def computeWordOvelap(imgc, word_gt, words, wordsOk, wordsFp):
best_match = 0
best_match2 = 0
for det_word in words:
try:
cv2.rectangle(imgc, (det_word[0], det_word[1]),
(det_word[2], det_word[3]), (0, 0, 255))
for gt_box in word_gt:
rect_int = utils.intersect(det_word, gt_box)
int_area = utils.area(rect_int)
union_area = utils.area(utils.union(det_word, gt_box))
ratio = int_area / float(union_area)
ratio2 = int_area / utils.area(gt_box)
if ratio > best_match:
best_match = ratio
w = det_word
best_match2 = ratio2
if best_match2 > 0.3:
wordsOk.append(det_word)
elif best_match == 0:
wordsFp.append(det_word)
except:
pass
return (best_match, best_match2)
def computeWordOvelap(imgc, word_gt, words, wordsOk, wordsFp):
best_match = 0
best_match2 = 0
for det_word in words:
try:
cv2.rectangle(imgc, (det_word[0], det_word[1]), (det_word[2], det_word[3]), (0, 0, 255))
for gt_box in word_gt:
rect_int = utils.intersect( det_word, gt_box )
int_area = utils.area(rect_int)
union_area = utils.area(utils.union(det_word, gt_box))
ratio = int_area / float(union_area)
ratio2 = int_area / utils.area(gt_box)
if ratio > best_match:
best_match = ratio
w = det_word
best_match2 = ratio2
if best_match2 > 0.3:
wordsOk.append(det_word)
elif best_match == 0:
wordsFp.append(det_word)
except:
pass
return (best_match, best_match2)
def define_location_quality(self):
"""
parameters:
self : we use self.point and check it against the dictionary of shape_corners
shapes = {'name1': APIshape, name2: Abstract/Section, name3: qq, ...}
"""
# if we were unable to calculate a point
if not self.point:
return 0
score = self.location_quality
for name, shape in self.reference_shapes.iteritems():
try:
shape = ensure_polygon(shape)
if shape.intersection(transform(4269, 3857, self.point)):
score += math.pow(area(shape, units='square miles'), -1) # if within a square mile => score = 1
# print '\tpoint inside the shape: %s, area: %.3f square miles' % (name, area(shape, units='square miles'))
else:
pass
# print '\tpoint outside the shape: %s' % name
except:
print '\tinvalid geomtery for %s' % name
# reset score if the point does not fall within any of the related shapes
if score == self.location_quality:
score = -1
self.location_quality = score
def associate_plate_to_vehicle(cars, plates):
"""
Associate each plate to the closest available vehicle.
Also check that the bounding box of the license plate is inside the
bounding box of the car it is assigned to.
Parameters
----------
cars : list
The list of vehicles without any license plate annotation.
plates : list
The list of license plates detected in a frame.
Returns
-------
list
The list of vehicles updated with license plates associated to the
correct vehicle.
"""
# Keep track of indexes of cars that have already been assigned
unavailable_cars_indexes = list()
for p in plates:
min_dist = np.inf
px, py = compute_center_coordinates(p['bounding_box'])
for ic, car in enumerate(cars):
# Skip vehicles that have already been assigned a plate
if ic in unavailable_cars_indexes:
continue
# Compute coordinates of the center of the vehicle's bb
cx, cy = compute_center_coordinates(car['bounding_box'])
dist = (cx - px)**2 + (cy - py)**2
if dist < min_dist:
min_dist = dist
min_dist_car_index = ic
# If a suitable car has been found AND there is an overlap between the
# bounding boux of the license plate and the one of the closest car, do
# proceed to assign that license plate to that car.
if (min_dist != np.inf and area(
p['bounding_box'], cars[min_dist_car_index]['bounding_box'])
is not None):
# Assign plate to the closest available car
cars[min_dist_car_index]['plates'] = [p]
# Mark car as unavailable
unavailable_cars_indexes.append(min_dist_car_index)
return cars
def define_location_quality(self):
"""
parameters:
self : we use self.point and check it against the dictionary of shape_corners
shapes = {'name1': APIshape, name2: Abstract/Section, name3: qq, ...}
"""
# if we were unable to calculate a point
if not self.point:
return 0
score = self.location_quality
for name, shape in self.reference_shapes.iteritems():
try:
shape = ensure_polygon(shape)
if shape.intersection(transform(4269, 3857, self.point)):
score += math.pow(
area(shape, units='square miles'),
-1) # if within a square mile => score = 1
# print '\tpoint inside the shape: %s, area: %.3f square miles' % (name, area(shape, units='square miles'))
else:
pass
# print '\tpoint outside the shape: %s' % name
except:
print '\tinvalid geomtery for %s' % name
# reset score if the point does not fall within any of the related shapes
if score == self.location_quality:
score = -1
self.location_quality = score
def computeSegmOverlap(gt_rects, segmentations, MIN_SEGM_OVRLAP=0.6):
segm2chars = 0
for k in range(len(gt_rects)):
gt_rect = gt_rects[k]
best_match = 0
best_match_line = 0
if (gt_rect[4] == ',' or gt_rect[4] == '.' or gt_rect[4] == '\''
or gt_rect[4] == ':'
or gt_rect[4] == '-') and not evalPunctuation:
continue
best_match2 = 0
for detId in range(segmentations.shape[0]):
rectn = segmentations[detId, :]
rect_int = utils.intersect(rectn, gt_rect)
int_area = utils.area(rect_int)
union_area = utils.area(utils.union(rectn, gt_rect))
ratio = int_area / float(union_area)
if ratio > best_match:
best_match = ratio
if ratio > best_match_line and rectn[7] == 1.0:
best_match_line = ratio
gt_rect[5] = best_match
if best_match < MIN_SEGM_OVRLAP:
if k < len(gt_rects) - 1:
gt_rect2 = gt_rects[k + 1]
chars2Rect = utils.union(gt_rect2, gt_rect)
rect_int = utils.intersect(rectn, chars2Rect)
int_area = utils.area(rect_int)
union_area = utils.area(utils.union(rectn, chars2Rect))
ratio = int_area / float(union_area)
if ratio > best_match2:
if ratio > MIN_SEGM_OVRLAP:
segm2chars += 1
best_match2 = ratio
gt_rect[5] = ratio
gt_rect2[5] = ratio
def main():
cam = cv2.VideoCapture(0)
# load data
embed_file_path = "data/embed.pkl"
face_database = loadData(embed_file_path)
# load models
device = "MYRIAD"
plugin = IEPlugin(device, plugin_dirs=None)
face_embed = FaceEmbedding(plugin)
face_detect = MobileFaceDetect(plugin)
# params
config = configparser.ConfigParser()
config.read("config.ini")
fm_threshold = float(config["FACE_MATCH"]['Threshold'])
label = "new"
while (True):
ret, frame = cam.read()
if not ret:
print("dead cam")
cam = cv2.VideoCapture(0)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
continue
face_bboxes = face_detect.inference(frame)
if len(face_bboxes) > 0:
areas = [area(box) for box in face_bboxes]
max_id = np.argmax(np.asarray(areas))
mfb = face_bboxes[max_id]
main_face = frame[mfb[1]:mfb[3], mfb[0]:mfb[2], :]
# TODO real face detection
# TODO face alignment
# face_feature = face_embed(main_face, fe_net, fe_input_blob)
# s = time.time()
face_feature = face_embed.inference(main_face)
# print(time.time() - s)
# TODO face record
best_match = bruteforce(face_feature, face_database, fm_threshold)
if best_match is None:
label = "new"
else:
label = str(best_match['id'])
# visualize for debug
cv2.rectangle(frame, (mfb[0], mfb[1]), (mfb[2], mfb[3]),
(255, 0, 0), 2)
cv2.putText(frame,
label, (mfb[0], mfb[1]),
cv2.FONT_HERSHEY_SIMPLEX,
0.6, (0, 0, 255),
lineType=cv2.LINE_AA)
cv2.imshow("gandalf", frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
def computeSegmOverlap(gt_rects, segmentations, MIN_SEGM_OVRLAP = 0.6):
segm2chars = 0
for k in range(len(gt_rects)):
gt_rect = gt_rects[k]
best_match = 0
best_match_line = 0
if (gt_rect[4] == ',' or gt_rect[4] == '.' or gt_rect[4] == '\'' or gt_rect[4] == ':' or gt_rect[4] == '-') and not evalPunctuation:
continue
best_match2 = 0
for detId in range(segmentations.shape[0]):
rectn = segmentations[detId, :]
rect_int = utils.intersect( rectn, gt_rect )
int_area = utils.area(rect_int)
union_area = utils.area(utils.union(rectn, gt_rect))
ratio = int_area / float(union_area)
if ratio > best_match:
best_match = ratio
if ratio > best_match_line and rectn[7] == 1.0 :
best_match_line = ratio
gt_rect[5] = best_match
if best_match < MIN_SEGM_OVRLAP:
if k < len(gt_rects) - 1:
gt_rect2 = gt_rects[k + 1]
chars2Rect = utils.union(gt_rect2, gt_rect)
rect_int = utils.intersect( rectn, chars2Rect )
int_area = utils.area(rect_int)
union_area = utils.area(utils.union(rectn, chars2Rect))
ratio = int_area / float(union_area)
if ratio > best_match2:
if ratio > MIN_SEGM_OVRLAP:
segm2chars += 1
best_match2 = ratio
gt_rect[5] = ratio
gt_rect2[5] = ratio
def fradius(theta, a, b, X, Y):
ph = [_tr(theta, b / a, X[i], Y[i]) for i in range(3)]
PX = [p[0] for p in ph]
PY = [p[1] for p in ph]
du = distsq([PX[0], PX[1]], [PY[0], PY[1]])
dv = distsq([PX[1], PX[2]], [PY[1], PY[2]])
dw = distsq([PX[0], PX[2]], [PY[0], PY[2]])
A = area(PX, PY)
return b**2 * 16 * A**2 - du * dv * dw
def is_included_in_nutrition_table(self, word):
"""Compute intersection area between a word area and nutrition table area and return a boolean to check if
percentage of the word's area included in nutritional table is greater than 50%
Keyword arguments:
word -- Word
"""
#Get word bounding_box vetices
word_vertices = word.bounding_box
#Find straight circumscribed rectangle
bounding_box = circumscribed_rectangle(word_vertices)
#Get inclusion score un nutrition table = area of intersection / area of word bounding box
inclusion_score = intersection_area(
bounding_box, self.table_bbx) / area(bounding_box)
return inclusion_score > 0.5
def remove_duplicate_plates(plates, annotations_history):
# TODO document
excluded_plates_indexes = list()
unique_plates_list = list()
for i in range(len(plates)):
# If it has been already excluded, skip
if i in excluded_plates_indexes:
continue
p1 = plates[i]
for j in range(i + 1, len(plates)):
p2 = plates[j]
# Handle collision
if area(p1['bounding_box'], p2['bounding_box']) is not None:
# If they're the same skip the other one
if p1['plate_text'] == p2['plate_text']:
excluded_plates_indexes.append(j)
if p1['valid_plate'] and not p2['valid_plate']:
# If the first one is valid and the second one isn't,
# remove the second one.
excluded_plates_indexes.append(j)
elif not p1['valid_plate'] and p2['valid_plate']:
# If the first one is NOT valid and the second one is
# valid, break the inner loop, thus preventing the first
# one to be added (it's the else part of the loop).
break
else:
# Choose the one that appeared more frequently in the past
# TODO
excluded_plates_indexes.append(j)
else:
unique_plates_list.append(p1)
return unique_plates_list
def process_batch(nets, optim, optim2, image_size, args):
global it, mean_loss, mean_rec
net, net_ctc = nets
net = net.net
net_ctc = net_ctc.net
net.blobs['data'].reshape(args.batch_size,1,image_size[1],image_size[0])
net.reshape()
it += 1
optim2.step(1)
im = net.blobs['data'].data[...]
draw = np.swapaxes(im,2,3)
draw = np.swapaxes(draw,1,3)
im_ctc = np.copy(draw)
draw += 1
draw *= 128
draw = np.array(draw, dtype="uint8").copy()
if args.debug:
grid_step = 16
line = 0
while line < image_size[0]:
cv2.line(draw[0], (0, line), (image_size[1], line), (128, 128, 128))
line += grid_step
boxes = net.blobs['boxes'].data[...]
word_gtob = net.blobs['gt_boxes'].data[...]
word_txt = net.blobs['gt_labels'].data[...]
lines_gtob = net.blobs['line_boxes'].data[...]
lines_txt = net.blobs['line_labels'].data[...]
#nms = boxeso[:, 0, 0, 8] == 0
#boxes = boxes[:, :, nms, :]
boxes[:, 0, :, 0] *= image_size[0]
boxes[:, 0, :, 1] *= image_size[1]
normFactor = math.sqrt(image_size[1] * image_size[1] + image_size[0] * image_size[0])
boxes[:, 0, :, 2] *= normFactor
boxes[:, 0, :, 3] *= normFactor
sum_cost = 0
count = 0
labels_gt = []
labels_det = []
gt_to_detection = {}
net_ctc.clear_param_diffs()
batch_buckets = []
dummy = {}
matched_detections = 0
for bid in range(im.shape[0]):
o_image = net.layers[0].get_image_file_name(bid)
o_image = cv2.imread(o_image, cv2.IMREAD_GRAYSCALE)
cx = net.layers[0].get_crop(bid, 0)
cy = net.layers[0].get_crop(bid, 1)
cmx = net.layers[0].get_crop(bid, 2)
cmy = net.layers[0].get_crop(bid, 3)
o_image = o_image[cy:cmy, cx:cmx]
boxes_count = 0
for i in range(0, boxes.shape[2]):
det_word = boxes[bid, 0, i]
if (det_word[0] == 0 and det_word[1] == 0) or det_word[5] < 0.01:
break
boxes_count += 1
x = [i for i in range(boxes_count)]
#random.shuffle(x)
bucket_images = {}
batch_buckets.append(bucket_images)
word_gto = word_gtob[bid]
word_gto_txt = word_txt[bid]
gt_count = 0
for gt_no in range(word_gto.shape[0]):
gt = word_gto[gt_no, :]
gt = gt.reshape(6)
gtnum = 1000 * bid + gt_no
if gt[5] == -1:
#print("ignore gt!")
continue
gt_count += 1
txt = word_gto_txt[gt_no, :]
gtbox = ((gt[0] * image_size[0], gt[1] * image_size[1]), (gt[2] * normFactor, gt[3] * normFactor), gt[4] * 180 / 3.14)
gtbox = cv2.boxPoints(gtbox)
gtbox = np.array(gtbox, dtype="int")
rect_gt = cv2.boundingRect(gtbox)
if rect_gt[0] == 0 or rect_gt[1] == 0 or rect_gt[0] + rect_gt[2] >= image_size[0] or rect_gt[1] + rect_gt[3] >= image_size[1]:
continue
if gt[3] * normFactor < 3:
if args.debug:
#print('too small gt!')
vis.draw_box_points(draw[bid], gtbox, color = (255, 255, 0))
cv2.imshow('draw', draw[bid])
continue
if args.debug:
vis.draw_box_points(draw[bid], gtbox, color = (0, 0, 0), thickness=2)
#vis.draw_box_points(draw[bid], gtbox, color = (255, 255, 255))
#cv2.imshow('draw', draw[bid])
rect_gt = [rect_gt[0], rect_gt[1], rect_gt[2], rect_gt[3]]
rect_gt[2] += rect_gt[0]
rect_gt[3] += rect_gt[1]
for i in range(0, min(100, boxes_count)):
if math.fabs(gt[4] - det_word[4]) > math.pi / 16:
continue
det_word = boxes[bid, 0, x[i], :]
if (det_word[0] == 0 and det_word[1] == 0) or det_word[5] < 0.01:
break
box = ((det_word[0], det_word[1]), (det_word[2], det_word[3]), det_word[4] * 180 / 3.14)
box = cv2.boxPoints(box)
if args.debug:
boxp = np.array(box, dtype="int")
vis.draw_box_points(draw[bid], boxp, color = (0, 255, 0))
box = np.array(box, dtype="int")
bbox = cv2.boundingRect(box)
bbox = [bbox[0], bbox[1], bbox[2], bbox[3]]
bbox[2] += bbox[0]
bbox[3] += bbox[1]
#rectangle intersection ...
inter = intersect(bbox, rect_gt)
uni = union(bbox, rect_gt)
ratio = area(inter) / float(area(uni))
ratio_gt = area(inter) / float(area(rect_gt))
if ratio_gt < 0.95:
continue
if ratio < 0.5:
continue
if not gt_to_detection.has_key(gtnum):
gt_to_detection[gtnum] = [0, 0, 0]
tupl = gt_to_detection[gtnum]
if tupl[0] < ratio:
tupl[0] = ratio
tupl[1] = x[i]
tupl[2] = ratio_gt
det_word = boxes[bid, 0, x[i], :]
box = ([det_word[0], det_word[1]], [det_word[2], det_word[3]], det_word[4] * 180 / 3.14)
boxO = get_obox(im_ctc[bid], o_image, box)
boxO = ((boxO[0][0], boxO[0][1]), (boxO[1][0], boxO[1][1]), boxO[2])
norm2, rot_mat = get_normalized_image(o_image, boxO)
#norm3, rot_mat = get_normalized_image(im_ctc[bid], ([det_word[0], det_word[1]], [det_word[2] * 1.2, det_word[3] * 1.1], det_word[4] * 180 / 3.14))
if norm2 is None:
continue
#if norm3 is None:
# continue
#continue
#cv2.imshow('ts', norm2)
#cv2.imshow('ts3', norm3)
#cv2.waitKey(1)
width_scale = 32.0 / norm2.shape[0]
width = norm2.shape[1] * width_scale
best_diff = width
bestb = 0
for b in range(0, len(buckets)):
if best_diff > abs(width * 1.3 - buckets[b]):
best_diff = abs(width * 1.3 - buckets[b])
bestb = b
scaled = cv2.resize(norm2, (buckets[bestb], 32))
scaled = np.asarray(scaled, dtype=np.float)
delta = scaled.max() - scaled.min()
scaled = (scaled) / (delta / 2)
scaled -= scaled.mean()
if not bucket_images.has_key(bestb):
bucket_images[bestb] = {}
bucket_images[bestb]['img'] = []
bucket_images[bestb]['sizes'] = []
bucket_images[bestb]['txt'] = []
bucket_images[bestb]['gt_enc'] = []
dummy[bestb] = 1
else:
if args.debug and len(bucket_images[bestb]) > 4:
continue
elif len(bucket_images[bestb]) > 32:
continue
gt_labels = []
txt_enc = ''
for k in range(txt.shape[1]):
if txt[0, k] > 0:
if codec_rev.has_key(txt[0, k]):
gt_labels.append( codec_rev[txt[0, k]] )
else:
gt_labels.append( 3 )
txt_enc += unichr(txt[0, k])
else:
gt_labels.append( 0 )
if scaled.ndim == 3:
scaled = cv2.cvtColor(scaled, cv2.COLOR_BGR2GRAY)
if args.debug:
cv2.imshow('scaled', scaled)
bucket_images[bestb]['sizes'].append(len(gt_labels))
bucket_images[bestb]['gt_enc'].append(gt_labels)
bucket_images[bestb]['txt'].append(txt_enc)
bucket_images[bestb]['img'].append(scaled)
matched_detections += 1
#and learn OCR
for bucket in bucket_images.keys():
imtf = np.asarray(bucket_images[bucket]['img'], dtype=np.float)
imtf = np.reshape(imtf, (imtf.shape[0], -1, imtf.shape[1], imtf.shape[2]))
#imtf = imtf.reshape((imtf.shape[0], imtf.shape[1], imtf.shape[2], 1))
#imtf = np.swapaxes(imtf,1,3)
net_ctc.blobs['data'].reshape(imtf.shape[0],imtf.shape[1],imtf.shape[2], imtf.shape[3])
net_ctc.blobs['data'].data[...] = imtf
labels = bucket_images[bucket]['gt_enc']
txt = bucket_images[bucket]['txt']
max_len = 0
for l in range(0, len(labels)):
max_len = max(max_len, len(labels[l]))
for l in range(0, len(labels)):
while len(labels[l]) < max_len:
labels[l].append(0)
labels = np.asarray(labels, np.float)
net_ctc.blobs['label'].reshape(labels.shape[0], labels.shape[1])
net_ctc.blobs['label'].data[...] = labels
if args.debug:
vis.vis_square(imtf[0])
cv2.imshow('draw', draw[0])
cv2.waitKey(5)
optim.step(1)
sum_cost += net_ctc.blobs['loss'].data[...]
if net_ctc.blobs['loss'].data[...] > 10:
vis.vis_square(imtf[0])
cv2.imshow('draw', draw[0])
sf = net_ctc.blobs['transpose'].data[...]
labels2 = sf.argmax(3)
out = utils.print_seq(labels2[:, 0, :])
print(u'{0} - {1}'.format(out, txt[0]) )
cv2.waitKey(5)
count += imtf.shape[0]
correct_cout = 0
for i in range(len(labels_gt)):
det_text = labels_det[i]
gt_text = labels_gt[i]
if it % 100 == 0:
print( u"{0} - {1}".format(det_text, gt_text).encode('utf8') )
if det_text == gt_text:
correct_cout += 1
count = max(count, 1)
mean_loss = 0.99 * mean_loss + 0.01 * sum_cost / count
mean_rec = mean_rec * 0.99 + 0.01 * correct_cout / float(max(1, len(labels_gt)))
#count detection ratio
tp = 0
for bid in range(im.shape[0]):
word_gto = word_gtob[bid]
for gt_no in range(len(word_gto)):
gt = word_gto[gt_no]
gtnum = 1000 * bid + gt_no
if gt_to_detection.has_key(gtnum):
tupl = gt_to_detection[gtnum]
if tupl[0] > 0.5:
tp += 1
loc_recall = tp / float(max(1, gt_count))
if args.debug:
cv2.imshow('draw', draw[0])
if im.shape[0] > 1:
cv2.imshow('draw2', draw[1])
cv2.waitKey(10)
if it % 10 == 0:
print('{0} - lr:{1:.3e} ctc:{2:.4f}/{3:.4f} wr:{4:.2f}/{5:.2f}, loc:{6:.2f} {7}'.format(it, 0.0001, sum_cost / count, mean_loss, correct_cout / float(max(1, len(labels_gt))), mean_rec, loc_recall, matched_detections))
if it % 1000 == 0:
optim.snapshot()
optim2.snapshot()
def evaluate_image(batch,
detections,
word_gto,
iou_th=0.3,
iou_th_vis=0.5,
iou_th_eval=0.4):
'''
Summary : Returns end-to-end true-positives, detection true-positives, number of GT to be considered for eval (len > 2).
Description : For each predicted bounding-box, comparision is made with each GT entry. Values of number of end-to-end true
positives, number of detection true positives, number of GT entries to be considered for evaluation are computed.
Parameters
----------
iou_th_eval : float
Threshold value of intersection-over-union used for evaluation of predicted bounding-boxes
iou_th_vis : float
Threshold value of intersection-over-union used for visualization when transciption is true but IoU is lesser.
iou_th : float
Threshold value of intersection-over-union between GT and prediction.
word_gto : list of lists
List of ground-truth bounding boxes along with transcription.
batch : list of lists
List containing data (input image, image file name, ground truth).
detections : tuple of tuples
Tuple of predicted bounding boxes along with transcriptions and text/no-text score.
Returns
-------
tp : int
Number of predicted bounding-boxes having IoU with GT greater than iou_th_eval.
tp_e2e : int
Number of predicted bounding-boxes having same transciption as GT and len > 2.
gt_e2e : int
Number of GT entries for which transcription len > 2.
'''
gt_to_detection = {}
tp = 0
tp_e2e = 0
gt_e2e = 0
draw = batch[4][0]
normFactor = math.sqrt(
draw.shape[1] * draw.shape[1] +
draw.shape[0] * draw.shape[0]) # Normalization factor
for i in range(0, len(detections)):
det = detections[i]
boxr = det[0]
box = cv2.boxPoints(boxr) # Predicted bounding-box parameters
box = np.array(
box, dtype="int") # Convert predicted bounding-box to numpy array
bbox = cv2.boundingRect(box)
bbox = [bbox[0], bbox[1], bbox[2], bbox[3]]
bbox[2] += bbox[0] # Convert width to right-coordinate
bbox[3] += bbox[1] # Convert height to bottom-coordinate
vis.draw_box_points(draw, box, color=(255, 0, 0))
det_text = det[1][0] # Predicted transcription for bounding-box
#print(det_text)
for gt_no in range(len(word_gto)):
gt = word_gto[gt_no]
txt = gt[5] # GT transcription for given GT bounding-box
gtbox = ((gt[0] * draw.shape[1], gt[1] * draw.shape[0]),
(gt[2] * normFactor, gt[3] * normFactor),
gt[4] * 180 / 3.14) # Re-scaling GT values
gtbox = cv2.boxPoints(gtbox)
gtbox = np.array(gtbox, dtype="int")
rect_gt = cv2.boundingRect(gtbox)
rect_gt = [rect_gt[0], rect_gt[1], rect_gt[2], rect_gt[3]]
rect_gt[2] += rect_gt[0] # Convert GT width to right-coordinate
rect_gt[3] += rect_gt[1] # Convert GT height to bottom-coordinate
inter = intersect(
bbox,
rect_gt) # Intersection of predicted and GT bounding-boxes
uni = union(bbox,
rect_gt) # Union of predicted and GT bounding-boxes
ratio = area(inter) / float(area(
uni)) # IoU measure between predicted and GT bounding-boxes
# 1). Visualize the predicted-bounding box if IoU with GT is higher than IoU threshold (iou_th) (Always required)
# 2). Visualize the predicted-bounding box if transcription matches the GT and condition 1. holds
# 3). Visualize the predicted-bounding box if transcription matches and IoU with GT is less than iou_th_vis and 1. and 2. hold
if ratio > iou_th:
vis.draw_box_points(draw, box, color=(0, 128, 0))
if not gt_to_detection.has_key(gt_no):
gt_to_detection[gt_no] = [0, 0]
if txt.lower() == det_text.lower():
to_cls_x.append(
[len(det_text), det[1][1], det[1][2], det[1][3]])
to_cls_y.append(1)
vis.draw_box_points(draw,
box,
color=(0, 255, 0),
thickness=2)
gt[7] = 1 # Change this parameter to 1 when predicted transcription is correct.
if ratio < iou_th_vis:
vis.draw_box_points(draw,
box,
color=(255, 255, 255),
thickness=2)
cv2.imshow('draw', draw)
#cv2.waitKey(0)
else:
to_cls_x.append(
[len(det_text), det[1][1], det[1][2], det[1][3]])
to_cls_y.append(0)
tupl = gt_to_detection[gt_no]
if tupl[0] < ratio:
tupl[0] = ratio
tupl[1] = i
# Count the number of end-to-end and detection true-positives
for gt_no in range(len(word_gto)):
gt = word_gto[gt_no]
txt = gt[5]
if len(txt) > 2:
gt_e2e += 1
if gt[7] == 1:
tp_e2e += 1
if gt_to_detection.has_key(gt_no):
tupl = gt_to_detection[gt_no]
if tupl[0] > iou_th_eval: # Increment detection true-positive, if IoU is greater than iou_th_eval
tp += 1
cv2.imshow('draw', draw)
return tp, tp_e2e, gt_e2e
def evaluate_image(batch, detections, word_gto, iou_th=0.3, iou_th_vis=0.5, iou_th_eval=0.4):
'''
Summary : Returns end-to-end true-positives, detection true-positives, number of GT to be considered for eval (len > 2).
Description : For each predicted bounding-box, comparision is made with each GT entry. Values of number of end-to-end true
positives, number of detection true positives, number of GT entries to be considered for evaluation are computed.
Parameters
----------
iou_th_eval : float
Threshold value of intersection-over-union used for evaluation of predicted bounding-boxes
iou_th_vis : float
Threshold value of intersection-over-union used for visualization when transciption is true but IoU is lesser.
iou_th : float
Threshold value of intersection-over-union between GT and prediction.
word_gto : list of lists
List of ground-truth bounding boxes along with transcription.
batch : list of lists
List containing data (input image, image file name, ground truth).
detections : tuple of tuples
Tuple of predicted bounding boxes along with transcriptions and text/no-text score.
Returns
-------
tp : int
Number of predicted bounding-boxes having IoU with GT greater than iou_th_eval.
tp_e2e : int
Number of predicted bounding-boxes having same transciption as GT and len > 2.
gt_e2e : int
Number of GT entries for which transcription len > 2.
'''
gt_to_detection = {}
tp = 0
tp_e2e = 0
gt_e2e = 0
draw = batch[4][0]
normFactor = math.sqrt(draw.shape[1] * draw.shape[1] + draw.shape[0] * draw.shape[0]) # Normalization factor
for i in range(0, len(detections)):
det = detections[i]
boxr = det[0]
box = cv2.boxPoints(boxr) # Predicted bounding-box parameters
box = np.array(box, dtype="int") # Convert predicted bounding-box to numpy array
bbox = cv2.boundingRect(box)
bbox = [bbox[0], bbox[1], bbox[2], bbox[3]]
bbox[2] += bbox[0] # Convert width to right-coordinate
bbox[3] += bbox[1] # Convert height to bottom-coordinate
vis.draw_box_points(draw, box, color = (255, 0, 0))
det_text = det[1][0] # Predicted transcription for bounding-box
#print(det_text)
for gt_no in range(len(word_gto)):
gt = word_gto[gt_no]
txt = gt[5] # GT transcription for given GT bounding-box
gtbox = ((gt[0] * draw.shape[1], gt[1] * draw.shape[0]), (gt[2] * normFactor, gt[3] * normFactor), gt[4] * 180 / 3.14) # Re-scaling GT values
gtbox = cv2.boxPoints(gtbox)
gtbox = np.array(gtbox, dtype="int")
rect_gt = cv2.boundingRect(gtbox)
rect_gt = [rect_gt[0], rect_gt[1], rect_gt[2], rect_gt[3]]
rect_gt[2] += rect_gt[0] # Convert GT width to right-coordinate
rect_gt[3] += rect_gt[1] # Convert GT height to bottom-coordinate
inter = intersect(bbox, rect_gt) # Intersection of predicted and GT bounding-boxes
uni = union(bbox, rect_gt) # Union of predicted and GT bounding-boxes
ratio = area(inter) / float(area(uni)) # IoU measure between predicted and GT bounding-boxes
# 1). Visualize the predicted-bounding box if IoU with GT is higher than IoU threshold (iou_th) (Always required)
# 2). Visualize the predicted-bounding box if transcription matches the GT and condition 1. holds
# 3). Visualize the predicted-bounding box if transcription matches and IoU with GT is less than iou_th_vis and 1. and 2. hold
if ratio > iou_th:
vis.draw_box_points(draw, box, color = (0, 128, 0))
if not gt_to_detection.has_key(gt_no):
gt_to_detection[gt_no] = [0, 0]
if txt.lower() == det_text.lower():
to_cls_x.append([len(det_text), det[1][1], det[1][2], det[1][3]])
to_cls_y.append(1)
vis.draw_box_points(draw, box, color = (0, 255, 0), thickness=2)
gt[7] = 1 # Change this parameter to 1 when predicted transcription is correct.
if ratio < iou_th_vis:
vis.draw_box_points(draw, box, color = (255, 255, 255), thickness=2)
cv2.imshow('draw', draw)
#cv2.waitKey(0)
else:
to_cls_x.append([len(det_text), det[1][1], det[1][2], det[1][3]])
to_cls_y.append(0)
tupl = gt_to_detection[gt_no]
if tupl[0] < ratio:
tupl[0] = ratio
tupl[1] = i
# Count the number of end-to-end and detection true-positives
for gt_no in range(len(word_gto)):
gt = word_gto[gt_no]
txt = gt[5]
if len(txt) > 2:
gt_e2e += 1
if gt[7] == 1:
tp_e2e += 1
if gt_to_detection.has_key(gt_no):
tupl = gt_to_detection[gt_no]
if tupl[0] > iou_th_eval: # Increment detection true-positive, if IoU is greater than iou_th_eval
tp += 1
cv2.imshow('draw', draw)
return tp, tp_e2e, gt_e2e
def run_evaluation(inputDir, outputDir, invert=False, isFp=False):
if not os.path.exists(outputDir):
os.mkdir(outputDir)
images = glob.glob('{0}/*.jpg'.format(inputDir))
images.extend(glob.glob('{0}/*.JPG'.format(inputDir)))
images.extend(glob.glob('{0}/*.png'.format(inputDir)))
segmDir = '{0}/segmentations'.format(inputDir)
for image in images:
print('Processing {0}'.format(image))
img = cv2.imread(image, 0)
imgc = cv2.imread(image)
imgproc = img
imgKp = np.copy(img)
imgKp.fill(0)
baseName = os.path.basename(image)
baseName = baseName[:-4]
workPoint = 0.3
segmentations = ftext.getCharSegmentations(
imgproc) #, outputDir, baseName)
segmentations = segmentations[:, 0:10]
segmentations = np.column_stack([
segmentations,
np.zeros((segmentations.shape[0], 2), dtype=np.float)
])
maskDuplicates = segmentations[:, 8] == -1
segmentationsDuplicates = segmentations[maskDuplicates, :]
maskNoNei = segmentationsDuplicates[:, 9] > workPoint
segmentationsNoNei = segmentationsDuplicates[maskNoNei, :]
keypoints = ftext.getLastDetectionKeypoints()
imgKp[keypoints[:, 1].astype(int), keypoints[:, 0].astype(int)] = 255
scales = ftext.getImageScales()
statc = ftext.getDetectionStat()
words = ftext.findTextLines()
segmLine = segmentations[segmentations[:, 7] == 1.0, :]
segmentations[:, 2] += segmentations[:, 0]
segmentations[:, 3] += segmentations[:, 1]
if isFp:
for detId in range(0, segmentations.shape[0]):
ftext.acummulateCharFeatures(0, detId)
continue
lineGt = '{0}/gt_{1}.txt'.format(inputDir, baseName)
if not os.path.exists(lineGt):
lineGt = '{0}/{1}.txt'.format(inputDir, baseName)
lineGt = '{0}/gt_{1}.txt'.format(inputDir, baseName)
if os.path.exists(lineGt):
try:
word_gt = utls.read_icdar2013_txt_gt(lineGt)
except ValueError:
try:
word_gt = utls.read_icdar2013_txt_gt(lineGt, separator=',')
except ValueError:
word_gt = utls.read_icdar2015_txt_gt(lineGt, separator=',')
else:
lineGt = '{0}/{1}.txt'.format(inputDir, baseName)
word_gt = utls.read_mrrc_txt_gt(lineGt, separator=',')
rWcurrent = 0.0
for gt_box in word_gt:
if len(gt_box[4]) == 1:
continue
best_match = 0
cv2.rectangle(imgc, (gt_box[0], gt_box[1]), (gt_box[2], gt_box[3]),
(0, 255, 0))
for det_word in words:
rect_int = utils.intersect(det_word, gt_box)
int_area = utils.area(rect_int)
union_area = utils.area(utils.union(det_word, gt_box))
if union_area == 0:
continue
ratio = int_area / float(union_area)
det_word[11] = max(det_word[11], ratio)
if ratio > best_match:
best_match = ratio
rWcurrent += best_match
best_match = 0
for detId in range(segmentations.shape[0]):
rectn = segmentations[detId, :]
rect_int = utils.intersect(rectn, gt_box)
int_area = utils.area(rect_int)
union_area = utils.area(utils.union(rectn, gt_box))
ratio = int_area / float(union_area)
rectn[11] = max(ratio, rectn[11])
if ratio > best_match:
best_match = ratio
if ratio > 0.7:
#print( "Word Match!" )
#tmp = ftext.getSegmentationMask(detId)
#cv2.imshow("ts", tmp)
#cv2.waitKey(0)
ftext.acummulateCharFeatures(2, detId)
segmImg = '{0}/{1}_GT.bmp'.format(segmDir, baseName)
if not os.path.exists(segmImg):
segmImg = '{0}/gt_{1}.png'.format(segmDir, baseName)
if not os.path.exists(segmImg):
segmImg = '{0}/{1}.png'.format(segmDir, baseName)
segmImg = cv2.imread(segmImg, 0)
if invert and segmImg is not None:
segmImg = ~segmImg
gt_rects = []
miss_rects = []
segmGt = '{0}/{1}_GT.txt'.format(segmDir, baseName)
if os.path.exists(segmGt) and False:
(gt_rects, groups) = utls.read_icdar2013_segm_gt(segmGt)
segmImg = '{0}/{1}_GT.bmp'.format(segmDir, baseName)
if not os.path.exists(segmImg):
segmImg = '{0}/gt_{1}.png'.format(segmDir, baseName)
segmImg = cv2.imread(segmImg)
else:
contours = cv2.findContours(np.copy(segmImg),
mode=cv2.RETR_EXTERNAL,
method=cv2.CHAIN_APPROX_SIMPLE)[1]
for cont in contours:
rect = cv2.boundingRect(cont)
rect = [
rect[0], rect[1], rect[0] + rect[2], rect[1] + rect[3],
'?', 0, 0
]
gt_rects.append(rect)
for detId in range(segmentations.shape[0]):
rectn = segmentations[detId, :]
for k in range(len(gt_rects)):
gt_rect = gt_rects[k]
best_match = 0
best_match_line = 0
if (gt_rect[4] == ',' or gt_rect[4] == '.'
or gt_rect[4] == '\'' or gt_rect[4] == ':'
or gt_rect[4] == '-') and not evalPunctuation:
continue
minSingleOverlap = MIN_SEGM_OVRLAP
if gt_rect[4] == 'i' or gt_rect[4] == '!':
minSingleOverlap = 0.5
rect_int = utils.intersect(rectn, gt_rect)
int_area = utils.area(rect_int)
union_area = utils.area(utils.union(rectn, gt_rect))
ratio = int_area / float(union_area)
rectn[10] = max(ratio, rectn[10])
if rectn[9] > workPoint:
gt_rect[6] = max(ratio, gt_rect[6])
if ratio > best_match:
best_match = ratio
if ratio > best_match_line and rectn[7] == 1.0:
best_match_line = ratio
if ratio > minSingleOverlap:
ftext.acummulateCharFeatures(1, detId)
if ratio < minSingleOverlap:
if k < len(gt_rects) - 1:
gt_rect2 = gt_rects[k + 1]
chars2Rect = utils.union(gt_rect2, gt_rect)
rect_int = utils.intersect(rectn, chars2Rect)
int_area = utils.area(rect_int)
union_area = utils.area(utils.union(rectn, chars2Rect))
ratio = int_area / float(union_area)
rectn[10] = max(ratio, rectn[10])
if ratio > 0.8:
best_match2 = ratio
gt_rect[5] = ratio
gt_rect2[5] = ratio
ftext.acummulateCharFeatures(2, detId)
thickness = 1
color = (255, 0, 255)
if best_match >= minSingleOverlap:
color = (0, 255, 0)
if best_match > 0.7:
thickness = 2
cv2.rectangle(imgc, (gt_rect[0], gt_rect[1]),
(gt_rect[2], gt_rect[3]), color, thickness)
if rectn[10] == 0 and rectn[11] == 0:
ftext.acummulateCharFeatures(0, detId)
'''
def run_words(inputDir, outputDir, invert=False):
if not os.path.exists(outputDir):
os.mkdir(outputDir)
#images = glob.glob('{0}/*.png'.format('/datagrid/personal/TextSpotter/evaluation-sets/MS-text_database'))
#images = glob.glob('{0}/*.jpg'.format('/datagrid/personal/TextSpotter/evaluation-sets/neocr_dataset'))
images = glob.glob('{0}/*.jpg'.format(inputDir))
images.extend(glob.glob('{0}/*.JPG'.format(inputDir)))
images.extend(glob.glob('{0}/*.png'.format(inputDir)))
matched_words = 0
word_count = 0
for image in sorted(images):
print('Processing {0}'.format(image))
img = cv2.imread(image, 0)
imgc = cv2.imread(image)
imgproc = img
imgKp = np.copy(img)
imgKp.fill(0)
baseName = os.path.basename(image)
baseName = baseName[:-4]
workPoint = 0.3
segmentations = ftext.getCharSegmentations(
imgproc) #, outputDir, baseName)
segmentations = segmentations[:, 0:10]
segmentations = np.column_stack([
segmentations,
np.zeros((segmentations.shape[0], 2), dtype=np.float)
])
maskDuplicates = segmentations[:, 8] == -1
segmentationsDuplicates = segmentations[maskDuplicates, :]
maskNoNei = segmentationsDuplicates[:, 9] > workPoint
keypoints = ftext.getLastDetectionKeypoints()
imgKp[keypoints[:, 1].astype(int), keypoints[:, 0].astype(int)] = 255
scales = ftext.getImageScales()
statc = ftext.getDetectionStat()
words = ftext.findTextLines()
segmentations[:, 2] += segmentations[:, 0]
segmentations[:, 3] += segmentations[:, 1]
lineGt = '{0}/gt_{1}.txt'.format(inputDir, baseName)
if not os.path.exists(lineGt):
lineGt = '{0}/{1}.txt'.format(inputDir, baseName)
lineGt = '{0}/gt_{1}.txt'.format(inputDir, baseName)
if os.path.exists(lineGt):
try:
word_gt = utls.read_icdar2013_txt_gt(lineGt)
except ValueError:
try:
word_gt = utls.read_icdar2013_txt_gt(lineGt, separator=',')
except ValueError:
word_gt = utls.read_icdar2015_txt_gt(lineGt, separator=',')
else:
lineGt = '{0}/{1}.txt'.format(inputDir, baseName)
word_gt = utls.read_mrrc_txt_gt(lineGt, separator=',')
cw = 0
for detId in range(segmentations.shape[0]):
best_match = 0
for gt_box in word_gt:
if len(gt_box[4]) == 1:
continue
if gt_box[4][0] == "#":
continue
cw += 1
rectn = segmentations[detId, :]
rect_int = utils.intersect(rectn, gt_box)
int_area = utils.area(rect_int)
union_area = utils.area(utils.union(rectn, gt_box))
ratio = int_area / float(union_area)
rectn[11] = max(ratio, rectn[11])
if ratio > best_match:
best_match = ratio
if ratio > 0.7:
#print( "Word Match!" )
#cv2.rectangle(imgc, (rectn[0], rectn[1]), (rectn[2], rectn[3]), (0, 255, 0))
#cv2.imshow("ts", imgc)
#cv2.waitKey(0)
ftext.acummulateCharFeatures(2, detId)
if gt_box[5] != -1:
matched_words += 1
gt_box[5] = -1
if best_match == 0:
ftext.acummulateCharFeatures(0, detId)
word_count += cw
print("word recall: {0}".format(matched_words / float(word_count)))
def serve(self):
face_rec_delay = time.time()
no_face_frame = 0
cv2.namedWindow('gandalf', cv2.WINDOW_NORMAL)
cv2.resizeWindow('gandalf', int(self.cw * self.window_mult),
int(self.ch * self.window_mult))
cv2.setWindowProperty('gandalf', cv2.WND_PROP_FULLSCREEN,
cv2.WINDOW_FULLSCREEN)
cam = cv2.VideoCapture(0)
# cam = cv2.VideoCapture(0 + cv2.CAP_DSHOW)
# label = "Hello"
while (True):
ret, frame = cam.read()
# h, w, c = frame.shape
# print(h, w, c)
if not ret:
# dead cam
cam = cv2.VideoCapture(0)
time.sleep(3.000) # some delay to init cam
# cam = cv2.VideoCapture(0 + cv2.CAP_DSHOW)
continue
# cv2.destroyAllWindows()
# return 1
if self.test_face:
frame[120:300, 280:400, :] = self.face_img
# add test face on frame
# go from standby to face detection phase
# if no_face_frame > self.no_face_frame_limit:
# # no error
# cv2.destroyAllWindows()
# return 0
# face detection phase
face_bboxes = self.face_detect.inference(frame)
if len(face_bboxes) > 0:
no_face_frame = 0
areas = [area(box) for box in face_bboxes]
max_id = np.argmax(np.asarray(areas))
mfb = face_bboxes[max_id]
# print(area(mfb), self.face_min_size, frame.shape)
# face consolidation phase, calculate face angle
if area(mfb) > self.face_min_size:
x0 = max(0, mfb[0] - self.face_margin)
y0 = max(0, mfb[1] - self.face_margin)
x1 = min(self.ch, mfb[2] + self.face_margin)
y1 = min(self.cw, mfb[3] + self.face_margin)
main_head = frame[y0:y1, x0:x1, :]
# detect blurry face
h, w, c = main_head.shape
# print("hp img shape: ", img.shape)
if (h > 0) and (w > 0):
blur_face = cv2.resize(main_head, (112, 112))
blur_face_var = cv2.Laplacian(blur_face,
cv2.CV_64F).var()
if blur_face_var < self.face_lap_min_score:
cv2.rectangle(frame, (mfb[0], mfb[1]),
(mfb[2], mfb[3]), (255, 0, 0), 2)
face_rec_delay_amount = time.time(
) - face_rec_delay
if face_rec_delay_amount > self.recognition_delay:
frame = self.display_draw.drawLACText(frame)
else:
frame = self.display_draw.drawLastText(frame)
# label = "please look at the camera"
cv2.imshow("gandalf", frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
cv2.destroyAllWindows()
return 2
continue
# detect head pose
yaw, pitch, roll = self.head_pose.inference(main_head)
if not good_head_angle(yaw, pitch, roll, self.angle_min,
self.angle_max):
cv2.rectangle(frame, (mfb[0], mfb[1]),
(mfb[2], mfb[3]), (255, 0, 0), 2)
face_rec_delay_amount = time.time() - face_rec_delay
if face_rec_delay_amount > self.recognition_delay:
frame = self.display_draw.drawLACText(frame)
else:
frame = self.display_draw.drawLastText(frame)
cv2.imshow("gandalf", frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
cv2.destroyAllWindows()
return 2
continue
# TODO: face liveness detection
else:
# face too small
face_rec_delay_amount = time.time() - face_rec_delay
if face_rec_delay_amount > self.recognition_delay:
frame = self.display_draw.drawMCText(frame)
else:
frame = self.display_draw.drawLastText(frame)
cv2.imshow("gandalf", frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
cv2.destroyAllWindows()
return 2
continue
# face recognition phase
face_rec_delay_amount = time.time() - face_rec_delay
if face_rec_delay_amount >= self.recognition_delay:
main_face = frame[mfb[1]:mfb[3], mfb[0]:mfb[2], :]
# TODO face alignment
face_feature = self.face_embed.inference(main_face)
best_match = bruteforce(face_feature, self.face_database,
self.fm_threshold)
# TODO face record
if best_match is None:
new_log = {
'result': 'failed',
'time':
datetime.now().strftime('%Y-%m-%d %H:%M:%S')
}
self.updateLog(new_log, main_face)
self.display_draw.drawFailedText(frame)
self.recognition_delay = self.recog_fai_delay
else:
self.callDoorControllerSocket()
new_log = {
'result': 'success',
'face_id': best_match['_id'],
'time':
datetime.now().strftime('%Y-%m-%d %H:%M:%S')
}
self.updateLog(new_log, main_face)
self.display_draw.drawSuccessText(
frame, str(best_match['name']))
self.recognition_delay = self.recog_suc_delay
face_rec_delay = time.time()
cv2.rectangle(frame, (mfb[0], mfb[1]), (mfb[2], mfb[3]),
(255, 0, 0), 2)
else:
no_face_frame += 1
face_rec_delay_amount = time.time() - face_rec_delay
if face_rec_delay_amount > self.recognition_delay:
frame = self.display_draw.drawDefaultText(frame)
else:
frame = self.display_draw.drawLastText(frame)
cv2.imshow("gandalf", frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
cv2.destroyAllWindows()
return 2
elif cv2.waitKey(1) & 0xFF == ord('t'):
self.test_face = True
elif cv2.waitKey(1) & 0xFF == ord('y'):
self.test_face = False
def process_batch(nets, optim, optim2, image_size, args):
global it, mean_loss, mean_rec
it += 1 # 迭代次数加一
net, net_ctc = nets
net = net.net
net_ctc = net_ctc.net
net.blobs['data'].reshape(args.batch_size, 1, image_size[1],
image_size[0]) # 把一个batch的输入图片reshape
net.reshape()
optim2.step(1)
im = net.blobs['data'].data[...] # shape [batch_size,1,416,416]
draw = np.swapaxes(im, 2, 3)
draw = np.swapaxes(draw, 1, 3)
im_ctc = np.copy(draw)
draw += 1
draw *= 128
draw = np.array(draw, dtype="uint8").copy()
if args.debug:
grid_step = 16
line = 0
while line < image_size[0]:
cv2.line(draw[0], (0, line), (image_size[1], line),
(128, 128, 128))
line += grid_step
boxes = net.blobs['boxes'].data[...] # shape (4, 1, 500, 15)
word_gtob = net.blobs['gt_boxes'].data[...] # shape (4, 6, 1, 6)
word_txt = net.blobs['gt_labels'].data[...] # shape (4, 6, 1, 14)
lines_gtob = net.blobs['line_boxes'].data[...] # shape (4, 1, 1, 5)
lines_txt = net.blobs['line_labels'].data[...] # shape (4, 1, 1, 7)
#nms = boxeso[:, 0, 0, 8] == 0
#boxes = boxes[:, :, nms, :]
boxes[:, 0, :, 0] *= image_size[0]
boxes[:, 0, :, 1] *= image_size[1]
normFactor = math.sqrt(image_size[1] * image_size[1] +
image_size[0] * image_size[0])
boxes[:, 0, :, 2] *= normFactor
boxes[:, 0, :, 3] *= normFactor
sum_cost = 0
count = 0
labels_gt = []
labels_det = []
gt_to_detection = {}
net_ctc.clear_param_diffs()
batch_buckets = []
dummy = {}
matched_detections = 0
for bid in range(im.shape[0]): # 遍历batchsize下的每一个样本
o_image = net.layers[0].get_image_file_name(bid)
o_image = cv2.imread(o_image, cv2.IMREAD_GRAYSCALE)
cx = net.layers[0].get_crop(bid, 0)
cy = net.layers[0].get_crop(bid, 1)
cmx = net.layers[0].get_crop(bid, 2)
cmy = net.layers[0].get_crop(bid, 3)
o_image = o_image[cy:cmy, cx:cmx]
boxes_count = 0
for i in range(0, boxes.shape[2]):
det_word = boxes[bid, 0, i]
if (det_word[0] == 0 and det_word[1] == 0) or det_word[5] < 0.01:
break
boxes_count += 1
x = [i for i in range(boxes_count)]
#random.shuffle(x)
bucket_images = {}
batch_buckets.append(bucket_images)
word_gto = word_gtob[bid]
word_gto_txt = word_txt[bid]
gt_count = 0
for gt_no in range(word_gto.shape[0]):
gt = word_gto[gt_no, :]
gt = gt.reshape(6)
gtnum = 1000 * bid + gt_no
if gt[5] == -1:
#print("ignore gt!")
continue
gt_count += 1
txt = word_gto_txt[gt_no, :]
gtbox = ((gt[0] * image_size[0], gt[1] * image_size[1]),
(gt[2] * normFactor,
gt[3] * normFactor), gt[4] * 180 / 3.14)
gtbox = cv2.boxPoints(gtbox)
gtbox = np.array(gtbox, dtype="int")
rect_gt = cv2.boundingRect(gtbox)
if rect_gt[0] == 0 or rect_gt[
1] == 0 or rect_gt[0] + rect_gt[2] >= image_size[
0] or rect_gt[1] + rect_gt[3] >= image_size[1]:
continue
if gt[3] * normFactor < 3:
if args.debug:
pass
print('too small gt!')
continue
rect_gt = [rect_gt[0], rect_gt[1], rect_gt[2], rect_gt[3]]
rect_gt[2] += rect_gt[0]
rect_gt[3] += rect_gt[1]
for i in range(0, min(100, boxes_count)):
if math.fabs(gt[4] - det_word[4]) > math.pi / 16:
continue
det_word = boxes[bid, 0, x[i], :]
if (det_word[0] == 0
and det_word[1] == 0) or det_word[5] < 0.01:
break
box = ((det_word[0], det_word[1]), (det_word[2], det_word[3]),
det_word[4] * 180 / 3.14)
box = cv2.boxPoints(box)
if args.debug:
boxp = np.array(box, dtype="int")
vis.draw_box_points(draw[bid], boxp, color=(0, 255, 0))
box = np.array(box, dtype="int")
bbox = cv2.boundingRect(box)
bbox = [bbox[0], bbox[1], bbox[2], bbox[3]]
bbox[2] += bbox[0]
bbox[3] += bbox[1]
#rectangle intersection ...
inter = intersect(bbox, rect_gt)
uni = union(bbox, rect_gt)
ratio = area(inter) / float(area(uni))
ratio_gt = area(inter) / float(area(rect_gt))
if ratio_gt < 0.95:
continue
if ratio < 0.5:
continue
if not gt_to_detection.has_key(gtnum):
gt_to_detection[gtnum] = [0, 0, 0]
tupl = gt_to_detection[gtnum]
if tupl[0] < ratio:
tupl[0] = ratio
tupl[1] = x[i]
tupl[2] = ratio_gt
det_word = boxes[bid, 0, x[i], :]
box = ([det_word[0],
det_word[1]], [det_word[2],
det_word[3]], det_word[4] * 180 / 3.14)
boxO = get_obox(im_ctc[bid], o_image, box)
boxO = ((boxO[0][0], boxO[0][1]), (boxO[1][0], boxO[1][1]),
boxO[2])
norm2, rot_mat = get_normalized_image(o_image, boxO)
#norm3, rot_mat = get_normalized_image(im_ctc[bid], ([det_word[0], det_word[1]], [det_word[2] * 1.2, det_word[3] * 1.1], det_word[4] * 180 / 3.14))
if norm2 is None:
continue
#if norm3 is None:
# continue
#continue
#cv2.imshow('ts', norm2)
#cv2.imshow('ts3', norm3)
#cv2.waitKey(1)
width_scale = 32.0 / norm2.shape[0]
width = norm2.shape[1] * width_scale
best_diff = width
bestb = 0
for b in range(0, len(buckets)):
if best_diff > abs(width * 1.3 - buckets[b]):
best_diff = abs(width * 1.3 - buckets[b])
bestb = b
scaled = cv2.resize(norm2, (buckets[bestb], 32))
scaled = np.asarray(scaled, dtype=np.float)
delta = scaled.max() - scaled.min()
scaled = (scaled) / (delta / 2)
scaled -= scaled.mean()
if not bucket_images.has_key(bestb):
bucket_images[bestb] = {}
bucket_images[bestb]['img'] = []
bucket_images[bestb]['sizes'] = []
bucket_images[bestb]['txt'] = []
bucket_images[bestb]['gt_enc'] = []
dummy[bestb] = 1
else:
if args.debug and len(bucket_images[bestb]) > 4:
continue
elif len(bucket_images[bestb]) > 32:
continue
gt_labels = []
txt_enc = ''
for k in range(txt.shape[1]):
if txt[0, k] > 0:
if codec_rev.has_key(txt[0, k]):
gt_labels.append(codec_rev[txt[0, k]])
else:
gt_labels.append(3)
txt_enc += unichr(txt[0, k])
else:
gt_labels.append(0)
if scaled.ndim == 3:
scaled = cv2.cvtColor(scaled, cv2.COLOR_BGR2GRAY)
if args.debug:
cv2.imshow('scaled', scaled)
bucket_images[bestb]['sizes'].append(len(gt_labels))
bucket_images[bestb]['gt_enc'].append(gt_labels)
bucket_images[bestb]['txt'].append(txt_enc)
bucket_images[bestb]['img'].append(scaled)
matched_detections += 1
#and learn OCR
for bucket in bucket_images.keys():
imtf = np.asarray(bucket_images[bucket]['img'], dtype=np.float)
imtf = np.reshape(imtf,
(imtf.shape[0], -1, imtf.shape[1], imtf.shape[2]))
#imtf = imtf.reshape((imtf.shape[0], imtf.shape[1], imtf.shape[2], 1))
#imtf = np.swapaxes(imtf,1,3)
net_ctc.blobs['data'].reshape(imtf.shape[0], imtf.shape[1],
imtf.shape[2], imtf.shape[3])
net_ctc.blobs['data'].data[...] = imtf
labels = bucket_images[bucket]['gt_enc']
txt = bucket_images[bucket]['txt']
max_len = 0
for l in range(0, len(labels)):
max_len = max(max_len, len(labels[l]))
for l in range(0, len(labels)):
while len(labels[l]) < max_len:
labels[l].append(0)
labels = np.asarray(labels, np.float)
net_ctc.blobs['label'].reshape(labels.shape[0], labels.shape[1])
net_ctc.blobs['label'].data[...] = labels
if args.debug:
vis.vis_square(imtf[0])
cv2.imshow('draw', draw[0])
cv2.waitKey(5)
#optim.step(1)
sum_cost += net_ctc.blobs['loss'].data[...]
if net_ctc.blobs['loss'].data[...] > 10:
#vis.vis_square(imtf[0])
#cv2.imshow('draw', draw[0])
sf = net_ctc.blobs['transpose'].data[...]
labels2 = sf.argmax(3)
out = utils.print_seq(labels2[:, 0, :])
print(u'{0} --- {1}'.format(out, txt[0]))
#cv2.waitKey(5)
count += imtf.shape[0]
correct_cout = 0
for i in range(len(labels_gt)):
det_text = labels_det[i]
gt_text = labels_gt[i]
if it % 100 == 0:
pass
#print( u"{0} -- {1}".format(det_text, gt_text).encode('utf8') )
if det_text == gt_text:
correct_cout += 1
count = max(count, 1)
mean_loss = 0.99 * mean_loss + 0.01 * sum_cost / count
mean_rec = mean_rec * 0.99 + 0.01 * correct_cout / float(
max(1, len(labels_gt)))
#count detection ratio
tp = 0
for bid in range(im.shape[0]):
word_gto = word_gtob[bid]
for gt_no in range(len(word_gto)):
gt = word_gto[gt_no]
gtnum = 1000 * bid + gt_no
if gt_to_detection.has_key(gtnum):
tupl = gt_to_detection[gtnum]
if tupl[0] > 0.5:
tp += 1
loc_recall = tp / float(max(1, gt_count))
if it % 10 == 0:
print(
'{0} - lr:{1:.3e} ctc:{2:.4f}/{3:.4f} wr:{4:.2f}/{5:.2f}, loc:{6:.2f} {7}'
.format(it, 0.0001, sum_cost / count, mean_loss,
correct_cout / float(max(1, len(labels_gt))), mean_rec,
loc_recall, matched_detections))
if it % snapshot_interval == 0:
#optim.snapshot()
optim2.snapshot()
def run_evaluation(inputDir, outputDir, process_color = 0, processTest = 0):
if not os.path.exists(outputDir):
os.mkdir(outputDir)
edgeThreshold = 14
fastex = FASTex(edgeThreshold = edgeThreshold)
modelFile = '/home/busta/outModel.boost'
model = cv2.Boost()
model.load(modelFile)
images = glob.glob('{0}/*.jpg'.format(inputDir))
segmDir = '{0}/segmentations'.format(inputDir)
precision = 0;
precisionDen = 0
recall = 0
recall05 = 0
recallNonMax = 0
recallDen = 0
wordRecall = 0
wordRecallDen = 0
segm2chars = 0
regionsCount = 0
regionsCountNonMax = 0
missing_segmNonMaxCount = 0
letterKeypointHistogram = defaultdict(lambda : defaultdict(float))
octaveLetterKeypointHistogram = defaultdict(lambda : defaultdict(float))
missing_letters = {}
letterHistogram = defaultdict(int)
missing_segm = {}
missing_segm2 = {}
missing_segmNonMax = {}
diffMaxOctavesMap = {}
diffScoreOctavesMap = {}
segmHistogram = []
segmWordHistogram = []
results = []
hist = None
histFp = None
histDist = None
histDistFp = None
histDistMax = None
histDistMaxWhite = None
histDistMaxFp = None
hist2dDist =None
hist2dDistFp = None
hist2dDistScore = None
hist2dDistScoreFp = None
histDistMaxWhiteFp = None
histSegm = np.zeros((256), dtype = np.float)
histSegmCount = np.zeros((256), dtype = np.int)
stat = np.asarray([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=np.float)
times = []
gtSegmCount = 0
wordsOk = []
wordsFp = []
keypointsTotal = 0
keypointsTotalInside = 0
orbTime = 0
lineNo = 0
perfectWords = 0;
perfectWordsNS = 0;
hasSegm = False
for image in images:
print('Processing {0}'.format(image))
img = cv2.imread(image, 0)
imgc = cv2.imread(image)
imgcO = cv2.imread(image)
if process_color == 1:
imgproc = imgc
else:
imgproc = img
baseName = os.path.basename(image)
baseName = baseName[:-4]
workPoint = 0.3
segmentations = fastex.getCharSegmentations(imgproc, outputDir, baseName)
segmentations = segmentations[:, 0:10]
segmentations = np.column_stack( [ segmentations , np.zeros( (segmentations.shape[0], 2), dtype = np.float ) ] )
maskDuplicates = segmentations[:, 8] == -1
segmentationsDuplicates = segmentations[maskDuplicates, :]
maskNoNei = segmentationsDuplicates[:, 9] > workPoint
segmentationsNoNei = segmentationsDuplicates[maskNoNei, :]
if segmentations.shape[0] > 0:
print( 'Dupl ratio: {0} - {1}/ {2} - {3}'.format(segmentationsDuplicates.shape[0] / float(segmentations.shape[0]), segmentationsDuplicates.shape[0], segmentations.shape[0], segmentationsNoNei.shape[0] ) )
keypoints = fastex.getLastDetectionKeypoints()
keypointsTotal += keypoints.shape[0]
statc = fastex.getDetectionStat()
times.append([ statc[1], statc[2], statc[3], statc[4], statc[5], statc[6], statc[7], statc[8], statc[9], statc[10]])
stat += statc
values = img[ keypoints[:, 1].astype(int), keypoints[:, 0].astype(int) ]
valuesMax = img[keypoints[:, 6].astype(int), keypoints[:, 5].astype(int)]
diffValMax = np.abs(values - valuesMax)
regionsCount += segmentations.shape[0]
regionsCountNonMax += segmentationsNoNei.shape[0]
segmentations[:, 2] += segmentations[:, 0]
segmentations[:, 3] += segmentations[:, 1]
keypointsOrb = fastex.getLastDetectionOrbKeypoints()
orbTime += keypointsOrb[0][9]
segmGt = '{0}/{1}_GT.txt'.format(segmDir, baseName)
pden = 0
rden = 0
if os.path.exists(segmGt):
hasSegm = True
(gt_rects, groups) = utls.read_icdar2013_segm_gt(segmGt)
segmImg = '{0}/{1}_GT.bmp'.format(segmDir, baseName)
if not os.path.exists(segmImg):
segmImg = '{0}/gt_{1}.png'.format(segmDir, baseName)
segmImg = cv2.imread(segmImg)
try:
(hist, histFp, histDist, histDistMax, histDistMaxWhite, hist2dDist, hist2dDistScore, histDistFp, histDistMaxFp, histDistMaxWhiteFp, hist2dDistFp, hist2dDistScoreFp, keypointsInside) = collect_histograms(img, segmImg, keypoints, values, diffValMax, keypointsTotalInside, diffMaxOctavesMap, diffScoreOctavesMap, hist, histFp, histDist, histDistMax, histDistMaxWhite, hist2dDist, hist2dDistScore, histDistFp, histDistMaxFp, histDistMaxWhiteFp, hist2dDistFp, hist2dDistScoreFp)
except:
pass
rcurrent = 0
rcurrent05 = 0
rcurrentNonMax = 0
for k in range(len(gt_rects)):
gt_rect = gt_rects[k]
best_match = 0
best_match_line = 0
if (gt_rect[4] == ',' or gt_rect[4] == '.' or gt_rect[4] == '\'' or gt_rect[4] == ':' or gt_rect[4] == '-') and not evalPunctuation:
continue
gtSegmCount += 1
rectMask = np.bitwise_and(np.bitwise_and( keypointsInside[:, 0] >= gt_rect[0], keypointsInside[:, 0] <= gt_rect[2]), np.bitwise_and(keypointsInside[:, 1] >= gt_rect[1], keypointsInside[:, 1] <= gt_rect[3]))
letterInside = keypointsInside[rectMask, :]
#make keypoints histogram
if letterInside.shape[0] > 0:
octaves = np.unique( letterInside[:, 2])
maxOctave = np.max(octaves)
maxOctavePoints = 0
for i in range(int(maxOctave) + 1):
octavePoints = letterInside[letterInside[:, 2] == i, :]
maxOctavePoints = max(maxOctavePoints, octavePoints.shape[0])
if maxOctavePoints > 0:
octaveLetterKeypointHistogram[gt_rect[4]][0] += 1
if maxOctavePoints > 1:
octaveLetterKeypointHistogram[gt_rect[4]][1] += 1
if maxOctavePoints > 2:
octaveLetterKeypointHistogram[gt_rect[4]][2] += 1
if maxOctavePoints > 3:
octaveLetterKeypointHistogram[gt_rect[4]][3] += 1
if letterInside.shape[0] == 0:
if not missing_letters.has_key(gt_rect[4]):
missing_letters[gt_rect[4]] = []
missing_letters[gt_rect[4]].append( (image, gt_rect) )
if letterInside.shape[0] > 0:
letterKeypointHistogram[gt_rect[4]][0] += 1
if letterInside.shape[0] > 1:
letterKeypointHistogram[gt_rect[4]][1] += 1
if letterInside.shape[0] > 2:
letterKeypointHistogram[gt_rect[4]][2] += 1
if letterInside.shape[0] > 3:
letterKeypointHistogram[gt_rect[4]][3] += 1
letterHistogram[gt_rect[4]] += 1
best_match2 = 0
minSingleOverlap = MIN_SEGM_OVRLAP
if gt_rect[4] == 'i' or gt_rect[4] == '!':
minSingleOverlap = 0.5
for detId in range(segmentations.shape[0]):
rectn = segmentations[detId, :]
rect_int = utils.intersect( rectn, gt_rect )
int_area = utils.area(rect_int)
union_area = utils.area(utils.union(rectn, gt_rect))
ratio = int_area / float(union_area)
rectn[10] = max(ratio, rectn[10])
if rectn[9] > workPoint:
gt_rect[6] = max(ratio, gt_rect[6])
if ratio > best_match:
best_match = ratio
best_segm = segmentations[detId, :]
if ratio > best_match_line and rectn[7] == 1.0 :
best_match_line = ratio
if best_match < minSingleOverlap:
if k < len(gt_rects) - 1:
gt_rect2 = gt_rects[k + 1]
chars2Rect = utils.union(gt_rect2, gt_rect)
rect_int = utils.intersect( rectn, chars2Rect )
int_area = utils.area(rect_int)
union_area = utils.area(utils.union(rectn, chars2Rect))
ratio = int_area / float(union_area)
rectn[10] = max(ratio, rectn[10])
if ratio > best_match2:
if ratio > MIN_SEGM_OVRLAP:
segm2chars += 1
best_match2 = ratio
gt_rect[5] = ratio
gt_rect2[5] = ratio
thickness = 1
color = (255, 0, 255)
if best_match >= minSingleOverlap:
color = (0, 255, 0)
if best_match > 0.7:
thickness = 2
cv2.rectangle(imgc, (gt_rect[0], gt_rect[1]), (gt_rect[2], gt_rect[3]), color, thickness)
recall += best_match
recallNonMax += gt_rect[6]
if best_match >= minSingleOverlap:
recall05 += best_match
rcurrent05 += best_match
else:
if not missing_segm.has_key(image):
missing_segm[image] = []
missing_segm[image].append(gt_rect)
if gt_rect[5] < MIN_SEGM_OVRLAP:
if not missing_segm2.has_key(image):
missing_segm2[image] = []
missing_segm2[image].append(gt_rect)
segm2chars += 1
if gt_rect[6] < minSingleOverlap:
if not missing_segmNonMax.has_key(image):
missing_segmNonMax[image] = []
missing_segmNonMax[image].append(gt_rect)
missing_segmNonMaxCount += 1
rcurrent += best_match
rcurrentNonMax += gt_rect[6]
recallDen += 1
rden += 1
if best_match > 0 and process_color != 1:
val = img[best_segm[5], best_segm[4]]
histSegm[val] += best_match
histSegmCount[val] += 1
pcurrent = 0
for detId in range(segmentations.shape[0]):
best_match = 0
rectn = segmentations[detId, :]
for gt_rect in gt_rects:
rect_int = utils.intersect( rectn, gt_rect )
int_area = utils.area(rect_int)
union_area = utils.area(utils.union(rectn, gt_rect))
ratio = int_area / float(union_area)
if ratio > best_match:
best_match = ratio
precision += best_match
pcurrent += best_match
precisionDen += 1
pden += 1
if pden == 0:
pcurrent = 0
else:
pcurrent = pcurrent / pden
if rden == 0:
rcurrent = 0
rcurrent05 = 0
rcurrentNonMax = 0
else:
rcurrent = rcurrent / rden
rcurrent05 = rcurrent05 / rden
rcurrentNonMax = rcurrentNonMax / rden
segmHistogram.append([ segmentations.shape[0], segmentations[segmentations[:, 10] > 0.4].shape[0], segmentations[segmentations[:, 10] > 0.5].shape[0], segmentations[segmentations[:, 10] > 0.6].shape[0], segmentations[segmentations[:, 10] > 0.7].shape[0] ])
segmWordHistogram.append([segmentations.shape[0], segmentations[np.bitwise_or(segmentations[:, 10] > 0.5, segmentations[:, 11] > 0.5 )].shape[0]])
results.append((baseName, rcurrent, pcurrent, rcurrent05))
if precisionDen == 0:
pcurrent = 0
else:
precision = precision / precisionDen
if recallDen == 0:
rcurrent = 0
else:
recall = recall / recallDen
recall05 = recall05 / recallDen
recallNonMax = recallNonMax / recallDen
wordRecall = wordRecall / max(1, wordRecallDen)
try:
histSegm = histSegm / max(1, histSegmCount)
except ValueError:
pass
print('Evalation Results:')
print( 'recall: {0}, precision: {1}, recall 0.5: {2}, recall NonMax: {3}'.format(recall, precision, recall05, recallNonMax) )
kpTimes = np.histogram(np.asarray(times)[:, 0], bins=20)
print('Keypoint Time Histogram: {0}'.format(kpTimes))
print('Detection statistics:')
print(stat)
for letter in letterKeypointHistogram.keys():
for num in letterKeypointHistogram[letter].keys():
letterKeypointHistogram[letter][num] = letterKeypointHistogram[letter][num] / float(letterHistogram[letter])
for num in octaveLetterKeypointHistogram[letter].keys():
octaveLetterKeypointHistogram[letter][num] = octaveLetterKeypointHistogram[letter][num] / float(letterHistogram[letter])
letterKeypointHistogram[letter] = dict(letterKeypointHistogram[letter])
octaveLetterKeypointHistogram[letter] = dict(octaveLetterKeypointHistogram[letter])
print('Perfect words: {0}'.format(perfectWords))
eval_date = datetime.date.today()
np.savez('{0}/evaluation'.format(outputDir), recall=recall, recall05 = recall05, recallNonMax=recallNonMax, precision=precision, eval_date=eval_date, regionsCount=regionsCount, inputDir = inputDir, hist = hist, histSegm = histSegm, stat=stat, letterKeypointHistogram = dict(letterKeypointHistogram), missing_letters=missing_letters, octaveLetterKeypointHistogram=dict(octaveLetterKeypointHistogram), missing_segm=missing_segm,
times=np.asarray(times), histFp = histFp, gtSegmCount = gtSegmCount, wordRecall=wordRecall, histDist=histDist, histDistFp = histDistFp, histDistMax=histDistMax, histDistMaxFp=histDistMaxFp, hist2dDist=hist2dDist, hist2dDistFp=hist2dDistFp, hist2dDistScore=hist2dDistScore, hist2dDistScoreFp=hist2dDistScoreFp, histDistMaxWhite=histDistMaxWhite, histDistMaxWhiteFp=histDistMaxWhiteFp, wordsOk=wordsOk, wordsFp=wordsFp, diffMaxOctavesMap = diffMaxOctavesMap, diffScoreOctavesMap = diffScoreOctavesMap,
missing_segm2=missing_segm2, segmHistogram=segmHistogram, segmWordHistogram=segmWordHistogram, regionsCountNonMax=regionsCountNonMax, missing_segmNonMax=missing_segmNonMax)
print( "GT segmentations count {0}".format(gtSegmCount) )
print('FasTex Inside {0}/{1} ({2})'.format(keypointsTotalInside, keypointsTotal, keypointsTotalInside / float(keypointsTotal) ))
print('FasText time: {0}, Orb time: {1} '.format( np.sum(times, 0)[0], orbTime))
print('2 Chars Segmentation: {0}'.format(segm2chars) )
print('NonMax Regions Count: {0}/{1}'.format(regionsCountNonMax, missing_segmNonMaxCount))
def creat_mask_VOC_labelfile():
with open(gt_label_file_path, 'r') as f:
gt_lines = f.readlines()
with open(ignore_bbox_file_path, 'r') as f:
ign_lines = f.readlines()
plt.figure(figsize=(10, 10))
for i in gt_lines:
info = i.split(' ')
img_origin = pimg.imread(images_root_path + info[0])
img_copy = img_origin.copy()
plt.imshow(img_copy, aspect='equal')
ign_ = [j.strip().split(' ')[1:] for j in ign_lines if j.split(' ')[0] == info[0][:9]]
ign_bboxs = []
if ign_:
ign_bboxs = [float(b) for b in ign_[0]]
# print(ign_box)
ign_bboxs = np.array(ign_bboxs, dtype=np.float32).reshape(-1, 4)
for b in ign_bboxs:
rect = plt.Rectangle((b[0], b[1]), b[2] - b[0],
b[3] - b[1], fill=True,
facecolor='black',
linewidth=1)
plt.gca().add_patch(rect)
# add black mask to image
img_copy[int(b[1]):int(b[3]), int(b[0]):int(b[2]), :] = 0
#print (ign_bbox)
bbox = [float(b) for b in info[1:]]
boxes = np.array(bbox, dtype=np.float32).reshape(-1, 4)
efficient_bboxs = []
# judge the area_intersect of bbox and ing_bbox
for b in boxes:
bbox_area = area(b)
ratio_intersect = 0.0
area_intersect = []
for ign_bbox in ign_bboxs:
area_intersect.append(area(intersect(b, ign_bbox)))
max_area_intersect = max(area_intersect)
ratio_intersect = max_area_intersect / bbox_area
if ratio_intersect >= 0.2:
continue
else:
rect = plt.Rectangle((b[0], b[1]), b[2] - b[0],
b[3] - b[1], fill=False,
edgecolor=(0, 1, 0),
linewidth=1)
efficient_bboxs.append(b)
plt.gca().add_patch(rect)
image_new_name = info[0].split('/')[0] + '_' + info[0].split('/')[1]
#print (efficient_bboxs)
create_VOC_label_file(os.path.join(image_label_save_rootpath, xml_file_path), image_new_name, img_origin.shape, efficient_bboxs)
image_save_path = os.path.join(os.path.join(image_label_save_rootpath, image_path), image_new_name)
cv2.imwrite(image_save_path, cv2.cvtColor(img_copy, cv2.COLOR_RGB2BGR))
plt.pause(0.01)
plt.cla()
def main():
cam = cv2.VideoCapture(0)
# load data
config = configparser.ConfigParser()
config.read("config.ini")
face_database, face_collection = loadMongoData(config)
# load models
device = "MYRIAD"
data_dir = "data"
plugin = IEPlugin(device, plugin_dirs=None)
face_embed = FaceEmbedding(plugin)
face_detect = MobileFaceDetect(plugin)
# params
fd_conf = 0.5
fm_threshold = 0.6
label = "new"
period = 1
button_pressed = False
max_num = 3
num = 0
face_features = []
face_imgs = []
s = time.time()
while (True):
ret, frame = cam.read()
if not ret:
print("dead cam")
cam = cv2.VideoCapture(0)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
continue
face_bboxes = []
# if time.time() - s > period:
if cv2.waitKey(1) & 0xFF == ord('c'):
button_pressed = True
if button_pressed and (num < max_num):
face_bboxes = face_detect.inference(frame)
if (len(face_bboxes) > 0) and button_pressed:
areas = [area(box) for box in face_bboxes]
max_id = np.argmax(np.asarray(areas))
mfb = face_bboxes[max_id]
main_face = frame[mfb[1]:mfb[3], mfb[0]:mfb[2], :]
# TODO real face detection
# TODO face alignment
face_feature = face_embed.inference(main_face)
face_feature = face_feature.tolist()
face_features.append(face_feature)
face_imgs.append(main_face)
num += 1
button_pressed = False
s = time.time()
# visualize for debug
cv2.rectangle(frame, (mfb[0], mfb[1]), (mfb[2], mfb[3]),
(255, 0, 0), 2)
cv2.putText(frame,
str(num), (mfb[0], mfb[1]),
cv2.FONT_HERSHEY_SIMPLEX,
0.6, (0, 0, 255),
lineType=cv2.LINE_AA)
print(num)
if num >= max_num:
# add new face features to database
new_id = face_database.count()
new_face = {'name': str(new_id), 'feats': face_features}
p_id = face_collection.insert_one(new_face).inserted_id
# commit changes
face_collection.update_one({'_id': p_id}, {"$set": new_face},
upsert=False)
# save images
img_dir = os.path.join(data_dir, str(new_id))
os.mkdir(img_dir)
for i, face in enumerate(face_imgs):
img_path = os.path.join(img_dir, "{}.jpg".format(i))
cv2.imwrite(img_path, face)
face_imgs = []
face_features = []
num = 0
s = time.time()
print("done!")
cv2.imshow("face registration", frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
def main():
utils.area()
utils.reverse()
utils.time_date()