import copy import json import os from PIL import Image from alibabacloud_imageseg20191230.client import Client as imageseg20191230Client from alibabacloud_imageseg20191230.models import SegmentCommodityAdvanceRequest from alibabacloud_imageseg20191230 import models as imageseg_20191230_models from alibabacloud_tea_util.models import RuntimeOptions from alibabacloud_tea_openapi import models as open_api_models from alibabacloud_tea_openapi.models import Config from alibabacloud_tea_util import models as util_models import requests from io import BytesIO import cv2 import numpy as np from .module_online_data import GetOnlineData # todo 获取密钥 # key_id,key_secret = GetOnlineData().get_cutout_image_config() # 惠利玛的key AccessKeyId = "LTAI5tBdDVT9Wc5idJXdGHjw" AccessKeySecret = "bCSotQ7eAztOxx6AqHwJJPsb0hkECe" ALI_MAPPING = {"tops": "上衣", "coat": "外套", "skirt": "裙装", "pants": "裤装"} ALI_CLOTH_CLASSES = ["tops", "coat", "skirt", "pants"] # https://help.aliyun.com/zh/viapi/developer-reference/python?spm=a2c4g.11186623.0.i0#task-2252575 # pip install alibabacloud_goodstech20191230 # pip install alibabacloud_tea_openapi # pip install alibabacloud_tea_util class Segment(object): def __init__(self): self.client = self.create_client() def get_no_bg_common(self, file_path): # 初始化RuntimeObject runtime_option = RuntimeOptions() try: # 场景一：文件在本地 img = open(file_path, "rb") request = SegmentCommodityAdvanceRequest() request.image_urlobject = img response = self.client.segment_common_image_advance(request, runtime_option) # 获取整体结果 # print(response.body) img.close() return response.body except Exception as error: # 获取整体报错信息 print("error", error) return None def get_no_bg_goods(self, file_path=None, _im=None): im = _im img = BytesIO() im.save(img, format="JPEG") # format: PNG or JPEG img.seek(0) # rewind to the start request = imageseg_20191230_models.SegmentCommodityAdvanceRequest() request.image_urlobject = img client = self.create_client() # 5、调用api，注意，recognize_bank_card_advance需要更换为相应能力对应的方法名。方法名是根据能力名称按照一定规范形成的，如能力名称为SegmentCommonImage，对应方法名应该为segment_common_image_advance。 runtime = util_models.RuntimeOptions() response = client.segment_commodity_advance(request, runtime) return response.body def get_no_bg_cloths(self, _im=None): im = _im img = BytesIO() im.save(img, format="JPEG") # format: PNG or JPEG img.seek(0) # rewind to the start request = imageseg_20191230_models.SegmentClothAdvanceRequest() request.image_urlobject = img request.cloth_class = ALI_CLOTH_CLASSES client = self.create_client() # 5、调用api，注意，recognize_bank_card_advance需要更换为相应能力对应的方法名。方法名是根据能力名称按照一定规范形成的，如能力名称为SegmentCommonImage，对应方法名应该为segment_common_image_advance。 runtime = util_models.RuntimeOptions() response = client.segment_cloth_advance(request, runtime) return response.body def create_client(self): """ 使用AK&SK初始化账号Client @param access_key_id: @param access_key_secret: @return: Client @throws Exception """ config = open_api_models.Config( # 必填，您的 AccessKey ID, access_key_id=AccessKeyId, # 必填，您的 AccessKey Secret, access_key_secret=AccessKeySecret, ) # 访问的域名 config.endpoint = f"imageseg.cn-shanghai.aliyuncs.com" return imageseg20191230Client(config) class Picture: def __init__(self, in_path, im=None): if im: self.im = im else: self.im = Image.open(in_path).convert("RGB") self.x, self.y = self.im.size # print(self.x, self.y) def save_img(self, outpath, quality=90): # self.im = self.im.convert("RGB") self.im.save(outpath, quality=quality) def resize(self, width): re_x = int(width) re_y = int(self.y * re_x / self.x) self.im = self.im.resize((re_x, re_y), Image.BICUBIC) self.x, self.y = self.im.size def resize_by_heigh(self, heigh): re_y = int(heigh) re_x = int(self.x * re_y / self.y) self.im = self.im.resize((re_x, re_y), Image.BICUBIC) self.x, self.y = self.im.size class RemoveBgALi(object): def __init__(self): self.segment = Segment() def get_image_cut(self, file_path, out_file_path=None, original_im=None): if original_im: original_pic = Picture(in_path=None, im=original_im) else: original_pic = Picture(file_path) if original_pic.im.mode != "RGB": print("抠图图片不能是PNG") return None new_pic = copy.copy(original_pic) after_need_resize = False if new_pic.x > new_pic.y: if new_pic.x > 2000: after_need_resize = True new_pic.resize(2000) else: if new_pic.y > 2000: after_need_resize = True new_pic.resize_by_heigh(heigh=2000) # new_pic.im.show() body = self.segment.get_no_bg_goods(file_path=None, _im=new_pic.im) body = eval(str(body)) try: image_url = body["Data"]["ImageURL"] except BaseException as e: print("阿里抠图错误：", e) # todo 处理失败，需要删除过程图片 return None # 字节流转PIL对象 response = requests.get(image_url) pic = response.content _img_im = Image.open(BytesIO(pic)) # 阿里返回的抠图结果 已转PIL对象 # 原图更大，则需要执行CV处理 if after_need_resize: # 将抠图结果转成mask # _img_im = Image.open(_path) # 将抠图结果放大到原始图大小 _img_im = _img_im.resize(original_pic.im.size) new_big_mask = Image.new("RGB", _img_im.size, (0, 0, 0)) white = Image.new("RGB", _img_im.size, (255, 255, 255)) new_big_mask.paste(white, mask=_img_im.split()[3]) # ---------制作选区缩小的mask # mask = cv2.imread(mask_path) # mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY) mask = cv2.cvtColor( np.asarray(new_big_mask), cv2.COLOR_BGR2GRAY ) # 将PIL 格式转换为 CV对象 mask[mask != 255] = 0 # 黑白反转 # mask = 255 - mask # 选区缩小10 kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (10, 10)) erode_im = cv2.morphologyEx(mask, cv2.MORPH_ERODE, kernel) # -------再进行抠图处理 mask = Image.fromarray( cv2.cvtColor(erode_im, cv2.COLOR_GRAY2RGBA) ) # CV 对象转 PIL transparent_im = Image.new("RGBA", original_pic.im.size, (0, 0, 0, 0)) # original_pic.im.show() # mask.show() transparent_im.paste(original_pic.im, (0, 0), mask.convert("L")) # transparent_im.show() # 上述抠图结果进行拼接 _img_im.paste(transparent_im, (0, 0), transparent_im) # _img_im.show("11111111111111111111111") if out_file_path: _img_im.save(out_file_path) return _img_im def get_image_cut_cloths(self, file_path, out_file_path=None, original_im=None): if original_im: original_pic = Picture(in_path=None, im=original_im) else: original_pic = Picture(file_path) if original_pic.im.mode != "RGB": print("抠图图片不能是PNG") return None new_pic = copy.copy(original_pic) after_need_resize = False if new_pic.x > new_pic.y: if new_pic.x > 2000: after_need_resize = True new_pic.resize(2000) else: if new_pic.y > 2000: after_need_resize = True new_pic.resize_by_heigh(heigh=2000) try: body = self.segment.get_no_bg_cloths(_im=new_pic.im) body = eval(str(body)) clothsArray = [] Elements = body["Data"]["Elements"] for idx, clothItem in enumerate(Elements): if idx == 0: ImageURL = clothItem.get("ImageURL") print("clothItem", clothItem) response = requests.get(ImageURL) pic = response.content _img_im = Image.open(BytesIO(pic)) clothsArray.append( {"type": "all", "cn_name": "合并图", "pilImage": _img_im} ) else: ClassUrl = clothItem.get("ClassUrl") classKeys = ClassUrl.keys() for classKey in classKeys: keyUrl = ClassUrl.get(classKey) response = requests.get(keyUrl) pic = response.content mask_image = Image.open(BytesIO(pic)) if self.isEmptyMask(mask_image.convert("RGB")): continue if mask_image.mode != "L": mask_image = mask_image.convert("L") # 使用蒙版图像对原图进行抠图 result_image = new_pic.im.copy() result_image.putalpha(mask_image) clothsArray.append( { "type": classKey, "cn_name": ALI_MAPPING[classKey], "pilImage": result_image, } ) except BaseException as e: # todo 处理失败，需要删除过程图片 return None image_arrs = [] for _, item in enumerate(clothsArray): _img_im = item["pilImage"] type = item["type"] cn_name = item["cn_name"] # 原图更大，则需要执行CV处理 if after_need_resize: _img_im = self.clothsDispose( _img_im, original_pic=original_pic, out_file_path=out_file_path ) image_arrs.append({"image_obj": _img_im, "type": type, "cn_name": cn_name}) return image_arrs def isEmptyMask(self, img): data = np.array(img) max_index = np.unravel_index(np.argmax(data, axis=None), data.shape) max_value = data[max_index] return True if max_value == 0 else False def clothsDispose(self, _img_im, original_pic, out_file_path): # 将抠图结果转成mask # _img_im = Image.open(_path) # 将抠图结果放大到原始图大小 _img_im = _img_im.resize(original_pic.im.size) new_big_mask = Image.new("RGB", _img_im.size, (0, 0, 0)) white = Image.new("RGB", _img_im.size, (255, 255, 255)) new_big_mask.paste(white, mask=_img_im.split()[3]) # ---------制作选区缩小的mask # mask = cv2.imread(mask_path) # mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY) mask = cv2.cvtColor( np.asarray(new_big_mask), cv2.COLOR_BGR2GRAY ) # 将PIL 格式转换为 CV对象 mask[mask != 255] = 0 # 黑白反转 # mask = 255 - mask # 选区缩小10 kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (10, 10)) erode_im = cv2.morphologyEx(mask, cv2.MORPH_ERODE, kernel) # -------再进行抠图处理 mask = Image.fromarray( cv2.cvtColor(erode_im, cv2.COLOR_GRAY2RGBA) ) # CV 对象转 PIL transparent_im = Image.new("RGBA", original_pic.im.size, (0, 0, 0, 0)) # original_pic.im.show() # mask.show() transparent_im.paste(original_pic.im, (0, 0), mask.convert("L")) # transparent_im.show() # 上述抠图结果进行拼接 _img_im.paste(transparent_im, (0, 0), transparent_im) # _img_im.show("11111111111111111111111") return _img_im def get_image_cut1(self, file_path, out_file_path=None): original_pic = Picture(file_path) new_pic = copy.copy(original_pic) if new_pic.x > 2000: new_pic.resize(2000) # new_pic.im.show() body = self.segment.get_no_bg_goods(file_path=out_file_path, _im=new_pic.im) body = eval(str(body)) try: image_url = body["Data"]["ImageURL"] except BaseException as e: print("阿里抠图错误：", e) # todo 处理失败，需要删除过程图片 return None # 字节流转PIL对象 response = requests.get(image_url) pic = response.content _img_im = Image.open(BytesIO(pic)) # 阿里返回的抠图结果 已转PIL对象 if original_pic.x > 2000: # 原图更大，则需要执行CV处理 # 对mask进行放大，然后进行抠图处理 print("对mask进行放大，然后进行抠图处理") transparent_im = Image.new("RGBA", original_pic.im.size, (0, 0, 0, 0)) _img_im = _img_im.resize((original_pic.x, original_pic.y)) transparent_im.paste(original_pic.im, (0, 0), mask=_img_im) _img_im = transparent_im pass _img_im.save(out_file_path) return _img_im def download_picture(self, url, out_path): response = requests.get(url) pic = response.content with open(out_path, "wb") as f: f.write(pic) if __name__ == "__main__": r = RemoveBgALi() path = r"D:\MyDocuments\PythonCode\MyPython\red_dragonfly\deal_pics\auto_capture_V2\IPC\test\171112057820408.png" out_path = "{}._no_bg-out.png".format(path) r.get_image_cut(path, out_file_path=out_path)