)
all_det.append(all_test_results)
print "saving off dval data"
dvals_copy_file = open(os.path.join('pickledCrops','allDetectionsConfusion.dat'),'wb')
pickle.dump(all_det,dvals_copy_file)
dvals_copy_file.close()
count = 0
img_res = list()
print "number of boxes found", count
for a in range(0,len(all_det)):
imgdet = list()
for det in all_det[a]:
detection_box = det
print "ahhhh", all_objects[a][0]
imgdet.append(resultcheck.c_matrix(all_objects[a][0],detection_box))
#for labels in all_objects:
# pass
#print "Labels", labels
img_res.append(imgdet)
tp=0
fp=0
tn=0
fn=0
print "Crops?", len(img_res) , len(all_det)
for i in range(0,len(img_res)):
img = Image.open(os.path.join(test_dir,testImages[i]))
#draw = ImageDraw.Draw(img)
for imgdata in img_res[i]:
#tp instance
imgdata = imgdata[0]
if imgdata[1] > 0 and imgdata[2] == 1:
#draw.rectangle((imgdata[0][0],imgdata[0][1],imgdata[0][2],imgdata[0][3]),outline='green')
img = draw_bbox(imgdata[0],img,'green')
print imgdata
tp += 1
elif imgdata[1] > 0 and imgdata[2] == 0:
fp += 1
elif imgdata[1] <= 0 and imgdata[2] == 1:
#draw.rectangle((imgdata[0][0],imgdata[0][1],imgdata[0][2],imgdata[0][3]),outline='red')
img = draw_bbox(imgdata[0],img,'red')
fn += 1
elif imgdata[1] <= 0 and imgdata[2] == 0:
tn += 1
#print i
#draw.rectangle((0,0,10,10),outline='red')
print "ALL", all_objects[i]
#print i
#for ground in all_objects[i]:
#print ground
for gtruth in all_objects[i][0]:
print gtruth
ground_tuple = gtruth
print "GROUND", ground_tuple
#img = draw_bbox(ground_tuple,img,'blue')
#img.save(os.path.join('res_img',all_objects[i][1]))
print "Confusion Matrix"
save_res.write(str(tp)+" "+str(fn)+"\n"+str(fp)+" "+str(tn)+"\n")
print tp,fn
print fp,tn
def loadTestSet(test_dir,anno_dir,obj_type): contents = os.listdir(test_dir) test_set = list() an.getTruthLocations(test_set,test_dir,anno_dir)
