def split_parts(pool, preproces_dir, g_conf):
target_dir = g_conf["target_dir"]
original_dir = g_conf["original_dir"]
error_dir = g_conf["error_dir"]
start_y = g_conf["start_y"]
collectData = g_conf["collectData"]
splitMode = g_conf["splitMode"]
preproces_files = get_image_paths_from_dir(preproces_dir, 'png')
futures = []
for preproces_path in preproces_files:
out_dir, dir_name = get_out_dir_name(preproces_path, target_dir)
futures.append(
pool.apply_async(split_parts_for_image,
(start_y, preproces_path, out_dir, dir_name,
original_dir, collectData, splitMode)))
for future in futures:
name, success = future.get()
copyName = get_jpeg_name_for_png(get_specified_dir(original_dir, name))
if success:
print("分解成功: ", copyName)
else:
distName = get_specified_dir(error_dir,
get_jpeg_name_for_png(name))
copy_move_file(copyName, distName)
print("分解失败: ", copyName)
def find(img, path, target):
img_small = resize_to_resolution(img, 1024)
small_h, w = img_small.shape[:2]
im = cv2.GaussianBlur(img_small, (1, 1), 0)
gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(gray, *(20, 30))
edges = cv2.dilate(edges, None)
edges = cv2.erode(edges, None)
(_, thresh) = cv2.threshold(edges, 127, 255, cv2.THRESH_BINARY)
cimg, cnts, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
# cv2.imshow("1",cimg)
# cv2.waitKey(0)
valid = []
for cnt in cnts:
x, y, w, h = cv2.boundingRect(cnt)
if h == small_h:
valid.append(x)
if len(valid) >= 2:
# print('检索出更多的图',path)
new_file_name = get_specified_dir(target, get_file_name(path))
cv2.imwrite(new_file_name, img_small)
return img_small
def preprocessImage(pool, image_files, g_conf):
futures = []
temp_dir = g_conf["temp_dir"]
start_y = g_conf["start_y"]
end_y = g_conf["end_y"]
resize = g_conf["resize"]
preproces_dir = create_specified_dir(temp_dir, "1_preprocess")
for path in image_files:
futures.append(
pool.apply_async(preprocess_image,
(path, temp_dir, start_y, end_y, resize)))
imageObjs = []
for future in futures:
img, path = future.get()
imageObjs.append(img)
new_file_name = get_specified_dir(preproces_dir,
get_png_name_for_jpeg(path))
cv2.imwrite(new_file_name, img)
return preproces_dir
def get_mask_file_name(img_file):
data_dir = get_data_dir(img_file)
masks_dir = get_specified_dir(data_dir, "2_masks")
png_name = get_png_name_for_jpeg(img_file)
return os.path.join(masks_dir, png_name)
def legao_main(original_dir="",
target_dir="",
interval="",
error_dir="",
start_y="",
collectData="",
splitMode="",
resize="",
end_y=""):
if original_dir == "":
print("必须传递原图目录 original_dir")
return
if target_dir == "":
print("必须传递保存目录 target_dir")
return
if start_y == "":
print("必须设置截图的Y轴上部距离(px) start_y")
return
if end_y == "":
print("必须设置截图的Y轴下部距离(px) end_y")
return
# 临时保存目录
temp_dir = get_specified_dir(original_dir, "temp")
error_dir = error_dir or get_specified_dir(original_dir, "error")
# 根目录处理
asscess_dir(target_dir)
asscess_dir(temp_dir)
asscess_dir(error_dir)
original_image_total_files = get_image_paths_from_dir(original_dir, 'jpg')
g_conf = {
"collectData": collectData,
"splitMode": splitMode,
"interval": interval,
"resize": resize,
"start_y": start_y,
"end_y": end_y,
"original_dir": original_dir,
"target_dir": target_dir,
"temp_dir": temp_dir,
"error_dir": error_dir
}
# 赋全局值
for prop in g_conf:
if not g_conf[prop]:
g_conf[prop] = config[prop]
modeName = "全部保留"
if splitMode == "full":
modeName = "仅保留完整的零件"
message = [
["原图目录: ", original_dir],
["临时目录: ", temp_dir],
["错误目录: ", error_dir],
["完成目录: ", target_dir],
["原图截取从Y: ", start_y],
["原图截止至Y: ", end_y],
["分解量: ", interval],
["切割模式: ", modeName],
]
for name in message:
print(name[0], name[1], sep="")
#开始任务
exec_process_image(0, original_image_total_files, g_conf)
def split_parts_for_image(start_y, preproces_path, out_dir, dir_name,
original_dir, collectData, splitMode):
try:
# 第三步图片
segmented_img = cv2.imread(get_seg_file_name(preproces_path))
# 原始图
original_img = cv2.imread(
get_specified_dir(original_dir, dir_name + ".jpg"))
# 原图宽度
original_width = original_img.shape[1]
original_resize = original_width / 1000
width = segmented_img.shape[1]
height = segmented_img.shape[0]
#定义区域
min_area = 50
max_area = width * height
# BGR=>灰度图
mask = cv2.cvtColor((segmented_img != 0).astype(np.uint8),
cv2.COLOR_BGR2GRAY)
# 部件图
partImages = []
# 边缘部件
part_remove_left = 0
part_remove_right = 0
while True:
# 将mask转化为1维数组
# 返回数组mask中值不为零的元素的下标,
nz = np.nonzero(mask.flatten())[0].flatten()
if len(nz) == 0:
break
nz_i = 0
found_mask = None
found_image = None
while True:
index = nz[nz_i]
seed_x = index % width
# 向下取整
seed_y = index // width
ff_mask = np.zeros((height + 2, width + 2), dtype=np.uint8)
area, _, __, rect = cv2.floodFill(mask,
ff_mask, (seed_x, seed_y),
255,
flags=cv2.FLOODFILL_MASK_ONLY
| cv2.FLOODFILL_FIXED_RANGE)
x = rect[0]
y = rect[1]
w = rect[2]
h = rect[3]
# slicing into found rect
roi_mask = ff_mask[y + 1:y + 1 + h, x + 1:x + 1 + w]
found = False
if min_area < area < max_area:
found_mask = roi_mask
newX = x * 4
newY = (y * 4) + start_y
newW = w * 4
newH = h * 4
# 边界模式，跳出循环
if splitMode == "full":
if newX <= 0:
found_mask = None
part_remove_left += 1
if newW + newX >= original_width:
found_mask = None
part_remove_right += 1
startX = newX - 30
endX = newX + newW + 30
startY = newY - 30
endY = newY + newH + 30
found_image = original_img[startY:endY, startX:endX].copy()
found = True
# clearing found component in the mask
mask[y:y + h, x:x + w][roi_mask != 0] = 0
if found:
break
nz_i += 1
if nz_i >= len(nz):
break
if found_mask is not None:
partImages.append(found_image)
# 如果有多个零件，创建目录保存
# hasmorepart = len(partImages) > 1
# if hasmorepart:
out_dir = get_specified_dir(out_dir, dir_name)
if os.path.exists(out_dir):
clear_dir(out_dir)
else:
create_dir(out_dir)
# 分割信息
if part_remove_left or part_remove_right:
fl = open(out_dir + "/data.json", 'w')
r_data = {
"left_remove": part_remove_left,
"right_remove": part_remove_right
}
fl.write(json.dumps(r_data, ensure_ascii=False, indent=2))
fl.close()
# 输出第二部分
part_index = 0
for part in partImages:
title = os.path.splitext(os.path.split(preproces_path)[1])[0]
file_name = os.path.join("", "%s_%02d.png" % (title, part_index))
# if hasmorepart:
out_file = os.path.join(out_dir,
"%s_%02d.png" % (title, part_index))
# else:
# out_file = os.path.join(out_dir, "%s.png" % (title))
cv2.imwrite(out_file, part)
# 支持数据采集
if collectData:
color_thief = ColorThief(out_file)
dominant_color = color_thief.get_color(quality=1)
h, w = part.shape[:2]
datas = {
"name": file_name,
"w": w,
"h": h,
"area": w * h,
"rgb": dominant_color
}
filePath = out_dir + "/data.json"
if (os.path.exists(filePath)):
fl = open(filePath, 'a')
else:
fl = open(filePath, 'w')
fl.write(json.dumps(datas, ensure_ascii=False, indent=2))
fl.close()
part_index += 1
return dir_name + ".png", True
except Exception as _:
return dir_name + ".png", False
def get_out_dir_name(img_file, target_dir):
out_dir = get_specified_dir(target_dir, "")
dir_name = os.path.splitext(os.path.split(img_file)[1])[0]
return out_dir, dir_name
def get_seg_file_name(img_file):
data_dir = get_data_dir(img_file)
seg_dir = get_specified_dir(data_dir, "3_segmentation")
png_name = get_png_name_for_jpeg(img_file)
return os.path.join(seg_dir, png_name)
def split_parts_for_image(start_y, preproces_path, out_dir, dir_name,
original_dir, collectData, splitMode):
try:
# 第三步图片
segmented_img = cv2.imread(get_seg_file_name(preproces_path))
# 原始图
original_img = cv2.imread(
get_specified_dir(original_dir, dir_name + ".jpg"))
# 原图宽度
original_width = original_img.shape[1]
original_resize = original_width / 1000
width = segmented_img.shape[1]
height = segmented_img.shape[0]
#定义区域
min_area = 50
max_area = width * height
# BGR=>灰度图
mask = cv2.cvtColor((segmented_img != 0).astype(np.uint8),
cv2.COLOR_BGR2GRAY)
# 部件图
partImages = []
# 边缘部件
part_remove_left = 0
part_remove_right = 0
#参考点标签的坐标合计
labelPoint = []
newimage = original_img.copy()
while True:
# 将mask转化为1维数组
# 返回数组mask中值不为零的元素的下标,
nz = np.nonzero(mask.flatten())[0].flatten()
if len(nz) == 0:
break
nz_i = 0
found_mask = None
found_image = None
while True:
index = nz[nz_i]
seed_x = index % width
# 向下取整
seed_y = index // width
ff_mask = np.zeros((height + 2, width + 2), dtype=np.uint8)
area, _, __, rect = cv2.floodFill(mask,
ff_mask, (seed_x, seed_y),
255,
flags=cv2.FLOODFILL_MASK_ONLY
| cv2.FLOODFILL_FIXED_RANGE)
x = rect[0]
y = rect[1]
w = rect[2]
h = rect[3]
# slicing into found rect
roi_mask = ff_mask[y + 1:y + 1 + h, x + 1:x + 1 + w]
found = False
# 找指定区域的内容
if min_area < area < max_area:
found_mask = roi_mask
newX = x * 4
newY = (y * 4) + start_y
newW = w * 4
newH = h * 4
# 边界模式，跳出循环
if splitMode == "full":
if newX <= 0:
found_mask = None
part_remove_left += 1
if newW + newX >= original_width:
found_mask = None
part_remove_right += 1
# 扩散30px截取
startX = newX - 30
endX = newX + newW + 30
startY = newY - 30
endY = newY + newH + 30
found_image = original_img[startY:endY, startX:endX].copy()
found = True
out_file = os.path.join(out_dir, "%s.png" % (newX))
cv2.imwrite(out_file, found_image)
# 数字下标
if newW < 400 and newH > 100 and newH < 200 and newY > 1800:
out_file = os.path.join(out_dir, "%s.png" % (newX))
cv2.imwrite(out_file, found_image)
pox = preproces_line(
out_file,
os.path.join(out_dir, "%s.line.png" % (newX)))
left_top_x, left_top_y = pox[0]
left_bottom_x, left_bottom_y = pox[1]
# 计算在图中真实坐标
left_top_x += startX
left_top_y += startY
left_bottom_x += startX
left_bottom_y += startY
# 目标的X坐标
targetX = getXValue((left_bottom_x, left_bottom_y),
(left_top_x, left_top_y), 1050)
labelPoint.append(
[left_bottom_x, left_bottom_y,
int(targetX), 1050])
cv2.line(newimage, (left_bottom_x, left_bottom_y),
(int(targetX), 1050), (255, 255, 0), 10)
found_mask = None
# 找到其余的元素
if found_mask is not None:
centerX = newX + newW / 2
centerY = newY + newH / 2
partImages.append(
[int(centerX),
int(centerY), found_image])
out_file = os.path.join(out_dir, "%s.png" % (newX))
cv2.imwrite(out_file, found_image)
# clearing found component in the mask
mask[y:y + h, x:x + w][roi_mask != 0] = 0
if found:
break
nz_i += 1
if nz_i >= len(nz):
break
# 获取交叉点坐标
crosslineDistance(newimage, partImages, labelPoint)
# 输出线图
cv2.imwrite(os.path.join(out_dir, "%s.png" % (dir_name)), newimage)
return dir_name + ".png", True
except Exception as _:
return dir_name + ".png", False
