def main(xform,test_set,dictionary):
object_dict = open(dictionary,'rb')
objects = pkl.load(object_dict)
object_dict.close()
print len(objects)
for key in objects.iterkeys():
crops = objects[key]
image = os.path.join(test_set,key)
windowing.sanity_check(xform,image,25,24,crops,debug=False)
print key
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_C1.txt'),'wb')
save_res.write('Average Number of annotations per image: '+str(runningTotal)+'\n')
for t in test_set:
save_res.write(t)
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)
for items in truthDict:
print items
# get all ground truths for the test set
for test in test_set:
ground_truth = resultcheck.getGroundTruths(test,test_dir,anno_dir,obj_type)
print test,len(ground_truth)
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,24,True)
test_res.append(results[0])
print len(test_set)
test_res = flattenResults(test_res)
calcPrecisionRecall(test_res,truthDict,test_dir,savename,save_res)
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
