def sample_hard_negatives(img, roi_mask, out_dir, img_id, abn,
patch_size=256, neg_cutoff=.35, nb_bkg=100,
start_sample_nb=0,
bkg_dir='background', verbose=False):
'''WARNING: the definition of hns may be problematic.
There has been study showing that the context of an ROI is also useful
for classification.
'''
bkg_out = os.path.join(out_dir, bkg_dir)
basename = '_'.join([img_id, str(abn)])
img = add_img_margins(img, patch_size/2)
roi_mask = add_img_margins(roi_mask, patch_size/2)
# Get ROI bounding box.
roi_mask_8u = roi_mask.astype('uint8')
ver = (cv2.__version__).split('.')
if int(ver[0]) < 3:
contours,_ = cv2.findContours(
roi_mask_8u.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
else:
_,contours,_ = cv2.findContours(
roi_mask_8u.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cont_areas = [ cv2.contourArea(cont) for cont in contours ]
idx = np.argmax(cont_areas) # find the largest contour.
rx,ry,rw,rh = cv2.boundingRect(contours[idx])
if verbose:
M = cv2.moments(contours[idx])
cx = int(M['m10']/M['m00'])
cy = int(M['m01']/M['m00'])
print "ROI centroid=", (cx,cy); sys.stdout.flush()
rng = np.random.RandomState(12345)
# Sample hard negative samples.
sampled_bkg = start_sample_nb
while sampled_bkg < start_sample_nb + nb_bkg:
x1,x2 = (rx - patch_size/2, rx + rw + patch_size/2)
y1,y2 = (ry - patch_size/2, ry + rh + patch_size/2)
x1 = crop_val(x1, patch_size/2, img.shape[1] - patch_size/2)
x2 = crop_val(x2, patch_size/2, img.shape[1] - patch_size/2)
y1 = crop_val(y1, patch_size/2, img.shape[0] - patch_size/2)
y2 = crop_val(y2, patch_size/2, img.shape[0] - patch_size/2)
x = rng.randint(x1, x2)
y = rng.randint(y1, y2)
if not overlap_patch_roi((x,y), patch_size, roi_mask, cutoff=neg_cutoff):
patch = img[y - patch_size/2:y + patch_size/2,
x - patch_size/2:x + patch_size/2]
patch_img = toimage(
patch.astype('int32'), high=patch.max(), low=patch.min(),
mode='I')
filename = basename + "_%04d" % (sampled_bkg) + ".png"
fullname = os.path.join(bkg_out, filename)
patch_img.save(fullname)
sampled_bkg += 1
if verbose:
print "sampled a hns patch at (x,y) center=", (x,y)
sys.stdout.flush()
def sample_hard_negatives(image, roi_mask, out_dir, image_id, abn_id,
patch_size=256, neg_cutoff=.35, nb_bkg=100,
start_sample_nb=0,
bkg_dir="background", verbose=False):
"""WARNING: the definition of hns may be problematic.
There has been study showing that the context of an ROI is also useful
for classification.
"""
bkg_out = os.path.join(out_dir, bkg_dir)
if not os.path.exists(bkg_out):
os.makedirs(bkg_out)
basename = "_".join([image_id, str(abn_id)])
image = add_img_margins(image, patch_size/2)
roi_mask = add_img_margins(roi_mask, patch_size/2)
# Get ROI bounding box.
roi_mask_8u = roi_mask.astype("uint8")
ver = (cv2.__version__).split(".")
contours, _ = cv2.findContours(roi_mask_8u.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cont_areas = [cv2.contourArea(cont) for cont in contours]
idx = np.argmax(cont_areas) # find the largest contour.
rx, ry, rw, rh = cv2.boundingRect(contours[idx])
if verbose:
M = cv2.moments(contours[idx])
cx = int(M["m10"]/M["m00"])
cy = int(M["m01"]/M["m00"])
print "ROI centroid=", (cx, cy)
sys.stdout.flush()
rng = np.random.RandomState(12345)
# Sample hard negative samples.
sampled_bkg = start_sample_nb
while sampled_bkg < start_sample_nb + nb_bkg:
x1, x2 = (rx - patch_size/2, rx + rw + patch_size/2)
y1, y2 = (ry - patch_size/2, ry + rh + patch_size/2)
x1 = crop_val(x1, patch_size/2, image.shape[1] - patch_size/2)
x2 = crop_val(x2, patch_size/2, image.shape[1] - patch_size/2)
y1 = crop_val(y1, patch_size/2, image.shape[0] - patch_size/2)
y2 = crop_val(y2, patch_size/2, image.shape[0] - patch_size/2)
x = rng.randint(x1, x2)
y = rng.randint(y1, y2)
if not overlap_patch_roi((x, y), patch_size, roi_mask, cutoff=neg_cutoff):
patch = image[y - patch_size/2:y + patch_size/2,
x - patch_size/2:x + patch_size/2]
patch = patch.astype("int32")
patch_image = toimage(patch, high=patch.max(), low=patch.min(),
mode="I")
filename = basename + "_%04d" % (sampled_bkg) + ".png"
fullname = os.path.join(bkg_out, filename)
patch_image.save(fullname)
sampled_bkg += 1
if verbose:
print "sampled a hns patch at (x,y) center=", (x, y)
sys.stdout.flush()
def sample_blob_negatives(img, roi_mask, out_dir, img_id, abn, blob_detector,
patch_size=256, neg_cutoff=.35, nb_bkg=100,
start_sample_nb=0,
bkg_dir='background', verbose=False):
bkg_out = os.path.join(out_dir, bkg_dir)
basename = '_'.join([img_id, str(abn)])
img = add_img_margins(img, patch_size/2)
roi_mask = add_img_margins(roi_mask, patch_size/2)
# Get ROI bounding box.获取ROI边界
roi_mask_8u = roi_mask.astype('uint8')
ver = (cv2.__version__).split('.')
if int(ver[0]) < 3:
contours,_ = cv2.findContours(
roi_mask_8u.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
else:
_,contours,_ = cv2.findContours(
roi_mask_8u.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cont_areas = [ cv2.contourArea(cont) for cont in contours ]
idx = np.argmax(cont_areas) # find the largest contour.
rx,ry,rw,rh = cv2.boundingRect(contours[idx])
if verbose:
M = cv2.moments(contours[idx])
cx = int(M['m10']/M['m00'])
cy = int(M['m01']/M['m00'])
print ("ROI centroid=", (cx,cy)); sys.stdout.flush()
# Sample blob negative samples.阴性斑点样本采样
# 采取特征点？
key_pts = blob_detector.detect((img/img.max()*255).astype('uint8'))
rng = np.random.RandomState(12345)
key_pts = rng.permutation(key_pts)
sampled_bkg = 0
for kp in key_pts:
if sampled_bkg >= nb_bkg:
break
x,y = int(kp.pt[0]), int(kp.pt[1])
if not overlap_patch_roi((x,y), patch_size, roi_mask, cutoff=neg_cutoff):
patch = img[y - patch_size/2:y + patch_size/2,
x - patch_size/2:x + patch_size/2]
patch_img = toimage(
patch.astype('int32'), high=patch.max(), low=patch.min(),
mode='I')
filename = basename + "_%04d" % (start_sample_nb + sampled_bkg) + ".png"
fullname = os.path.join(bkg_out, filename)
patch_img.save(fullname)
if verbose:
print ("sampled a blob patch at (x,y) center=", (x,y))
sys.stdout.flush()
sampled_bkg += 1
return sampled_bkg
def sample_blob_negatives(img, roi_mask, out_dir, img_id, abn, blob_detector,
patch_size=256, neg_cutoff=.35, nb_bkg=100,
start_sample_nb=0,
bkg_dir='background', verbose=False):
bkg_out = os.path.join(out_dir, bkg_dir)
basename = '_'.join([img_id, str(abn)])
img = add_img_margins(img, patch_size/2)
roi_mask = add_img_margins(roi_mask, patch_size/2)
# Get ROI bounding box.
roi_mask_8u = roi_mask.astype('uint8')
ver = (cv2.__version__).split('.')
if int(ver[0]) < 3:
contours,_ = cv2.findContours(
roi_mask_8u.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
else:
_,contours,_ = cv2.findContours(
roi_mask_8u.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cont_areas = [ cv2.contourArea(cont) for cont in contours ]
idx = np.argmax(cont_areas) # find the largest contour.
rx,ry,rw,rh = cv2.boundingRect(contours[idx])
if verbose:
M = cv2.moments(contours[idx])
cx = int(M['m10']/M['m00'])
cy = int(M['m01']/M['m00'])
print "ROI centroid=", (cx,cy); sys.stdout.flush()
# Sample blob negative samples.
key_pts = blob_detector.detect((img/img.max()*255).astype('uint8'))
rng = np.random.RandomState(12345)
key_pts = rng.permutation(key_pts)
sampled_bkg = 0
for kp in key_pts:
if sampled_bkg >= nb_bkg:
break
x,y = int(kp.pt[0]), int(kp.pt[1])
if not overlap_patch_roi((x,y), patch_size, roi_mask, cutoff=neg_cutoff):
patch = img[y - patch_size/2:y + patch_size/2,
x - patch_size/2:x + patch_size/2]
patch_img = toimage(
patch.astype('int32'), high=patch.max(), low=patch.min(),
mode='I')
filename = basename + "_%04d" % (start_sample_nb + sampled_bkg) + ".png"
fullname = os.path.join(bkg_out, filename)
patch_img.save(fullname)
if verbose:
print "sampled a blob patch at (x,y) center=", (x,y)
sys.stdout.flush()
sampled_bkg += 1
return sampled_bkg
def sample_blob_negatives(image, roi_mask, out_dir, image_id, abn_id, blob_detector,
patch_size=256, neg_cutoff=.35, nb_bkg=100,
start_sample_nb=0,
bkg_dir="background", verbose=False):
bkg_out = os.path.join(out_dir, bkg_dir)
if not os.path.exists(bkg_out):
os.makedirs(bkg_out)
basename = "_".join([image_id, str(abn_id)])
image = add_img_margins(image, patch_size/2)
roi_mask = add_img_margins(roi_mask, patch_size/2)
# Get ROI bounding box.
roi_mask_8u = roi_mask.astype("uint8")
ver = (cv2.__version__).split(".")
contours, _ = cv2.findContours(roi_mask_8u.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cont_areas = [cv2.contourArea(cont) for cont in contours]
idx = np.argmax(cont_areas) # find the largest contour.
rx, ry, rw, rh = cv2.boundingRect(contours[idx])
if verbose:
M = cv2.moments(contours[idx])
cx = int(M["m10"]/M["m00"])
cy = int(M["m01"]/M["m00"])
print "ROI centroid=", (cx, cy)
sys.stdout.flush()
# Sample blob negative samples.
key_pts = blob_detector.detect((image/image.max()*255).astype("uint8"))
rng = np.random.RandomState(12345)
key_pts = rng.permutation(key_pts)
sampled_bkg = 0
for kp in key_pts:
if sampled_bkg >= nb_bkg:
break
x, y = int(kp.pt[0]), int(kp.pt[1])
if not overlap_patch_roi((x, y), patch_size, roi_mask, cutoff=neg_cutoff):
patch = image[y - patch_size/2:y + patch_size/2,
x - patch_size/2:x + patch_size/2]
patch = patch.astype("int32")
patch_image = toimage(patch, high=patch.max(), low=patch.min(),
mode="I")
filename = basename + "_%04d" % (start_sample_nb + sampled_bkg) + ".png"
fullname = os.path.join(bkg_out, filename)
patch_image.save(fullname)
if verbose:
print "sampled a blob patch at (x,y) center=", (x, y)
sys.stdout.flush()
sampled_bkg += 1
return sampled_bkg
def sample_patches(img, roi_mask, out_dir, img_id, abn, pos, patch_size=256,
pos_cutoff=.75, neg_cutoff=.35,
nb_bkg=100, nb_abn=100, start_sample_nb=0,
bkg_dir='background', pos_dir='malignant', neg_dir='benign',
verbose=False):
if pos:
roi_out = os.path.join(out_dir, pos_dir)
else:
roi_out = os.path.join(out_dir, neg_dir)
bkg_out = os.path.join(out_dir, bkg_dir)
basename = '_'.join([img_id, str(abn)])
img = add_img_margins(img, patch_size/2)
roi_mask = add_img_margins(roi_mask, patch_size/2)
# Get ROI bounding box.
roi_mask_8u = roi_mask.astype('uint8')
ver = (cv2.__version__).split('.')
if int(ver[0]) < 3:
contours,_ = cv2.findContours(
roi_mask_8u.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
else:
_,contours,_ = cv2.findContours(
roi_mask_8u.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cont_areas = [ cv2.contourArea(cont) for cont in contours ]
idx = np.argmax(cont_areas) # find the largest contour.
rx,ry,rw,rh = cv2.boundingRect(contours[idx])
if verbose:
M = cv2.moments(contours[idx])
cx = int(M['m10']/M['m00'])
cy = int(M['m01']/M['m00'])
print "ROI centroid=", (cx,cy); sys.stdout.flush()
rng = np.random.RandomState(12345)
# Sample abnormality first.
sampled_abn = 0
nb_try = 0
while sampled_abn < nb_abn:
x = rng.randint(rx, rx + rw)
y = rng.randint(ry, ry + rh)
nb_try += 1
if nb_try >= 1000:
print "Nb of trials reached maximum, decrease overlap cutoff by 0.05"
sys.stdout.flush()
pos_cutoff -= .05
nb_try = 0
if pos_cutoff <= .0:
raise Exception("overlap cutoff becomes non-positive, "
"check roi mask input.")
# import pdb; pdb.set_trace()
if overlap_patch_roi((x,y), patch_size, roi_mask, cutoff=pos_cutoff):
patch = img[y - patch_size/2:y + patch_size/2,
x - patch_size/2:x + patch_size/2]
patch_img = toimage(
patch.astype('int32'), high=patch.max(), low=patch.min(),
mode='I')
# patch = patch.reshape((patch.shape[0], patch.shape[1], 1))
filename = basename + "_%04d" % (sampled_abn) + ".png"
fullname = os.path.join(roi_out, filename)
# import pdb; pdb.set_trace()
patch_img.save(fullname)
sampled_abn += 1
nb_try = 0
if verbose:
print "sampled an abn patch at (x,y) center=", (x,y)
sys.stdout.flush()
# Sample background.
sampled_bkg = start_sample_nb
while sampled_bkg < start_sample_nb + nb_bkg:
x = rng.randint(patch_size/2, img.shape[1] - patch_size/2)
y = rng.randint(patch_size/2, img.shape[0] - patch_size/2)
if not overlap_patch_roi((x,y), patch_size, roi_mask, cutoff=neg_cutoff):
patch = img[y - patch_size/2:y + patch_size/2,
x - patch_size/2:x + patch_size/2]
patch_img = toimage(
patch.astype('int32'), high=patch.max(), low=patch.min(),
mode='I')
filename = basename + "_%04d" % (sampled_bkg) + ".png"
fullname = os.path.join(bkg_out, filename)
patch_img.save(fullname)
sampled_bkg += 1
if verbose:
print "sampled a bkg patch at (x,y) center=", (x,y)
sys.stdout.flush()
def sample_patches(img, roi_mask, out_dir, img_id, abn, pos, patch_size=256,
pos_cutoff=.75, neg_cutoff=.35,
nb_bkg=100, nb_abn=100, start_sample_nb=0,
bkg_dir='background', pos_dir='malignant', neg_dir='benign',
verbose=False):
if pos:
#阳性，
roi_out = os.path.join(out_dir, pos_dir)
else:
#阴性
roi_out = os.path.join(out_dir, neg_dir)
bkg_out = os.path.join(out_dir, bkg_dir)
basename = '_'.join([img_id, str(abn)])
img = add_img_margins(img, patch_size/2)
roi_mask = add_img_margins(roi_mask, patch_size/2)
# Get ROI bounding box.
#获取边界框
roi_mask_8u = roi_mask.astype('uint8')
ver = (cv2.__version__).split('.')
if int(ver[0]) < 3:
#寻找图像中物体的轮廓（病变区域，不规则轮廓）返回向量集
contours,_ = cv2.findContours(
roi_mask_8u.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
else:
_,contours,_ = cv2.findContours(
roi_mask_8u.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
#计算轮廓面积
cont_areas = [ cv2.contourArea(cont) for cont in contours ]
idx = np.argmax(cont_areas) # find the largest contour.找到面积最大的轮廓索引值
#边界左上角坐标及宽高
#boundingRect，用一个最小的矩形，把找到的形状包起来
rx,ry,rw,rh = cv2.boundingRect(contours[idx])
if verbose:
# 将计算得到的矩以一个字典的形式返回至M
M = cv2.moments(contours[idx])
cx = int(M['m10']/M['m00'])
cy = int(M['m01']/M['m00'])
print ("ROI centroid=", (cx,cy)); sys.stdout.flush()
rng = np.random.RandomState(12345)
# Sample abnormality first.采样异常图片先进行
sampled_abn = 0
nb_try = 0
while sampled_abn < nb_abn:
x = rng.randint(rx, rx + rw)
y = rng.randint(ry, ry + rh)
nb_try += 1
if nb_try >= 1000:
# 试验的Nb达到最大值，重叠截止以0.05幅度降低
print ("Nb of trials reached maximum, decrease overlap cutoff by 0.05")
sys.stdout.flush()
pos_cutoff -= .05
nb_try = 0
if pos_cutoff <= .0:
# 重叠截止值达到了非阳性界限，检查感性区域掩码输入值
raise Exception("overlap cutoff becomes non-positive, "
"check roi mask input.")
# import pdb; pdb.set_trace()
if overlap_patch_roi((x,y), patch_size, roi_mask, cutoff=pos_cutoff):
patch = img[y - patch_size/2:y + patch_size/2,
x - patch_size/2:x + patch_size/2]
patch_img = toimage(
patch.astype('int32'), high=patch.max(), low=patch.min(),
mode='I')
# patch = patch.reshape((patch.shape[0], patch.shape[1], 1))
filename = basename + "_%04d" % (sampled_abn) + ".png"
fullname = os.path.join(roi_out, filename)
# import pdb; pdb.set_trace()
patch_img.save(fullname)
sampled_abn += 1 #异常样本数量记录
nb_try = 0
if verbose:
# 将异常样本的块坐标显示
print ("sampled an abn patch at (x,y) center=", (x,y))
sys.stdout.flush()
# Sample background.
sampled_bkg = start_sample_nb
while sampled_bkg < start_sample_nb + nb_bkg:
x = rng.randint(patch_size/2, img.shape[1] - patch_size/2)
y = rng.randint(patch_size/2, img.shape[0] - patch_size/2)
if not overlap_patch_roi((x,y), patch_size, roi_mask, cutoff=neg_cutoff):
patch = img[y - patch_size/2:y + patch_size/2,
x - patch_size/2:x + patch_size/2]
patch_img = toimage(
patch.astype('int32'), high=patch.max(), low=patch.min(),
mode='I')
filename = basename + "_%04d" % (sampled_bkg) + ".png"
fullname = os.path.join(bkg_out, filename)
patch_img.save(fullname)
sampled_bkg += 1
if verbose:
# 将样本背景中心块坐标显示
print ("sampled a bkg patch at (x,y) center=", (x,y))
sys.stdout.flush()
def sample_hard_negatives(img, roi_mask, out_dir, img_id, abn,
patch_size=256, neg_cutoff=.35, nb_bkg=100,
start_sample_nb=0,
bkg_dir='background', verbose=False):
'''WARNING: the definition of hns may be problematic.
There has been study showing that the context of an ROI is also useful
for classification.
'''
# 添加背景目录并命名
bkg_out = os.path.join(out_dir, bkg_dir)
basename = '_'.join([img_id, str(abn)])
# 图像加0边距，掩膜同样处理
img = add_img_margins(img, patch_size/2)
roi_mask = add_img_margins(roi_mask, patch_size/2)
# Get ROI bounding box.
roi_mask_8u = roi_mask.astype('uint8')
ver = (cv2.__version__).split('.')
if int(ver[0]) < 3:
contours,_ = cv2.findContours(
roi_mask_8u.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
else:
_,contours,_ = cv2.findContours(
roi_mask_8u.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cont_areas = [ cv2.contourArea(cont) for cont in contours ]
idx = np.argmax(cont_areas) # find the largest contour.
rx,ry,rw,rh = cv2.boundingRect(contours[idx])
if verbose:
M = cv2.moments(contours[idx])
cx = int(M['m10']/M['m00'])
cy = int(M['m01']/M['m00'])
print ("ROI centroid=", (cx,cy)); sys.stdout.flush()
rng = np.random.RandomState(12345)
# Sample hard negative samples.难类样本采样
sampled_bkg = start_sample_nb
while sampled_bkg < start_sample_nb + nb_bkg:
# 扩大感兴趣区域，取不小于patch_size/2,不超过img.shape - patch_size/2的x,y的随机值
x1,x2 = (rx - patch_size/2, rx + rw + patch_size/2)
y1,y2 = (ry - patch_size/2, ry + rh + patch_size/2)
x1 = crop_val(x1, patch_size/2, img.shape[1] - patch_size/2)
x2 = crop_val(x2, patch_size/2, img.shape[1] - patch_size/2)
y1 = crop_val(y1, patch_size/2, img.shape[0] - patch_size/2)
y2 = crop_val(y2, patch_size/2, img.shape[0] - patch_size/2)
x = rng.randint(x1, x2)
y = rng.randint(y1, y2)
# 如果patch与掩码不重叠则执行
if not overlap_patch_roi((x,y), patch_size, roi_mask, cutoff=neg_cutoff):
# 选取图像中patch
patch = img[y - patch_size/2:y + patch_size/2,
x - patch_size/2:x + patch_size/2]
# patch由数组转为图像，并保存至背景
patch_img = toimage(
patch.astype('int32'), high=patch.max(), low=patch.min(),
mode='I')
filename = basename + "_%04d" % (sampled_bkg) + ".png"
fullname = os.path.join(bkg_out, filename)
patch_img.save(fullname)
sampled_bkg += 1
if verbose:
print ("sampled a hns patch at (x,y) center=", (x,y))
sys.stdout.flush()
def sample_patches(img,
roi_mask,
out_dir,
img_id,
abn,
pos,
patch_size=256,
pos_cutoff=.75,
neg_cutoff=.35,
nb_bkg=100,
nb_abn=100,
start_sample_nb=0,
itype='calc',
bkg_dir='background',
calc_pos_dir='calc_mal',
calc_neg_dir='calc_ben',
mass_pos_dir='mass_mal',
mass_neg_dir='mass_ben',
verbose=False):
if pos:
if itype == 'calc':
roi_out = os.path.join(out_dir, calc_pos_dir)
else:
roi_out = os.path.join(out_dir, mass_pos_dir)
else:
if itype == 'calc':
roi_out = os.path.join(out_dir, calc_neg_dir)
else:
roi_out = os.path.join(out_dir, mass_neg_dir)
bkg_out = os.path.join(out_dir, bkg_dir)
basename = '_'.join([img_id, str(abn)])
img = add_img_margins(img, patch_size / 2)
roi_mask = add_img_margins(roi_mask, patch_size / 2)
# Get ROI bounding box.
roi_mask_8u = roi_mask.astype('uint8')
ver = (cv2.__version__).split('.')
if int(ver[0]) < 3:
contours, _ = cv2.findContours(roi_mask_8u.copy(), cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
else:
_, contours, _ = cv2.findContours(roi_mask_8u.copy(), cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
cont_areas = [cv2.contourArea(cont) for cont in contours]
idx = np.argmax(cont_areas) # find the largest contour.
rx, ry, rw, rh = cv2.boundingRect(contours[idx])
if verbose:
M = cv2.moments(contours[idx])
try:
cx = int(M['m10'] / M['m00'])
cy = int(M['m01'] / M['m00'])
print "ROI centroid=", (cx, cy)
sys.stdout.flush()
except ZeroDivisionError:
print "ROI centroid=Unknown"
sys.stdout.flush()
rng = np.random.RandomState(12345)
# Sample abnormality first.
sampled_abn = 0
nb_try = 0
while sampled_abn < nb_abn:
x = rng.randint(rx, rx + rw)
y = rng.randint(ry, ry + rh)
nb_try += 1
if nb_try >= 1000:
print "Nb of trials reached maximum, decrease overlap cutoff by 0.05"
sys.stdout.flush()
pos_cutoff -= .05
nb_try = 0
if pos_cutoff <= .0:
raise Exception("overlap cutoff becomes non-positive, "
"check roi mask input.")
# import pdb; pdb.set_trace()
if overlap_patch_roi((x, y), patch_size, roi_mask, cutoff=pos_cutoff):
patch = img[y - patch_size / 2:y + patch_size / 2,
x - patch_size / 2:x + patch_size / 2]
patch = patch.astype('int32')
patch_img = toimage(patch,
high=patch.max(),
low=patch.min(),
mode='I')
# patch = patch.reshape((patch.shape[0], patch.shape[1], 1))
filename = basename + "_%04d" % (sampled_abn) + ".png"
fullname = os.path.join(roi_out, filename)
# import pdb; pdb.set_trace()
patch_img.save(fullname)
sampled_abn += 1
nb_try = 0
if verbose:
print "sampled an abn patch at (x,y) center=", (x, y)
sys.stdout.flush()
# Sample background.
sampled_bkg = start_sample_nb
while sampled_bkg < start_sample_nb + nb_bkg:
x = rng.randint(patch_size / 2, img.shape[1] - patch_size / 2)
y = rng.randint(patch_size / 2, img.shape[0] - patch_size / 2)
if not overlap_patch_roi(
(x, y), patch_size, roi_mask, cutoff=neg_cutoff):
patch = img[y - patch_size / 2:y + patch_size / 2,
x - patch_size / 2:x + patch_size / 2]
patch = patch.astype('int32')
patch_img = toimage(patch,
high=patch.max(),
low=patch.min(),
mode='I')
filename = basename + "_%04d" % (sampled_bkg) + ".png"
fullname = os.path.join(bkg_out, filename)
patch_img.save(fullname)
sampled_bkg += 1
if verbose:
print "sampled a bkg patch at (x,y) center=", (x, y)
sys.stdout.flush()
def score_write_patches(img_list,
lab_list,
target_height,
target_scale,
patch_size,
stride,
model,
batch_size,
neg_out,
pos_out,
bkg_out,
preprocess=None,
equalize_hist=False,
featurewise_center=False,
featurewise_mean=91.6,
roi_cutoff=.9,
bkg_cutoff=[.5, 1.],
sample_bkg=True,
img_ext='png',
random_seed=12345,
parallelized=False):
'''Score image patches and write them to an external directory
'''
def write_patches(img_fn, patch_dat, idx, out_dir, img_ext='png'):
basename = os.path.basename(img_fn)
fn_no_ext = os.path.splitext(basename)[0]
if img_ext == 'png':
max_val = 65535.
else:
max_val = 255.
for i in idx:
patch = patch_dat[i]
patch_max = patch.max() if patch.max() != 0 else max_val
patch *= max_val / patch_max
patch = patch.astype('int32')
mode = 'I' if img_ext == 'png' else None
patch_img = toimage(patch,
high=patch.max(),
low=patch.min(),
mode=mode)
filename = fn_no_ext + "_%06d" % (i) + '.' + img_ext
fullname = os.path.join(out_dir, filename)
patch_img.save(fullname)
rng = RandomState(random_seed)
nb_roi = 0
nb_bkg = 0
for img_fn, img_lab in zip(img_list, lab_list):
img = read_resize_img(img_fn, target_height=target_height)
img, _ = prep.segment_breast(img)
img = add_img_margins(img, patch_size / 2)
patch_dat, nb_row, nb_col = sweep_img_patches(
img,
patch_size,
stride,
target_scale=target_scale,
equalize_hist=equalize_hist)
org_patch_dat = patch_dat.copy()
if parallelized and len(patch_dat) % 2 == 1:
last_patch = patch_dat[-1:, :, :]
patch_dat = np.append(patch_dat, last_patch, axis=0)
appended = True
else:
appended = False
if dim_ordering == 'th':
patch_X = np.zeros((patch_dat.shape[0], 3, patch_dat.shape[1],
patch_dat.shape[2]),
dtype='float64')
patch_X[:, 0, :, :] = patch_dat
patch_X[:, 1, :, :] = patch_dat
patch_X[:, 2, :, :] = patch_dat
else:
patch_X = np.zeros((patch_dat.shape[0], patch_dat.shape[1],
patch_dat.shape[2], 3),
dtype='float64')
patch_X[:, :, :, 0] = patch_dat
patch_X[:, :, :, 1] = patch_dat
patch_X[:, :, :, 2] = patch_dat
if featurewise_center:
patch_X -= featurewise_mean
elif preprocess is not None:
patch_X = preprocess(patch_X)
pred = model.predict(patch_X, batch_size=batch_size)
# import pdb; pdb.set_trace()
if appended:
pred = pred[:-1]
roi_idx = np.where(pred[:, 0] < 1 - roi_cutoff)[0]
bkg_idx = np.where(
np.logical_and(pred[:, 0] > bkg_cutoff[0],
pred[:, 0] <= bkg_cutoff[1]))[0]
if sample_bkg and len(bkg_idx) > len(roi_idx):
bkg_idx = rng.choice(bkg_idx, len(roi_idx), replace=False)
roi_out = pos_out if img_lab == 1 else neg_out
write_patches(img_fn, org_patch_dat, roi_idx, roi_out, img_ext)
write_patches(img_fn, org_patch_dat, bkg_idx, bkg_out, img_ext)
nb_roi += len(roi_idx)
nb_bkg += len(bkg_idx)
return nb_roi, nb_bkg
def sample_patches(img,
roi_mask,
out_dir,
img_id,
abn,
pos,
patch_size=256,
pos_cutoff=.75,
neg_cutoff=.35,
nb_bkg=100,
nb_abn=100,
start_sample_nb=0,
itype='calc',
bkg_dir='background',
calc_pos_dir='calc_mal',
calc_neg_dir='calc_ben',
mass_pos_dir='mass_mal',
mass_neg_dir='mass_ben',
verbose=False):
#图像如果为阳性
if pos:
#是钙化点
if itype == 'calc':
#图像放入输出目录中钙化阳性目录
roi_out = os.path.join(out_dir, calc_pos_dir)
else:
#否则放入输出目录中肿块阳性目录
roi_out = os.path.join(out_dir, mass_pos_dir)
#图像如果为阴性
else:
#是钙化点
if itype == 'calc':
#图像放入输出目录中钙化阴性目录
roi_out = os.path.join(out_dir, calc_neg_dir)
else:
#否则放入输出目录中肿块阴性目录
roi_out = os.path.join(out_dir, mass_neg_dir)
#背景放到输出目录中背景目录下
bkg_out = os.path.join(out_dir, bkg_dir)
#命名
basename = '_'.join([img_id, str(abn)])
#图像增加边距
img = add_img_margins(img, patch_size / 2)
#掩码增加边距
roi_mask = add_img_margins(roi_mask, patch_size / 2)
# Get ROI bounding box.
#获取感兴趣区域边界
#掩码转为8位无符号整型
roi_mask_8u = roi_mask.astype('uint8')
#获取轮廓contours
ver = (cv2.__version__).split('.')
if int(ver[0]) < 3:
contours, _ = cv2.findContours(roi_mask_8u.copy(), cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
else:
_, contours, _ = cv2.findContours(roi_mask_8u.copy(), cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
#获取轮廓面积
cont_areas = [cv2.contourArea(cont) for cont in contours]
#返回轮廓面积的索引值
idx = np.argmax(cont_areas) # find the largest contour.
#获取轮廓左上角点的坐标以及宽高
rx, ry, rw, rh = cv2.boundingRect(contours[idx])
#显示详细信息为true
if verbose:
#将计算得到的矩以一个字典的形式返回至M
M = cv2.moments(contours[idx])
try:
cx = int(M['m10'] / M['m00'])
cy = int(M['m01'] / M['m00'])
print("ROI centroid=", (cx, cy))
sys.stdout.flush()
except ZeroDivisionError:
cx = rx + int(rw / 2)
cy = ry + int(rh / 2)
print("ROI centroid=Unknown, use b-box center=", (cx, cy))
sys.stdout.flush()
#实现的随机数生成通常为伪随机数生成器，为了使得具备随机性的代码最终的结果可复现，需要设置相同的种子值
rng = np.random.RandomState(12345)
# Sample abnormality first.采样异常图片先进行
sampled_abn = 0
nb_try = 0
while sampled_abn < nb_abn:
if nb_abn > 1: #randint用于产生基质的均匀分布的随机整数
x = rng.randint(rx, rx + rw)
y = rng.randint(ry, ry + rh)
nb_try += 1
if nb_try >= 1000:
# 试验的Nb达到最大值，重叠截止以0.05幅度降低
print(
"Nb of trials reached maximum, decrease overlap cutoff by 0.05"
)
sys.stdout.flush()
pos_cutoff -= .05
nb_try = 0
if pos_cutoff <= .0:
# 重叠截止值达到了非阳性界限，检查感性区域掩码输入值
raise Exception("overlap cutoff becomes non-positive, "
"check roi mask input.")
else:
x = cx
y = cy
# import pdb; pdb.set_trace()
if nb_abn == 1 or overlap_patch_roi(
(x, y), patch_size, roi_mask, cutoff=pos_cutoff):
patch = img[y - patch_size / 2:y + patch_size / 2,
x - patch_size / 2:x + patch_size / 2]
patch = patch.astype('int32')
#max返回最大值，最小值
patch_img = toimage(patch,
high=patch.max(),
low=patch.min(),
mode='I')
# patch = patch.reshape((patch.shape[0], patch.shape[1], 1))
filename = basename + "_%04d" % (sampled_abn) + ".png"
fullname = os.path.join(roi_out, filename)
# import pdb; pdb.set_trace()
patch_img.save(fullname)
sampled_abn += 1 #异常样本数量记录
nb_try = 0
if verbose:
#将异常样本的块坐标显示
print("sampled an abn patch at (x,y) center=", (x, y))
sys.stdout.flush()
# Sample background.
sampled_bkg = start_sample_nb
while sampled_bkg < start_sample_nb + nb_bkg:
x = rng.randint(patch_size / 2, img.shape[1] - patch_size / 2)
y = rng.randint(patch_size / 2, img.shape[0] - patch_size / 2)
if not overlap_patch_roi(
(x, y), patch_size, roi_mask, cutoff=neg_cutoff):
patch = img[y - patch_size / 2:y + patch_size / 2,
x - patch_size / 2:x + patch_size / 2]
patch = patch.astype('int32')
patch_img = toimage(patch,
high=patch.max(),
low=patch.min(),
mode='I')
filename = basename + "_%04d" % (sampled_bkg) + ".png"
fullname = os.path.join(bkg_out, filename)
patch_img.save(fullname)
sampled_bkg += 1
if verbose:
#将样本背景中心块坐标显示
print("sampled a bkg patch at (x,y) center=", (x, y))
sys.stdout.flush()
def sample_patches(image,
roi_mask,
out_dir,
image_id,
abn_id,
pos,
patch_size=256,
pos_cutoff=.75,
neg_cutoff=.35,
nb_bkg=100,
nb_abn=100,
start_sample_nb=0,
abn_type="calcification",
bkg_dir="background",
calc_pos_dir="calc_mal",
calc_neg_dir="calc_ben",
mass_pos_dir="mass_mal",
mass_neg_dir="mass_ben",
verbose=False):
if pos:
if abn_type == "calcification":
roi_out = os.path.join(out_dir, calc_pos_dir)
else:
roi_out = os.path.join(out_dir, mass_pos_dir)
else:
if abn_type == "calcification":
roi_out = os.path.join(out_dir, calc_neg_dir)
else:
roi_out = os.path.join(out_dir, mass_neg_dir)
bkg_out = os.path.join(out_dir, bkg_dir)
if not os.path.exists(roi_out):
os.mkdir(roi_out)
if not os.path.exists(bkg_out):
os.mkdir(bkg_out)
base_name = "_".join([image_id, str(abn_id)])
image = add_img_margins(image, patch_size / 2)
roi_mask = add_img_margins(roi_mask, patch_size / 2)
# Get ROI bounding box.
roi_mask_8u = roi_mask.astype("uint8")
contours, _ = cv2.findContours(roi_mask_8u.copy(), cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
cont_areas = [cv2.contourArea(cont) for cont in contours]
idx = np.argmax(cont_areas) # find the largest contour.
rx, ry, rw, rh = cv2.boundingRect(contours[idx])
if verbose:
M = cv2.moments(contours[idx])
try:
cx = int(M["m10"] / M["m00"])
cy = int(M["m01"] / M["m00"])
print "ROI centroid=", (cx, cy)
sys.stdout.flush()
except ZeroDivisionError:
cx = rx + int(rw / 2)
cy = ry + int(rh / 2)
print "ROI centroid=Unknown, use b-box center=", (cx, cy)
sys.stdout.flush()
rng = np.random.RandomState(12345)
# Sample abnormality first.
sampled_abn = 0
nb_try = 0
while sampled_abn < nb_abn:
file_name = base_name + "_%04d" % (sampled_abn) + ".png"
full_path = os.path.join(roi_out, file_name)
if os.path.exists(full_path):
print "already exists:", full_path
sampled_abn += 1
continue
if nb_abn > 1:
x = rng.randint(rx, rx + rw)
y = rng.randint(ry, ry + rh)
nb_try += 1
if nb_try >= 1000:
print "Nb of trials reached maximum, decrease overlap cutoff by 0.05"
sys.stdout.flush()
pos_cutoff -= .05
nb_try = 0
if pos_cutoff <= .0:
raise Exception("overlap cutoff becomes non-positive, "
"check roi mask input.")
else:
x = cx
y = cy
# import pdb; pdb.set_trace()
if nb_abn == 1 or overlap_patch_roi(
(x, y), patch_size, roi_mask, cutoff=pos_cutoff):
patch = image[y - patch_size / 2:y + patch_size / 2,
x - patch_size / 2:x + patch_size / 2]
patch = patch.astype("int32")
patch_image = toimage(patch,
high=patch.max(),
low=patch.min(),
mode="I")
# patch = patch.reshape((patch.shape[0], patch.shape[1], 1))
# import pdb; pdb.set_trace()
patch_image.save(full_path)
sampled_abn += 1
nb_try = 0
if verbose:
print "sampled an", abn_id, "patch at (x,y) center=", (x, y)
sys.stdout.flush()
# Sample background.
sampled_bkg = start_sample_nb
while sampled_bkg < start_sample_nb + nb_bkg:
file_name = base_name + "_%04d" % (sampled_bkg) + ".png"
full_path = os.path.join(bkg_out, file_name)
if os.path.exists(full_path):
print "already exists:", full_path
sampled_bkg += 1
continue
x = rng.randint(patch_size / 2, image.shape[1] - patch_size / 2)
y = rng.randint(patch_size / 2, image.shape[0] - patch_size / 2)
if not overlap_patch_roi(
(x, y), patch_size, roi_mask, cutoff=neg_cutoff):
patch = image[y - patch_size / 2:y + patch_size / 2,
x - patch_size / 2:x + patch_size / 2]
patch = patch.astype("int32")
patch_image = toimage(patch,
high=patch.max(),
low=patch.min(),
mode="I")
patch_image.save(full_path)
sampled_bkg += 1
if verbose:
print "sampled a bkg patch at (x,y) center=", (x, y)
sys.stdout.flush()
