def run(self, image, threshold=0.7, do_nms=True, show_result=True, nms_threshold=0.3):
"""Public function to run the DigitSpotter.
Parameters
----------
image : str
filename of the test image
Returns
----------
bbs : ndarray, shape of (N, 4)
detected bounding box. (y1, y2, x1, x2) ordered.
probs : ndarray, shape of (N,)
evaluated score for the DigitSpotter and test images on average precision.
Examples
--------
"""
# 1. Get candidate patches
candidate_regions = self._region_proposer.detect(image)
patches = candidate_regions.get_patches(dst_size=self._cls.input_shape)
# 3. Run pre-trained classifier
probs = self._cls.predict_proba(patches)[:, 1]
# 4. Thresholding
bbs, patches, probs = self._get_thresholded_boxes(candidate_regions.get_boxes(), patches, probs, threshold)
# 5. non-maxima-suppression
if do_nms and len(bbs) != 0:
bbs, patches, probs = NonMaxSuppressor().run(bbs, patches, probs, nms_threshold)
if len(patches) > 0:
probs_ = self._recognizer.predict_proba(patches)
y_pred = probs_.argmax(axis=1)
if show_result:
for i, bb in enumerate(bbs):
# todo : show module 정리
image = show.draw_box(image, bb, 2)
y1, y2, x1, x2 = bb
msg = "{0}".format(y_pred[i])
cv2.putText(image, msg, (x1, y1), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,255), thickness=2)
cv2.imshow("MSER + CNN", image)
cv2.waitKey(0)
return bbs, probs
def run(self,
image,
threshold=0.7,
do_nms=True,
show_result=True,
nms_threshold=0.3):
"""Public function to run the DigitSpotter.
Parameters
----------
image : str
filename of the test image
Returns
----------
bbs : ndarray, shape of (N, 4)
detected bounding box. (y1, y2, x1, x2) ordered.
probs : ndarray, shape of (N,)
evaluated score for the DigitSpotter and test images on average precision.
Examples
--------
"""
# 1. Get candidate patches
candidate_regions = self._region_proposer.detect(image)
patches = candidate_regions.get_patches(dst_size=self._cls.input_shape)
# 3. Run pre-trained classifier
probs = self._cls.predict_proba(patches)[:, 1]
# 4. Thresholding
bbs, patches, probs = self._get_thresholded_boxes(
candidate_regions.get_boxes(), patches, probs, threshold)
# 5. non-maxima-suppression
if do_nms and len(bbs) != 0:
bbs, patches, probs = NonMaxSuppressor().run(
bbs, patches, probs, nms_threshold)
if len(patches) > 0:
probs_ = self._recognizer.predict_proba(patches)
y_pred = probs_.argmax(axis=1)
max_area = 0
area_list = []
for i, bb in enumerate(bbs):
y1, y2, x1, x2 = bb
bb_area = (y2 - y1) * (x2 - x1)
area_list.append(bb_area)
if bb_area > max_area:
max_area = bb_area
index = [
a for a in range(len(area_list))
if area_list[a] >= max_area * 0.97
]
if len(index) == 1:
return y_pred[index[0]]
elif len(index) == 2:
if bbs[index[0]][2] > bbs[index[1]][2]:
return y_pred[index[1]] * 10 + y_pred[index[0]]
else:
return y_pred[index[0]] * 10 + y_pred[index[1]]
else:
return y_pred
if show_result:
for i, bb in enumerate(bbs):
#if probs[i] > 0.99:
# todo : show module 정리
image = show.draw_box(image, bb, 2)
y1, y2, x1, x2 = bb
msg = "{0}\n{1}\n{2}".format(y_pred[i], bb, probs[i])
cv2.putText(image,
msg, ((x1 + x2) // 2, (y1 + y2) // 2),
cv2.FONT_HERSHEY_SIMPLEX,
0.5, (0, 0, 255),
thickness=1)
cv2.imshow("MSER + CNN", image)
cv2.waitKey(0)
cv2.destroyAllWindows()
return bbs
else:
i = 0
print i
if i == 0:
image = img
else:
image = cv2.flip(img, -1)
digit_dic = {}
bbs, probs, y_preds = results[i]
for bb, prob, y_pred in zip(bbs, probs, y_preds):
if prob < threshold:
continue
image = show.draw_box(image, bb, 2)
y1, y2, x1, x2 = bb
msg = "{0}".format(y_pred)
cv2.putText(image,
msg, (x1, y1),
cv2.FONT_HERSHEY_SIMPLEX,
1, (0, 0, 255),
thickness=2)
digit_dic[x1] = y_pred
sorted_list = sorted(digit_dic.items(), key=operator.itemgetter(0))
digit_list = [item[1] for item in sorted_list]
print 'Predict', os.path.basename(img_file), digit_list