def handle(dirs, out_dir, clip):
start_time = datetime.datetime.now()
if not os.path.isdir(out_dir):
os.mkdir(out_dir)
files = getFiles(dirs)
total = len(files)
fail, success, skip, count = 0, 0, 0, 0
for f in files:
count += 1
print(count, '/', total)
img_dirs = os.path.join(out_dir, f.split("\\")[-1])
if os.path.isfile(img_dirs):
skip += 1
continue
try:
# 读取图片
img = cv2.imread(f, 0)
# 切边
x, w, y, h = clip
img = img[x:w, y:h]
# 去除噪点
img = moveNoise(img, 7)
# 根据最大熵算法获得最佳阈值
threshed = maxEntrop(img)
# 二值化
threshold, thrshed_img = cv2.threshold(img, threshed * 0.4, 255,
cv2.THRESH_BINARY)
# 使用区域生长法分割
img_segement, thresh_img = regionGrowing(img, thrshed_img)
# 保存
saveImage(img_dirs, "_new", img_segement)
# 打印信息到输出台
printToConsole(start_time, f, count, total, 5)
success += 1
except Exception as e:
# 错误情况
saveError(e, out_dir, f)
fail += 1
end_time = datetime.datetime.now()
expend = end_time - start_time
print(
"\n\ntotal: %d\nsuccessful: %d\nskip: %d\nfailed: %d\nExpend time: %s"
% (total, success, skip, fail, expend))
os.startfile(out_dir)
def handle(dirs, out_dir, clip):
start_time = datetime.datetime.now()
if not os.path.isdir(out_dir):
os.mkdir(out_dir)
files = getFiles(dirs)
total = len(files)
fail, success, skip, count = 0, 0, 0, 0
for f in files:
count += 1
print(count, '/', total)
img_dirs = os.path.join(out_dir, f.split("\\")[-1])
if os.path.isfile(img_dirs):
skip += 1
continue
try:
# 读取图片
img = cv2.imread(f, 0)
# 切边
x, w, y, h = clip
img = img[x:w, y:h]
# 去除噪点
img = moveNoise(img, 7)
# 获得图像像素均值
threshed = getMean(img)
# 二值化
threshold, thrshed_img = cv2.threshold(img, threshed * 0.86, 255,
cv2.THRESH_BINARY)
saveImage(img_dirs, "_threshed_raw", thrshed_img)
# 使用轮廓法(速度快)分割
img_segement, thresh_img = maxContour(img, thrshed_img)
saveImage(img_dirs, "_threshed", thresh_img)
# 去除多余边缘
img_remove_margin = moveMargin(img_segement, thresh_img)
saveImage(img_dirs, "_remove_margin", img_remove_margin)
# 扩充为正方形并缩小为256x256
img_new = normalization(img_remove_margin)
saveImage(img_dirs, "_new", img_new)
# 打印信息到输出台
printToConsole(start_time, f, count, total, 5)
success += 1
except Exception as e:
# 错误情况
saveError(e, out_dir, f)
fail += 1
end_time = datetime.datetime.now()
expend = end_time - start_time
print(
"\n\ntotal: %d\nsuccessful: %d\nskip: %d\nfailed: %d\nExpend time: %s"
% (total, success, skip, fail, expend))
os.startfile(out_dir)
def handle(dirs, out_dir, clip, w0):
start_time = datetime.datetime.now()
if not os.path.isdir(out_dir):
os.mkdir(out_dir)
files = getFiles(dirs)
total = len(files)
fail, success, skip, count = 0, 0, 0, 0
for f in files:
count += 1
print(count, '/', total)
img_dirs = os.path.join(out_dir, f.split("\\")[-1])
if os.path.isfile(img_dirs):
skip += 1
continue
try:
# 读取图片
img = cv2.imread(f, 0)
# 切边
x,w,y,h = clip
img = img[x:w , y:h]
# saveImage(img_dirs, "_original", img) # 保存原图
# 获得预测值阈值，二值化
thresh_value = getThreshValuebySoftmax(img, w0)
threshold, thrshed_img = cv2.threshold(img, thresh_value + 2, 255, cv2.THRESH_BINARY)
saveImage(img_dirs, "_threshed_raw", thrshed_img)
# 使用区域生长法分割
img_segement, thresh_img = regionGrowing(img, thrshed_img)
saveImage(img_dirs, "_threshed", thresh_img)
# 去除多余边缘
img_remove_margin = moveMargin(img_segement, thresh_img)
saveImage(img_dirs, "_remove_margin", img_remove_margin)
# 扩充为正方形并缩小为256x256
img_new = normalization(img_remove_margin)
saveImage(img_dirs, "_new", img_new)
# 打印信息到输出台
printToConsole(start_time, f, count, total, 5)
success += 1
except Exception as e:
# 错误情况
saveError(e, out_dir, f)
fail += 1
traceback.print_exc()
end_time = datetime.datetime.now()
expend = end_time - start_time
print("\n\ntotal: %d\nsuccessful: %d\nskip: %d\nfailed: %d\nExpend time: %s" %(total, success, skip, fail, expend))
os.startfile(out_dir)
def handle(dirs, out_dir):
start_time = datetime.datetime.now()
if not os.path.isdir(out_dir):
os.mkdir(out_dir)
files = getFiles(dirs)
total = len(files)
fail, success, skip, count = 0, 0, 0, 0
for f in files:
count += 1
print(count, '/', total)
img_dirs = os.path.join(out_dir, f.split("\\")[-1])
if os.path.isfile(img_dirs):
skip += 1
continue
try:
img = cv2.imread(f, 0)
# img_new = moveMargin(img, img)
img_new = normalization(img_new)
saveImage(img_dirs, "", img_new)
# 打印信息到输出台
printToConsole(start_time, f, count, total, 5)
success += 1
except Exception as e:
# 错误情况
saveError(e, out_dir, f)
fail += 1
end_time = datetime.datetime.now()
expend = end_time - start_time
print(
"\n\ntotal: %d\nsuccessful: %d\nskip: %d\nfailed: %d\nExpend time: %s"
% (total, success, skip, fail, expend))
os.startfile(out_dir)
def handle(dirs, out_dir, clip):
start_time = datetime.datetime.now()
if not os.path.isdir(out_dir):
os.mkdir(out_dir)
files = getFiles(dirs)
total = len(files)
fail, success, skip, count = 0, 0, 0, 0
for f in files[:20]:
count += 1
print(count, '/', total)
img_dirs = os.path.join(out_dir, f.split("\\")[-1])
if os.path.isfile(img_dirs):
skip += 1
continue
try:
# 读取图片
img = cv2.imread(f, 0)
# 切边
x,w,y,h = clip
img = img[x:w , y:h]
# 去除噪点
img = moveNoise(img, 7)
# 根据最大熵算法获得最佳阈值
threshed = getDBSCANvalue(img)
# 二值化
# thrshed_img = np.zeros(np.shape(img))
# img_m, img_n = np.shape(img)
# for i in range(img_m):
# for j in range(img_n):
# if img[i, j] >= threshed:
# thrshed_img[i, j] = 255
threshold, thrshed_img = cv2.threshold(img, threshed, 255, cv2.THRESH_BINARY)
# saveImage(img_dirs, "_threshed_raw", thrshed_img)
# 使用区域生长法分割
img_segement, thresh_img = regionGrowing(img, thrshed_img)
saveImage(img_dirs, "_threshed", thresh_img)
# 去除多余边缘
img_remove_margin = moveMargin(img_segement, thresh_img)
# saveImage(img_dirs, "_remove_margin", img_remove_margin)
# 扩充为正方形并缩小为256x256
img_new = normalization(img_remove_margin)
# saveImage(img_dirs, "_new", img_new)
# 打印信息到输出台
printToConsole(start_time, f, count, total, 5)
success += 1
except Exception as e:
# 错误情况
saveError(e, out_dir, f)
fail += 1
end_time = datetime.datetime.now()
expend = end_time - start_time
print("\n\ntotal: %d\nsuccessful: %d\nskip: %d\nfailed: %d\nExpend time: %s" %(total, success, skip, fail, expend))
os.startfile(out_dir)