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 = checker.getTruthDict(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 = checker.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 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:
print anno_contents[j], image_names[j]
# 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 main(test_dir, anno_dir, c1, obj_type, step_size, savename):
save_res = open(os.path.join("results", savename + "_NewSetConfusion.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 = checker.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)):
print "image? ", testImages
img = Image.open(os.path.join(test_dir, testSet[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
