def getProcessedBoxes(self, net_out): """ Takes net output, draw net_out, save to disk """ # meta meta = self.meta H, W, _ = meta['out_size'] C, B = meta['classes'], meta['num'] anchors = meta['anchors'] threshold = self.FLAGS.threshold max_iou = self.FLAGS.slice_max_iou net_out = net_out.reshape([H, W, B, -1]) boxes = list() for row in range(H): for col in range(W): for b in range(B): bx = BoundBox(C) bx.x, bx.y, bx.w, bx.h, bx.c = net_out[row, col, b, :5] #print(bx.x, bx.y, bx.w, bx.h, bx.c) bx.c = expit(bx.c) bx.x = (col + expit(bx.x)) / W bx.y = (row + expit(bx.y)) / H bx.w = math.exp(bx.w) * anchors[2 * b + 0] / W bx.h = math.exp(bx.h) * anchors[2 * b + 1] / H classes = net_out[row, col, b, 5:] bx.probs = _softmax(classes) * bx.c bx.probs *= bx.probs > threshold boxes.append(bx) for c in range(C): for i in range(len(boxes)): max_indx = np.argmax(boxes[i].probs) boxes[i].class_num = max_indx # boxes = sorted(boxes, key = prob_compare) # for i in range(len(boxes)): # boxi = boxes[i] # if boxi.probs[c] == 0: continue # for j in range(i + 1, len(boxes)): # boxj = boxes[j] # if box_iou(boxi, boxj) >= max_iou: # boxes[j].probs[c] = 0. return boxes
def findboxes(self, net_out):
meta, FLAGS = self.meta, self.FLAGS
threshold, sqrt = FLAGS.threshold, meta['sqrt'] + 1
C, B, S = meta['classes'], meta['num'], meta['side']
boxes = []
SS = S * S # number of grid cells
prob_size = SS * C # class probabilities
conf_size = SS * B # confidences for each grid cell
# net_out = net_out[0]
probs = net_out[0:prob_size]
confs = net_out[prob_size:(prob_size + conf_size)]
cords = net_out[(prob_size + conf_size):]
probs = probs.reshape([SS, C])
confs = confs.reshape([SS, B])
cords = cords.reshape([SS, B, 4])
for grid in range(SS):
for b in range(B):
bx = BoundBox(C)
bx.c = confs[grid, b]
bx.x = (cords[grid, b, 0] + grid % S) / S
bx.y = (cords[grid, b, 1] + grid // S) / S
bx.w = cords[grid, b, 2]**sqrt
bx.h = cords[grid, b, 3]**sqrt
p = probs[grid, :] * bx.c
p *= (p > threshold)
bx.probs = p
boxes.append(bx)
# non max suppress boxes
for c in range(C):
for i in range(len(boxes)):
boxes[i].class_num = c
boxes = sorted(boxes, key=prob_compare, reverse=True)
for i in range(len(boxes)):
boxi = boxes[i]
if boxi.probs[c] == 0: continue
for j in range(i + 1, len(boxes)):
boxj = boxes[j]
if box_iou(boxi, boxj) >= .4:
boxes[j].probs[c] = 0.
return boxes
def findboxes(self, net_out):
# meta
meta = self.meta
H, W, _ = meta['out_size']
threshold = meta['thresh']
C, B = meta['classes'], meta['num']
anchors = meta['anchors']
net_out = net_out.reshape([H, W, B, -1])
boxes = list()
for row in range(H):
for col in range(W):
for b in range(B):
bx = BoundBox(C)
bx.x, bx.y, bx.w, bx.h, bx.c = net_out[row, col, b, :5]
bx.c = expit(bx.c)
bx.x = (col + expit(bx.x)) / W
bx.y = (row + expit(bx.y)) / H
bx.w = math.exp(bx.w) * anchors[2 * b + 0] / W
bx.h = math.exp(bx.h) * anchors[2 * b + 1] / H
classes = net_out[row, col, b, 5:]
bx.probs = _softmax(classes) * bx.c
bx.probs *= bx.probs > threshold
boxes.append(bx)
# non max suppress boxes
for c in range(C):
for i in range(len(boxes)):
boxes[i].class_num = c
boxes = sorted(boxes, key=prob_compare, reverse=True)
for i in range(len(boxes)):
boxi = boxes[i]
if boxi.probs[c] == 0: continue
for j in range(i + 1, len(boxes)):
boxj = boxes[j]
if box_iou(boxi, boxj) >= .4:
boxes[j].probs[c] = 0.
return boxes
def postprocess(self, net_out, im, save=True):
"""
Takes net output, draw net_out, save to disk
"""
# meta
meta = self.meta
H, W, _ = meta['out_size']
threshold = meta['thresh']
C, B = meta['classes'], meta['num']
anchors = meta['anchors']
net_out = net_out.reshape([H, W, B, -1])
boxes = list()
for row in range(H):
for col in range(W):
for b in range(B):
bx = BoundBox(C)
bx.x, bx.y, bx.w, bx.h, bx.c = net_out[row, col, b, :5]
bx.c = expit(bx.c)
bx.x = (col + expit(bx.x)) / W
bx.y = (row + expit(bx.y)) / H
bx.w = math.exp(bx.w) * anchors[2 * b + 0] / W
bx.h = math.exp(bx.h) * anchors[2 * b + 1] / H
p = net_out[row, col, b, 5:] * bx.c
mi = np.argmax(p)
if p[mi] < 1.5: continue
bx.ind = mi
bx.pi = p[mi]
boxes.append(bx)
# non max suppress boxes
boxes = sorted(boxes, cmp=prob_compare2)
for i in range(len(boxes)):
boxi = boxes[i]
if boxi.pi == 0: continue
for j in range(i + 1, len(boxes)):
boxj = boxes[j]
areaj = boxj.w * boxj.h
if box_intersection(boxi, boxj) / areaj >= .4:
boxes[j].pi = 0.
colors = meta['colors']
labels = meta['labels']
if type(im) is not np.ndarray:
imgcv = cv2.imread(im)
else:
imgcv = im
h, w, _ = imgcv.shape
for b in boxes:
if b.pi > 0.:
label = labels[b.ind]
left = int((b.x - b.w / 2.) * w)
right = int((b.x + b.w / 2.) * w)
top = int((b.y - b.h / 2.) * h)
bot = int((b.y + b.h / 2.) * h)
if left < 0: left = 0
if right > w - 1: right = w - 1
if top < 0: top = 0
if bot > h - 1: bot = h - 1
thick = int((h + w) / 300)
cv2.rectangle(imgcv, (left, top), (right, bot), colors[b.ind],
thick)
mess = '{}'.format(label)
cv2.putText(imgcv, mess, (left, top - 12), 0, 1e-3 * h,
colors[b.ind], thick / 5)
if not save: return imgcv
outfolder = os.path.join(self.FLAGS.test, 'out')
img_name = os.path.join(outfolder, im.split('/')[-1])
cv2.imwrite(img_name, imgcv)
def postprocess(self, net_out, im, save = True):
"""
Takes net output, draw net_out, save to disk
"""
# meta
meta = self.meta
H, W, _ = meta['out_size']
threshold = meta['thresh']
C, B = meta['classes'], meta['num']
anchors = meta['anchors']
net_out = net_out.reshape([H, W, B, -1])
boxes = list()
for row in range(H):
for col in range(W):
for b in range(B):
bx = BoundBox(C)
bx.x, bx.y, bx.w, bx.h, bx.c = net_out[row, col, b, :5]
bx.c = expit(bx.c)
bx.x = (col + expit(bx.x)) / W
bx.y = (row + expit(bx.y)) / H
bx.w = math.exp(bx.w) * anchors[2 * b + 0] / W
bx.h = math.exp(bx.h) * anchors[2 * b + 1] / H
classes = net_out[row, col, b, 5:]
bx.probs = _softmax(classes) * bx.c
bx.probs *= bx.probs > threshold
boxes.append(bx)
# non max suppress boxes
for c in range(C):
for i in range(len(boxes)):
boxes[i].class_num = c
boxes = sorted(boxes, key = prob_compare, reverse = True)
for i in range(len(boxes)):
boxi = boxes[i]
if boxi.probs[c] == 0: continue
for j in range(i + 1, len(boxes)):
boxj = boxes[j]
if box_iou(boxi, boxj) >= .4:
boxes[j].probs[c] = 0.
colors = meta['colors']
labels = meta['labels']
if type(im) is not np.ndarray:
imgcv = cv2.imread(im)
else: imgcv = im
h, w, _ = imgcv.shape
textBuff = "["
for b in boxes:
max_indx = np.argmax(b.probs)
max_prob = b.probs[max_indx]
label = labels[max_indx]
if max_prob > threshold:
left = int ((b.x - b.w/2.) * w)
right = int ((b.x + b.w/2.) * w)
top = int ((b.y - b.h/2.) * h)
bot = int ((b.y + b.h/2.) * h)
if left < 0 : left = 0
if right > w - 1: right = w - 1
if top < 0 : top = 0
if bot > h - 1: bot = h - 1
thick = int((h+w)/300)
mess = '{}'.format(label)
if self.FLAGS.json:
line = ('{"label":"%s",'
'"topleft":{"x":%d,"y":%d},'
'"bottomright":{"x":%d,"y":%d}},\n') % \
(mess, left, top, right, bot)
textBuff += line
continue
cv2.rectangle(imgcv,
(left, top), (right, bot),
colors[max_indx], thick)
cv2.putText(imgcv, mess, (left, top - 12),
0, 1e-3 * h, colors[max_indx],thick//3)
# Removing trailing comma+newline adding json list terminator.
textBuff = textBuff[:-2] + "]"
outfolder = os.path.join(self.FLAGS.test, 'out')
img_name = os.path.join(outfolder, im.split('/')[-1])
if self.FLAGS.json:
textFile = os.path.splitext(img_name)[0] + ".json"
with open(textFile, 'w') as f:
f.write(textBuff)
return
if not save: return imgcv
cv2.imwrite(img_name, imgcv)
def postprocess(self, net_out, im, save=True):
"""
Takes net output, draw predictions, save to disk
"""
meta, FLAGS = self.meta, self.FLAGS
threshold, sqrt = FLAGS.threshold, meta['sqrt'] + 1
C, B, S = meta['classes'], meta['num'], meta['side']
colors, labels = meta['colors'], meta['labels']
boxes = []
SS = S * S # number of grid cells
prob_size = SS * C # class probabilities
conf_size = SS * B # confidences for each grid cell
#net_out = net_out[0]
probs = net_out[0:prob_size]
confs = net_out[prob_size:(prob_size + conf_size)]
cords = net_out[(prob_size + conf_size):]
probs = probs.reshape([SS, C])
confs = confs.reshape([SS, B])
cords = cords.reshape([SS, B, 4])
for grid in range(SS):
for b in range(B):
bx = BoundBox(C)
bx.c = confs[grid, b]
bx.x = (cords[grid, b, 0] + grid % S) / S
bx.y = (cords[grid, b, 1] + grid // S) / S
bx.w = cords[grid, b, 2]**sqrt
bx.h = cords[grid, b, 3]**sqrt
p = probs[grid, :] * bx.c
p *= (p > threshold)
bx.probs = p
boxes.append(bx)
# non max suppress boxes
for c in range(C):
for i in range(len(boxes)):
boxes[i].class_num = c
boxes = sorted(boxes, key=prob_compare, reverse=True)
for i in range(len(boxes)):
boxi = boxes[i]
if boxi.probs[c] == 0: continue
for j in range(i + 1, len(boxes)):
boxj = boxes[j]
if box_iou(boxi, boxj) >= .4:
boxes[j].probs[c] = 0.
if type(im) is not np.ndarray:
imgcv = cv2.imread(im)
else:
imgcv = im
h, w, _ = imgcv.shape
textBuff = "["
for b in boxes:
max_indx = np.argmax(b.probs)
max_prob = b.probs[max_indx]
label = self.meta['labels'][max_indx]
if max_prob > threshold:
left = int((b.x - b.w / 2.) * w)
right = int((b.x + b.w / 2.) * w)
top = int((b.y - b.h / 2.) * h)
bot = int((b.y + b.h / 2.) * h)
if left < 0: left = 0
if right > w - 1: right = w - 1
if top < 0: top = 0
if bot > h - 1: bot = h - 1
thick = int((h + w) // 300)
mess = '{}'.format(label)
if self.FLAGS.json:
line = ('{"label":"%s",'
'"topleft":{"x":%d,"y":%d},'
'"bottomright":{"x":%d,"y":%d}},\n') % \
(mess, left, top, right, bot)
textBuff += line
continue
cv2.rectangle(imgcv, (left, top), (right, bot),
self.meta['colors'][max_indx], thick)
cv2.putText(imgcv, mess, (left, top - 12), 0, 1e-3 * h,
self.meta['colors'][max_indx], thick // 3)
# Removing trailing comma+newline adding json list terminator.
textBuff = textBuff[:-2] + "]"
if self.FLAGS.json:
textFile = os.path.splitext(img_name)[0] + ".json"
with open(textFile, 'w') as f:
f.write(textBuff)
return
if not save: return imgcv
outfolder = os.path.join(self.FLAGS.test, 'out')
img_name = os.path.join(outfolder, im.split('/')[-1])
cv2.imwrite(img_name, imgcv)
def postprocess(self, net_out, im, save=True):
"""
Takes net output, draw net_out, save to disk
"""
# meta
meta = self.meta
H, W, _ = meta['out_size']
threshold = meta['thresh']
C, B = meta['classes'], meta['num']
anchors = meta['anchors']
net_out = net_out.reshape([H, W, B, -1])
boxes = list()
for row in range(H):
for col in range(W):
for b in range(B):
bx = BoundBox(C)
bx.x, bx.y, bx.w, bx.h, bx.c = net_out[row, col, b, :5]
bx.c = expit(bx.c)
bx.x = (col + expit(bx.x)) / W
bx.y = (row + expit(bx.y)) / H
bx.w = math.exp(bx.w) * anchors[2 * b + 0] / W
bx.h = math.exp(bx.h) * anchors[2 * b + 1] / H
classes = net_out[row, col, b, 5:]
bx.probs = _softmax(classes) * bx.c
bx.probs *= bx.probs > threshold
boxes.append(bx)
# non max suppress boxes
for c in range(C):
for i in range(len(boxes)):
boxes[i].class_num = c
boxes = sorted(boxes, key=prob_compare)
for i in range(len(boxes)):
boxi = boxes[i]
if boxi.probs[c] == 0: continue
for j in range(i + 1, len(boxes)):
boxj = boxes[j]
if box_iou(boxi, boxj) >= .4:
boxes[j].probs[c] = 0.
colors = meta['colors']
labels = meta['labels']
if type(im) is not np.ndarray:
imgcv = cv2.imread(im)
else:
imgcv = im
h, w, _ = imgcv.shape
for b in boxes:
max_indx = np.argmax(b.probs)
max_prob = b.probs[max_indx]
label = 'object' * int(C < 2)
label += labels[max_indx] * int(C > 1)
if max_prob > threshold:
left = int((b.x - b.w / 2.) * w)
right = int((b.x + b.w / 2.) * w)
top = int((b.y - b.h / 2.) * h)
bot = int((b.y + b.h / 2.) * h)
if left < 0: left = 0
if right > w - 1: right = w - 1
if top < 0: top = 0
if bot > h - 1: bot = h - 1
thick = int((h + w) / 300)
cv2.rectangle(imgcv, (left, top), (right, bot), colors[max_indx],
thick)
mess = '{}'.format(label)
cv2.putText(imgcv, mess, (left, top - 12), 0, 1e-3 * h,
colors[max_indx], thick // 3)
if not save: return imgcv
outfolder = os.path.join(self.FLAGS.test, 'out')
img_name = os.path.join(outfolder, im.split('/')[-1])
cv2.imwrite(img_name, imgcv)
# def _postprocess(self, net_out, im, save = True):
# """
# Takes net output, draw net_out, save to disk
# """
# # meta
# meta = self.meta
# H, W, _ = meta['out_size']
# threshold = meta['thresh']
# C, B = meta['classes'], meta['num']
# anchors = meta['anchors']
# net_out = net_out.reshape([H, W, B, -1])
# boxes = list()
# for row in range(H):
# for col in range(W):
# for b in range(B):
# bx = BoundBox(C)
# bx.x, bx.y, bx.w, bx.h, bx.c = net_out[row, col, b, :5]
# bx.c = expit(bx.c)
# bx.x = (col + expit(bx.x)) / W
# bx.y = (row + expit(bx.y)) / H
# bx.w = math.exp(bx.w) * anchors[2 * b + 0] / W
# bx.h = math.exp(bx.h) * anchors[2 * b + 1] / H
# p = net_out[row, col, b, 5:] * bx.c
# mi = np.argmax(p)
# if p[mi] < threshold*2: continue
# bx.ind = mi; bx.pi = p[mi]
# boxes.append(bx)
# # non max suppress boxes
# boxes = sorted(boxes, cmp = prob_compare2)
# for i in range(len(boxes)):
# boxi = boxes[i]
# if boxi.pi == 0: continue
# for j in range(i + 1, len(boxes)):
# boxj = boxes[j]
# areaj = boxj.w * boxj.h
# if box_intersection(boxi, boxj)/areaj >= .4:
# boxes[j].pi = 0.
# colors = meta['colors']
# labels = meta['labels']
# if type(im) is not np.ndarray:
# imgcv = cv2.imread(im)
# else: imgcv = im
# h, w, _ = imgcv.shape
# for b in boxes:
# if b.pi > 0.:
# label = labels[b.ind]
# left = int ((b.x - b.w/2.) * w)
# right = int ((b.x + b.w/2.) * w)
# top = int ((b.y - b.h/2.) * h)
# bot = int ((b.y + b.h/2.) * h)
# if left < 0 : left = 0
# if right > w - 1: right = w - 1
# if top < 0 : top = 0
# if bot > h - 1: bot = h - 1
# thick = int((h+w)/300)
# cv2.rectangle(imgcv,
# (left, top), (right, bot),
# colors[b.ind], thick)
# mess = '{}'.format(label)
# cv2.putText(imgcv, mess, (left, top - 12),
# 0, 1e-3 * h, colors[b.ind], thick // 3)
# if not save: return imgcv
# outfolder = os.path.join(self.FLAGS.test, 'out')
# img_name = os.path.join(outfolder, im.split('/')[-1])
# cv2.imwrite(img_name, imgcv)
def postprocess(predictions, img_path, FLAGS, meta):
"""
Takes net output, draw predictions, save to results/
prediction is a numpy tensor - net's output
img_path is the path to testing folder
FLAGS contains threshold for predictions
meta supplies labels and colors for drawing
"""
# meta
threshold = FLAGS.threshold
C, B, S = meta['classes'], meta['num'], meta['side']
colors, labels = meta['colors'], meta['labels']
boxes = []
SS = S * S # number of grid cells
prob_size = SS * C # class probabilities
conf_size = SS * B # confidences for each grid cell
probs = predictions[0:prob_size]
confs = predictions[prob_size:(prob_size + conf_size)]
cords = predictions[(prob_size + conf_size):]
probs = probs.reshape([SS, C])
confs = confs.reshape([SS, B])
cords = cords.reshape([SS, B, 4])
for grid in range(SS):
for b in range(B):
new_box = BoundBox(C)
new_box.c = confs[grid, b]
new_box.x = (cords[grid, b, 0] + grid % S) / S
new_box.y = (cords[grid, b, 1] + grid // S) / S
new_box.w = cords[grid, b, 2]**2
new_box.h = cords[grid, b, 3]**2
new_box.id = '{}-{}'.format(grid, b)
for c in range(C):
new_box.probs[c] = new_box.c * probs[grid, c]
boxes.append(new_box)
# non max suppress boxes
for c in range(C):
for i in range(len(boxes)):
boxes[i].class_num = c
boxes = sorted(boxes, cmp=prob_compare)
for i in range(len(boxes)):
boxi = boxes[i]
if boxi.probs[c] == 0: continue
for j in range(i + 1, len(boxes)):
boxj = boxes[j]
boxij = box_intersection(boxi, boxj)
boxja = boxj.w * boxj.h
apart = boxij / boxja
if apart >= .5:
if boxi.probs[c] > boxj.probs[c]:
boxes[j].probs[c] = 0.
else:
boxes[i].probs[c] = 0.
imgcv = cv2.imread(img_path)
h, w, _ = imgcv.shape
for b in boxes:
max_indx = np.argmax(b.probs)
max_prob = b.probs[max_indx]
label = 'object' * int(C < 2)
label += labels[max_indx] * int(C > 1)
if (max_prob > threshold):
left = int((b.x - b.w / 2.) * w)
right = int((b.x + b.w / 2.) * w)
top = int((b.y - b.h / 2.) * h)
bot = int((b.y + b.h / 2.) * h)
if left < 0: left = 0
if right > w - 1: right = w - 1
if top < 0: top = 0
if bot > h - 1: bot = h - 1
thick = int((h + w) / 300)
cv2.rectangle(imgcv, (left, top), (right, bot), colors[max_indx],
thick)
mess = '{}:{:.3f}'.format(label, max_prob)
cv2.putText(imgcv, mess, (left, top - 12), 0, 1e-3 * h,
colors[max_indx], thick / 5)
outfolder = os.path.join(FLAGS.test, 'out')
img_name = os.path.join(outfolder, img_path.split('/')[-1])
cv2.imwrite(img_name, imgcv)
def eval_list(boxes, self):
actuals = udacity_voc_csv(self.FLAGS.valAnn, self.meta['labels'])
names = list()
for box in actuals:
names.append(box[0])
imgName = list(OrderedDict.fromkeys(names))
conf_thresh = 0.25
nms_thresh = 0.4
iou_thresh = 0.5
min_box_scale = 8. / 448
total = 0.0
proposals = 0.0
correct = 0.0
lineId = 0
avg_iou = 0.0
groundBox = BoundBox(20)
prdiction = BoundBox(20)
for names in imgName:
for boxgt in actuals:
if (names[1:] == boxgt[0][1:]):
total = total + 1
best_iou = 0
for box in boxes:
if (box[0] == boxgt[0][1:]
and box[1][2][0][0] == boxgt[1][2][0][0]):
proposals = proposals + 1
box_gt = boxgt[1][2][0][1:5]
boxp = box[1][2][0][1:5]
groundBox.x = (box_gt[2] + box_gt[0]) / 2
groundBox.y = (box_gt[1] + box_gt[3]) / 2
groundBox.w = (box_gt[2] - box_gt[0])
groundBox.h = (box_gt[3] - box_gt[1])
prdiction.x = (boxp[2] + boxp[0]) / 2
prdiction.y = (boxp[1] + boxp[3]) / 2
prdiction.w = (boxp[2] - boxp[0])
prdiction.h = (boxp[3] - boxp[1])
iou = box_iou(groundBox, prdiction)
best_iou = max(iou, best_iou)
if best_iou > iou_thresh:
avg_iou += best_iou
correct = correct + 1
if (proposals == 0):
precision = 0
else:
precision = 1.0 * correct / proposals
recall = 1.0 * correct / total
if (correct == 0):
fscore = 0
IOU = 0
else:
fscore = 2.0 * precision * recall / (precision + recall)
IOU = avg_iou / correct
proposals = 0
total = 0
print(
"Image no:", names[1:],
"IOU: %f, Recal: %f, Precision: %f, Fscore: %f" %
(IOU, recall, precision, fscore))
def postprocess(self, net_out, im, save=True):
"""
Takes net output, draw predictions, save to disk
"""
meta, FLAGS = self.meta, self.FLAGS
threshold, sqrt = FLAGS.threshold, meta['sqrt'] + 1
C, B, S = meta['classes'], meta['num'], meta['side']
colors, labels = meta['colors'], meta['labels']
boxes = []
SS = S * S # number of grid cells
prob_size = SS * C # class probabilities
conf_size = SS * B # confidences for each grid cell
#net_out = net_out[0]
probs = net_out[0:prob_size]
confs = net_out[prob_size:(prob_size + conf_size)]
cords = net_out[(prob_size + conf_size):]
probs = probs.reshape([SS, C])
confs = confs.reshape([SS, B])
cords = cords.reshape([SS, B, 4])
for grid in range(SS):
for b in range(B):
bx = BoundBox(C)
bx.c = confs[grid, b]
bx.x = (cords[grid, b, 0] + grid % S) / S
bx.y = (cords[grid, b, 1] + grid // S) / S
bx.w = cords[grid, b, 2]**sqrt
bx.h = cords[grid, b, 3]**sqrt
p = probs[grid, :] * bx.c
p *= (p > threshold)
bx.probs = p
boxes.append(bx)
# non max suppress boxes
for c in range(C):
for i in range(len(boxes)):
boxes[i].class_num = c
boxes = sorted(boxes, key=prob_compare)
for i in range(len(boxes)):
boxi = boxes[i]
if boxi.probs[c] == 0: continue
for j in range(i + 1, len(boxes)):
boxj = boxes[j]
if box_iou(boxi, boxj) >= .4:
boxes[j].probs[c] = 0.
if type(im) is not np.ndarray:
imgcv = cv2.imread(im)
else:
imgcv = im
h, w, _ = imgcv.shape
for b in boxes:
max_indx = np.argmax(b.probs)
max_prob = b.probs[max_indx]
label = self.meta['labels'][max_indx]
if max_prob > _thresh.get(label, threshold):
left = int((b.x - b.w / 2.) * w)
right = int((b.x + b.w / 2.) * w)
top = int((b.y - b.h / 2.) * h)
bot = int((b.y + b.h / 2.) * h)
if left < 0: left = 0
if right > w - 1: right = w - 1
if top < 0: top = 0
if bot > h - 1: bot = h - 1
thick = int((h + w) // 150)
cv2.rectangle(imgcv, (left, top), (right, bot),
self.meta['colors'][max_indx], thick)
mess = '{}'.format(label)
cv2.putText(imgcv, mess, (left, top - 12), 0, 1e-3 * h,
self.meta['colors'][max_indx], thick // 3)
if not save: return imgcv
outfolder = os.path.join(FLAGS.test, 'out')
img_name = os.path.join(outfolder, im.split('/')[-1])
cv2.imwrite(img_name, imgcv)
def postprocess(self, net_out, im, save = True, check= False):
"""
Takes net output, draw net_out, save to disk
"""
# meta
meta = self.meta
H, W, _ = meta['out_size']
threshold = meta['thresh']
C, B = meta['classes'], meta['num']
anchors = meta['anchors']
net_out = net_out.reshape([H, W, B, -1])
boxes = list()
for row in range(H):
for col in range(W):
for b in range(B):
bx = BoundBox(C)
bx.x, bx.y, bx.w, bx.h, bx.c = net_out[row, col, b, :5]
bx.c = expit(bx.c)
bx.x = (col + expit(bx.x)) / W
bx.y = (row + expit(bx.y)) / H
bx.w = math.exp(bx.w) * anchors[2 * b + 0] / W
bx.h = math.exp(bx.h) * anchors[2 * b + 1] / H
classes = net_out[row, col, b, 5:]
bx.probs = _softmax(classes) * bx.c
bx.probs *= bx.probs > threshold
boxes.append(bx)
# non max suppress boxes
for c in range(C):
for i in range(len(boxes)):
boxes[i].class_num = c
boxes = sorted(boxes, key = prob_compare)
for i in range(len(boxes)):
boxi = boxes[i]
if boxi.probs[c] == 0: continue
for j in range(i + 1, len(boxes)):
boxj = boxes[j]
if box_iou(boxi, boxj) >= .4:
boxes[j].probs[c] = 0.
colors = meta['colors']
labels = meta['labels']
if type(im) is not np.ndarray:
imgcv = cv2.imread(im)
else: imgcv = im
h, w, _ = imgcv.shape
resultsForJSON = []
for b in boxes:
max_indx = np.argmax(b.probs)
max_prob = b.probs[max_indx]
label = 'object' * int(C < 2)
label += labels[max_indx] * int(C>1)
if max_prob > threshold:
left = int ((b.x - b.w/2.) * w)
right = int ((b.x + b.w/2.) * w)
top = int ((b.y - b.h/2.) * h)
bot = int ((b.y + b.h/2.) * h)
if left < 0 : left = 0
if right > w - 1: right = w - 1
if top < 0 : top = 0
if bot > h - 1: bot = h - 1
thick = int((h+w)/300)
mess = '{}'.format(label)
#print(("label", mess, "confidence", max_prob, "topleft ","x ", left, "y ", top, "bottomright", " x", right, "y", bot))
if self.FLAGS.json:
resultsForJSON.append({"label": mess, "confidence": float('%.2f' % max_prob), "topleft": {"x": left, "y": top}, "bottomright": {"x": right, "y": bot}})
continue
cv2.rectangle(imgcv,
(left, top), (right, bot),
colors[max_indx], thick)
cv2.putText(imgcv, mess, (left, top - 12),
0, 1e-3 * h, colors[max_indx],thick//3)
outfolder = os.path.join(self.FLAGS.test, 'out')
img_name = os.path.join(outfolder, im.split('/')[-1])
if(check==False):
if self.FLAGS.json:
textJSON = json.dumps(resultsForJSON)
textFile = os.path.splitext(img_name)[0] + ".json"
with open(textFile, 'w') as f:
f.write(textJSON)
return
if not save: return imgcv
cv2.imwrite(img_name, imgcv)