def evaluate(self, image_file=None, image=None):
show = False
if image is None:
image = skimage.img_as_float(imread(image_file))
show = True
image, classes, offsets = self.detect_objects(image)
class_names, rects, _, _ = show_boxes(args,
image,
classes,
offsets,
self.feature_shapes,
show=show)
return class_names, rects
def evaluate(self, image_file=None, image=None):
show = False
if image is None:
image = skimage.img_as_float(imread(image_file))
show = True
image = np.expand_dims(image, axis=0)
classes, offsets = self.ssd.predict(image)
# print("Classes shape: ", classes.shape)
# print("Offsets shape: ", offsets.shape)
image = np.squeeze(image, axis=0)
# classes = np.argmax(classes[0], axis=1)
classes = np.squeeze(classes)
# classes = np.argmax(classes, axis=1)
offsets = np.squeeze(offsets)
class_names, rects, _, _ = show_boxes(image,
classes,
offsets,
self.feature_shapes,
show=show,
normalize=self.normalize)
return class_names, rects
def evaluate_test(self):
# test labels csv path
path = os.path.join(self.args.data_path,
self.args.test_labels)
# test dictionary
dictionary, _ = build_label_dictionary(path)
keys = np.array(list(dictionary.keys()))
# sum of precision
s_precision = 0
# sum of recall
s_recall = 0
# sum of IoUs
s_iou = 0
# evaluate per image
for key in keys:
# grounnd truth labels
labels = np.array(dictionary[key])
# 4 boxes coords are 1st four items of labels
gt_boxes = labels[:, 0:-1]
# last one is class
gt_class_ids = labels[:, -1]
# load image id by key
image_file = os.path.join(self.args.data_path, key)
image = skimage.img_as_float(imread(image_file))
image, classes, offsets = self.detect_objects(image)
# perform nms
_, _, class_ids, boxes = show_boxes(args,
image,
classes,
offsets,
self.feature_shapes,
show=False)
boxes = np.reshape(np.array(boxes), (-1,4))
# compute IoUs
iou = layer_utils.iou(gt_boxes, boxes)
# skip empty IoUs
if iou.size ==0:
continue
# the class of predicted box w/ max iou
maxiou_class = np.argmax(iou, axis=1)
# true positive
tp = 0
# false positiove
fp = 0
# sum of objects iou per image
s_image_iou = []
for n in range(iou.shape[0]):
# ground truth bbox has a label
if iou[n, maxiou_class[n]] > 0:
s_image_iou.append(iou[n, maxiou_class[n]])
# true positive has the same class and gt
if gt_class_ids[n] == class_ids[maxiou_class[n]]:
tp += 1
else:
fp += 1
# objects that we missed (false negative)
fn = abs(len(gt_class_ids) - tp)
s_iou += (np.sum(s_image_iou) / iou.shape[0])
s_precision += (tp/(tp + fp))
s_recall += (tp/(tp + fn))
n_test = len(keys)
print_log("mIoU: %f" % (s_iou/n_test),
self.args.verbose)
print_log("Precision: %f" % (s_precision/n_test),
self.args.verbose)
print_log("Recall : %f" % (s_recall/n_test),
self.args.verbose)
def evaluate_test(self):
csv_path = os.path.join(config.params['data_path'],
config.params['test_labels'])
print("CSV", csv_path)
dictionary, _ = build_label_dictionary(csv_path)
keys = np.array(list(dictionary.keys()))
n_iou = 0
s_iou = 0
i = 0
tp = 0
fp = 0
for key in keys:
labels = dictionary[key]
labels = np.array(labels)
# 4 boxes coords are 1st four items of labels
gt_boxes = labels[:, 0:-1]
gt_class_ids = labels[:, -1]
image_file = os.path.join(config.params['data_path'], key)
print("Image: ", image_file)
image = skimage.img_as_float(imread(image_file))
image = np.expand_dims(image, axis=0)
classes, offsets = self.ssd.predict(image)
image = np.squeeze(image, axis=0)
classes = np.squeeze(classes)
offsets = np.squeeze(offsets)
_, _, class_ids, boxes = show_boxes(image,
classes,
offsets,
self.feature_shapes,
show=False,
normalize=self.normalize)
boxes = np.reshape(np.array(boxes), (-1, 4))
iou = layer_utils.iou(gt_boxes, boxes)
if iou.size == 0:
continue
print("--------------")
print("gt:", gt_class_ids, gt_boxes)
print("iou w/ gt:", iou)
print("iou shape:", iou.shape)
maxiou_class = np.argmax(iou, axis=1)
print("classes: ", maxiou_class)
n = iou.shape[0]
n_iou += n
s = []
for j in range(n):
s.append(iou[j, maxiou_class[j]])
if gt_class_ids[j] == class_ids[maxiou_class[j]]:
tp += 1
else:
fp += 1
fp += abs(len(class_ids) - len(gt_class_ids))
print("max ious: ", s)
s = np.sum(s)
s_iou += s
print("pred:", class_ids, boxes)
print("--------------")
# i += 1
#if i==10:
# break
print("sum:", s_iou)
print("num:", n_iou)
print("mIoU:", s_iou / n_iou)
print("tp:", tp)
print("fp:", fp)
print("precision:", tp / (tp + fp))