# Copyright(C) 2022. Huawei Technologies Co.,Ltd. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import os
import json
import stat
import glob
import cv2
from StreamManagerApi import StreamManagerApi, MxDataInput
import numpy as np
from utils import xyxy2xywh

from plots import box_label, colors

names = ['non_conduct', 'abrasion_mark', 'corner_leak', 'orange_peel', 'leak', 'jet_flow', 'paint_bubble', 'pit',
         'motley', 'dirty_spot']

if __name__ == '__main__':
    MODES = stat.S_IWUSR | stat.S_IRUSR
    # init stream manager
    dict_classes = {
        "non_conduct": "0", "abrasion_mark": "1", "corner_leak": "2", "orange_peel": "3", "leak": "4", "jet_flow": "5",
        "paint_bubble": "6", "pit": "7", "motley": "8", "dirty_spot": "9"
    }

    streamManagerApi = StreamManagerApi()
    ret = streamManagerApi.InitManager()
    if ret != 0:
        print("Failed to init Stream manager, ret=%s" % str(ret))
        exit()
    # create streams by pipeline config file
    with open("./pipeline/AlDefectDetection.pipeline", 'rb') as f:
        pipelineStr = f.read()
    ret = streamManagerApi.CreateMultipleStreams(pipelineStr)
    if ret != 0:
        print("Failed to create Stream, ret=%s" % str(ret))
        exit()

    print("load pipline done!")

    TESTIMGS = 0
    # Input object of streams -- detection target
    ORI_IMG_PATH = "./test/data/test/"
    TXT_PATH = "./test/" + "test_out_txt/"
    DETECT_IMG_PATH = "./test/" + "img_detected/"
    PRE_IMG_PATH = "./test/" + "img_pre/"
    if not os.path.exists(TXT_PATH):
        os.makedirs(TXT_PATH)
    if not os.path.exists(DETECT_IMG_PATH):
        os.makedirs(DETECT_IMG_PATH)
    if not os.path.exists(PRE_IMG_PATH):
        os.makedirs(PRE_IMG_PATH)

    files_list = glob.glob(ORI_IMG_PATH + '/*.jpg')
    if len(files_list) == 0:
        print("The input directory is EMPTY!")
        print("Please place the picture in './test/data/test/' !")
        exit()

    for item in os.listdir(ORI_IMG_PATH):

        ori_img_path = os.path.join(ORI_IMG_PATH, item)

        if os.path.getsize(ori_img_path) == 0:
            print("Error!The test image is empty.")
            continue

        ori_img = cv2.imread(ori_img_path)  # 读取图片
        h0, w0 = ori_img.shape[:2]
        img_size = 640
        r = img_size / max(h0, w0)  # ratio
        input_shape = (640, 640)

        print("file_path:", ori_img_path)
        img_name = item.split(".")[0]
        img_txt = TXT_PATH + img_name + ".txt"
        if os.path.exists(img_txt):
            os.remove(img_txt)

        # Construct the input of the stream
        dataInput = MxDataInput()
        with open(ori_img_path, 'rb') as f:
            dataInput.data = f.read()

        # Inputs data to a specified stream based on streamName.
        streamName = b'classification+detection'
        inPluginId = 0
        uniqueId = streamManagerApi.SendDataWithUniqueId(streamName, inPluginId, dataInput)
        if uniqueId < 0:
            print("Failed to send data to stream.")
            exit()

        # Obtain the inference result by specifying streamName and uniqueId.
        inferResult = streamManagerApi.GetResultWithUniqueId(streamName, uniqueId, 5000)
        if inferResult.errorCode != 0:
            print("GetResultWithUniqueId error. errorCode=%d, errorMsg=%s" % (
                inferResult.errorCode, inferResult.data.decode()))
            exit()

        results = json.loads(inferResult.data.decode())
        if not results:
            print("No object detected")
            with os.fdopen(os.open(img_txt, os.O_RDWR | os.O_CREAT, MODES), 'a+') as f:
                pass
            continue
        img = cv2.imread(ori_img_path, cv2.IMREAD_COLOR)
        gn = np.array(ori_img.shape)[[1, 0, 1, 0]]

        bboxes = []
        classVecs = []
        for info in results['MxpiObject']:
            bboxes.append([float(info['x0']), float(info['y0']), float(info['x1']), float(info['y1'])])
            classVecs.append(info["classVec"])
        for (xyxy, classVec) in zip(bboxes, classVecs):
            xyxy = np.array(xyxy)
            xywh = (xyxy2xywh(xyxy.reshape(1, 4)) / gn).reshape(-1).tolist()  # normalized xywh
            line = (
                int(dict_classes[classVec[0]["className"]]), *xywh, round(classVec[0]["confidence"], 6))  # label format
            with os.fdopen(os.open(img_txt, os.O_RDWR | os.O_CREAT, MODES), 'a+') as f:
                f.write(('%g ' * len(line)).rstrip() % line + '\n')

            label = f'{classVec[0]["className"]} {classVec[0]["confidence"]:.4f}'
            save_img = box_label(ori_img, xyxy, label, color=colors[names.index(classVec[0]["className"])])

        cv2.imwrite(DETECT_IMG_PATH + 'result' + item, save_img)
        TESTIMGS += 1
        ######################################################################################
        # print the infer result
        print(inferResult.data.decode())

    # Mark image count
    print("Image count:%d" % TESTIMGS)

    # destroy streams
    streamManagerApi.DestroyAllStreams()