#!/usr/bin/env python
"""
 Copyright (C) 2018-2019 Intel Corporation

 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 sys
import os
from argparse import ArgumentParser, SUPPRESS
import cv2
import time
import logging as log
import numpy as np
from math import exp

from openvino.inference_engine import IENetwork, IECore

# コマンドラインパーサの構築
def build_argparser():
    parser = ArgumentParser(add_help=False)
    args = parser.add_argument_group('Options')
    args.add_argument('-h', '--help', action='help', default=SUPPRESS, help='Show this help message and exit.')
    args.add_argument("-m", "--model", help="Required. Path to an .xml file with a trained model.",
                      required=True, type=str)
    args.add_argument("-i", "--input", 
                      help="Required. Path to a image/video file. (Specify 'cam' to work with camera)", 
                      required=True, type=str)
    args.add_argument("-l", "--cpu_extension",
                      help="Optional. Required for CPU custom layers. Absolute path to a shared library with "
                           "the kernels implementations.", type=str, default=None)
    args.add_argument("-d", "--device",
                      help="Optional. Specify the target device to infer on; CPU, GPU, FPGA, HDDL or MYRIAD is "
                           "acceptable. The demo will look for a suitable plugin for device specified. "
                           "Default value is CPU", default="CPU", type=str)
    args.add_argument("--labels", help="Optional. Labels mapping file", default=None, type=str)
    args.add_argument("-pt", "--prob_threshold", help="Optional. Probability threshold for detections filtering",
                      default=0.5, type=float)
    args.add_argument("-iout", "--iou_threshold", help="Optional. Intersection over union threshold for overlapping "
                                                       "detections filtering", default=0.4, type=float)
    args.add_argument("-ni", "--number_iter", help="Optional. Number of inference iterations", default=1, type=int)
    args.add_argument("-pc", "--perf_counts", help="Optional. Report performance counters", default=False,
                      action="store_true")
    args.add_argument("-r", "--raw_output_message", help="Optional. Output inference results raw values showing",
                      default=False, action="store_true")
    parser.add_argument("--save", help="Optional. Save result to specified file", default=None, type=str)
    parser.add_argument("--log", help="Optional. Save log to specified file", default=None, type=str)
    parser.add_argument("--sync", action='store_true', help="Optional. Sync mode")
    parser.add_argument("--no_disp", action='store_true', help="Optional. without image display")
    return parser


class YoloParams:
    # ------------------------------------------- Extracting layer parameters ------------------------------------------
    # Magic numbers are copied from yolo samples
    def __init__(self, param, side):
        self.num = 3 if 'num' not in param else int(param['num'])
        self.coords = 4 if 'coords' not in param else int(param['coords'])
        self.classes = 80 if 'classes' not in param else int(param['classes'])
        self.anchors = [10.0, 13.0, 16.0, 30.0, 33.0, 23.0, 30.0, 61.0, 62.0, 45.0, 59.0, 119.0, 116.0, 90.0, 156.0,
                        198.0,
                        373.0, 326.0] if 'anchors' not in param else [float(a) for a in param['anchors'].split(',')]

        if 'mask' in param:
            mask = [int(idx) for idx in param['mask'].split(',')]
            self.num = len(mask)

            maskedAnchors = []
            for idx in mask:
                maskedAnchors += [self.anchors[idx * 2], self.anchors[idx * 2 + 1]]
            self.anchors = maskedAnchors

        self.side = side
        self.isYoloV3 = 'mask' in param  # Weak way to determine but the only one.


    def log_params(self):
        # params_to_print = {'classes': self.classes, 'num': self.num, 'coords': self.coords, 'anchors': self.anchors}
        # [log.info("         {:8}: {}".format(param_name, param)) for param_name, param in params_to_print.items()]
        pass

def entry_index(side, coord, classes, location, entry):
    side_power_2 = side ** 2
    n = location // side_power_2
    loc = location % side_power_2
    return int(side_power_2 * (n * (coord + classes + 1) + entry) + loc)


def scale_bbox(x, y, h, w, class_id, confidence, h_scale, w_scale):
    xmin = int((x - w / 2) * w_scale)
    ymin = int((y - h / 2) * h_scale)
    xmax = int(xmin + w * w_scale)
    ymax = int(ymin + h * h_scale)
    return dict(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, class_id=class_id, confidence=confidence)


def parse_yolo_region(blob, resized_image_shape, original_im_shape, params, threshold):
    # ------------------------------------------ Validating output parameters ------------------------------------------
    _, _, out_blob_h, out_blob_w = blob.shape
    assert out_blob_w == out_blob_h, "Invalid size of output blob. It sould be in NCHW layout and height should " \
                                     "be equal to width. Current height = {}, current width = {}" \
                                     "".format(out_blob_h, out_blob_w)

    # ------------------------------------------ Extracting layer parameters -------------------------------------------
    orig_im_h, orig_im_w = original_im_shape
    resized_image_h, resized_image_w = resized_image_shape
    objects = list()
    predictions = blob.flatten()
    side_square = params.side * params.side

    # ------------------------------------------- Parsing YOLO Region output -------------------------------------------
    for i in range(side_square):
        row = i // params.side
        col = i % params.side
        for n in range(params.num):
            obj_index = entry_index(params.side, params.coords, params.classes, n * side_square + i, params.coords)
            scale = predictions[obj_index]
            if scale < threshold:
                continue
            box_index = entry_index(params.side, params.coords, params.classes, n * side_square + i, 0)
            # Network produces location predictions in absolute coordinates of feature maps.
            # Scale it to relative coordinates.
            x = (col + predictions[box_index + 0 * side_square]) / params.side
            y = (row + predictions[box_index + 1 * side_square]) / params.side
            # Value for exp is very big number in some cases so following construction is using here
            try:
                w_exp = exp(predictions[box_index + 2 * side_square])
                h_exp = exp(predictions[box_index + 3 * side_square])
            except OverflowError:
                continue
            # Depends on topology we need to normalize sizes by feature maps (up to YOLOv3) or by input shape (YOLOv3)
            w = w_exp * params.anchors[2 * n] / (resized_image_w if params.isYoloV3 else params.side)
            h = h_exp * params.anchors[2 * n + 1] / (resized_image_h if params.isYoloV3 else params.side)
            for j in range(params.classes):
                class_index = entry_index(params.side, params.coords, params.classes, n * side_square + i,
                                          params.coords + 1 + j)
                confidence = scale * predictions[class_index]
                if confidence < threshold:
                    continue
                objects.append(scale_bbox(x=x, y=y, h=h, w=w, class_id=j, confidence=confidence,
                                          h_scale=orig_im_h, w_scale=orig_im_w))
    return objects


def intersection_over_union(box_1, box_2):
    width_of_overlap_area = min(box_1['xmax'], box_2['xmax']) - max(box_1['xmin'], box_2['xmin'])
    height_of_overlap_area = min(box_1['ymax'], box_2['ymax']) - max(box_1['ymin'], box_2['ymin'])
    if width_of_overlap_area < 0 or height_of_overlap_area < 0:
        area_of_overlap = 0
    else:
        area_of_overlap = width_of_overlap_area * height_of_overlap_area
    box_1_area = (box_1['ymax'] - box_1['ymin']) * (box_1['xmax'] - box_1['xmin'])
    box_2_area = (box_2['ymax'] - box_2['ymin']) * (box_2['xmax'] - box_2['xmin'])
    area_of_union = box_1_area + box_2_area - area_of_overlap
    if area_of_union == 0:
        return 0
    return area_of_overlap / area_of_union

# 結果の解析と表示
def parse_result(net, res, frame, labels_map, args) :
    objects = list()
    input_blob = list(net.inputs.keys())[0]                 # 冒頭でinputは一つでなければエラーになってるので決め打ちで[0]
    in_frame_shape = net.inputs[input_blob].shape[2:]       # HWC→BCHWに変更してあるので、height/widthはshape[2:]で取得
    for layer_name, out_blob in res.items():
        out_blob = out_blob.reshape(net.layers[net.layers[layer_name].parents[0]].shape)
        layer_params = YoloParams(net.layers[layer_name].params, out_blob.shape[2])
        # log.info("Layer {} parameters: ".format(layer_name))
        layer_params.log_params()
        objects += parse_yolo_region(out_blob, 
                                        in_frame_shape,
                                        frame.shape[:-1], 
                                        layer_params,
                                        args.prob_threshold)

    # Filtering overlapping boxes with respect to the --iou_threshold CLI parameter
    objects = sorted(objects, key=lambda obj : obj['confidence'], reverse=True)
    for i in range(len(objects)):
        if objects[i]['confidence'] == 0:
            continue
        for j in range(i + 1, len(objects)):
            if intersection_over_union(objects[i], objects[j]) > args.iou_threshold:
                objects[j]['confidence'] = 0
    
    # Drawing objects with respect to the --prob_threshold CLI parameter
    objects = [obj for obj in objects if obj['confidence'] >= args.prob_threshold]
    
    if len(objects) and args.raw_output_message:
        log.info("\nDetected boxes for batch {}:".format(1))
        log.info(" Class ID | Confidence | XMIN | YMIN | XMAX | YMAX | COLOR ")
    
    origin_im_size = frame.shape[:-1]
    for obj in objects:
        # Validation bbox of detected object
        if obj['xmax'] > origin_im_size[1] or obj['ymax'] > origin_im_size[0] or obj['xmin'] < 0 or obj['ymin'] < 0:
            continue
        # color = (int(min(obj['class_id'] * 12.5, 255)),
        #          min(obj['class_id'] * 7, 255), min(obj['class_id'] * 5, 255))
        color = (255, 128, 128)
        det_label = labels_map[obj['class_id']] if labels_map and len(labels_map) >= obj['class_id'] else \
            str(obj['class_id'])
        
        if args.raw_output_message:
            log.info(
                "{:^9} | {:10f} | {:4} | {:4} | {:4} | {:4} | {} ".format(det_label, obj['confidence'], obj['xmin'],
                                                                          obj['ymin'], obj['xmax'], obj['ymax'],
                                                                          color))
        
        cv2.rectangle(frame, (obj['xmin'], obj['ymin']), (obj['xmax'], obj['ymax']), color, 2)
        cv2.putText(frame,
                    "#" + det_label + ' ' + str(round(obj['confidence'] * 100, 1)) + ' %',
                    (obj['xmin'], obj['ymin'] - 7), cv2.FONT_HERSHEY_COMPLEX, 0.6, color, 1)

def main():
    log.basicConfig(format="[ %(levelname)s ] %(message)s", level=log.INFO, stream=sys.stdout)
    args = build_argparser().parse_args()
    
    model_xml = args.model
    model_bin = os.path.splitext(model_xml)[0] + ".bin"
    
    no_disp = args.no_disp
    
    model_label = None
    if args.labels:
        model_label = args.labels
    else:
        model_label = os.path.splitext(model_xml)[0] + ".labels"
    if not os.path.isfile(model_label)  :
        model_label = None
    
    # ------------- 1. Plugin initialization for specified device and load extensions library if specified -------------
    log.info("Creating Inference Engine...")
    ie = IECore()
    if args.cpu_extension and 'CPU' in args.device:
        ie.add_extension(args.cpu_extension, "CPU")
    
    # -------------------- 2. Reading the IR generated by the Model Optimizer (.xml and .bin files) --------------------
    log.info(f"Loading network files:\n\t{model_xml}\n\t{model_bin}\n\t{model_label}")
    net = IENetwork(model=model_xml, weights=model_bin)
    
    # ---------------------------------- 3. Load CPU extension for support specific layer ------------------------------
    if "CPU" in args.device:
        supported_layers = ie.query_network(net, "CPU")
        not_supported_layers = [l for l in net.layers.keys() if l not in supported_layers]
        if len(not_supported_layers) != 0:
            log.error("Following layers are not supported by the plugin for specified device {}:\n {}".
                      format(args.device, ', '.join(not_supported_layers)))
            log.error("Please try to specify cpu extensions library path in sample's command line parameters using -l "
                      "or --cpu_extension command line argument")
            sys.exit(1)
    
    # ---------------------------------------------- 
    # print(net.outputs)
    # YOLOのoutputsは1ではない
    
    # ---------------------------------------------- 4. Preparing inputs -----------------------------------------------
    log.info("Preparing inputs")
    # print(net.inputs)
    assert len(net.inputs.keys()) == 1, "Sample supports only YOLO V3 based single input topologies"
    input_blob = list(net.inputs.keys())[0]     # inputは一つだけなので決め打ちで[0]
    
    #  Defaulf batch_size is 1
    net.batch_size = 1
    
    # Read and pre-process input image
    input_n, input_colors, input_height, input_width = net.inputs[input_blob].shape
    
    feed_dict = {}
    
    if args.input == 'cam':
        input_stream = 0
    else:
        input_stream = os.path.abspath(args.input)
        assert os.path.isfile(input_stream), "Specified input file doesn't exist"
    
    cap = cv2.VideoCapture(input_stream)
    
    # print(f'save flag : {args.save}')
    writer = None
    # 幅と高さを取得
    img_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
    img_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
    disp_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + 90      # 情報表示領域分を追加
    # フレームレート(1フレームの時間単位はミリ秒)の取得
    frame_rate = int(cap.get(cv2.CAP_PROP_FPS))
    if args.save :
        # フォーマット
        fmt = cv2.VideoWriter_fourcc('m', 'p', '4', 'v')
        writer = cv2.VideoWriter(args.save, fmt, frame_rate, (img_width, disp_height))
    
    log_f = None
    if args.log :
        log_f = open(args.log, mode='w')
        log_f.write(f'frame_number, frame_time, preprocess_time, inf_time, parse_time, render_time, wait_time\n')     # 見出し行
    
    if model_label:
        with open(model_label, 'r') as f:
            labels_map = [x.strip() for x in f]
    else:
        labels_map = None
    
    cur_request_id = 0
    next_request_id = 1
    
    log.info("Starting inference in async mode...")
    is_async_mode = not args.sync
    
    number_input_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
    number_input_frames = 1 if number_input_frames != -1 and number_input_frames < 0 else number_input_frames
    
    wait_key_code = 1
    
    # Number of frames in picture is 1 and this will be read in cycle. Sync mode is default value for this case
    if number_input_frames != 1:
        ret, frame = cap.read()
    else:
        is_async_mode = False
        wait_key_code = 0
    
    # ----------------------------------------- 5. Loading model to the plugin -----------------------------------------
    log.info("Loading model to the plugin")
    exec_net = ie.load_network(network=net, num_requests=2, device_name=args.device)
    
    # ----------------------------------------------- 6. Doing inference -----------------------------------------------
    log.info("Starting inference...")
    print("To close the application, press 'CTRL+C' here or switch to the output window and press ESC key")
    print("To switch between sync/async modes, press TAB key in the output window")
    
    frame_time = 0
    preprocess_time = 0
    inf_time = 0
    parse_time = 0
    render_time = 0
    prev_time = time.time()
    frame_number = 0
    
    while cap.isOpened():
        preprocess_start = time.time()
        if is_async_mode:
            ret, next_frame = cap.read()
        else:
            ret, frame = cap.read()
        if not ret:
            break
        
        # 表示用領域を含んだフレーム
        pad_img = np.zeros((disp_height, img_width, 3), np.uint8)
        preprocess_end = time.time()
        preprocess_time = preprocess_end - preprocess_start
        
        inf_start = time.time()
        if is_async_mode:
            request_id = next_request_id
            in_frame = cv2.resize(next_frame, (input_width, input_height))
        else:
            request_id = cur_request_id
            in_frame = cv2.resize(frame, (input_width, input_height))
        
        # resize input_frame to network size
        in_frame = in_frame.transpose((2, 0, 1))  # Change data layout from HWC to CHW
        in_frame = in_frame.reshape((input_n, input_colors, input_height, input_width))     # CHW→BCHW
        feed_dict[input_blob] = in_frame
        
        exec_net.start_async(request_id=request_id, inputs=feed_dict)
        if exec_net.requests[cur_request_id].wait(-1) == 0:
            inf_end = time.time()
            inf_time = inf_end - inf_start
            
            # Parse detection results of the current request
            parse_start = time.time()
            res = exec_net.requests[cur_request_id].outputs
            parse_result(net, res, frame, labels_map, args)
            parse_end = time.time()
            parse_time = parse_end - parse_start
            
            # Draw performance stats
            inf_time_message =      "Inference time: N\A for async mode" if is_async_mode else \
                                    "Inference time: {:.3f} ms".format(inf_time * 1000)
            frame_time_message =    "Frame time: {:.3f} ms".format(frame_time * 1000)
            render_time_message =   "OpenCV rendering time: {:.3f} ms".format(render_time * 1000)
            async_mode_message =    "Async mode is on. Processing request {}".format(cur_request_id) if is_async_mode else \
                                    "Async mode is off. Processing request {}".format(cur_request_id)
            parsing_time_message =  "parse time is {:.3f}".format(parse_time * 1000)
            
            """
            cv2.putText(frame, inf_time_message,    (15, 15),                   cv2.FONT_HERSHEY_COMPLEX, 0.5, (200,  10,  10), 1)
            cv2.putText(frame, parsing_time_message,(15, 30),                   cv2.FONT_HERSHEY_COMPLEX, 0.5, ( 10,  10, 200), 1)
            cv2.putText(frame, render_time_message, (15, 45),                   cv2.FONT_HERSHEY_COMPLEX, 0.5, ( 10,  10, 200), 1)
            cv2.putText(frame, frame_time_message,  (10, int(img_height - 35)), cv2.FONT_HERSHEY_COMPLEX, 0.5, (200,  10,  10), 1)
            cv2.putText(frame, async_mode_message,  (10, int(img_height - 20)), cv2.FONT_HERSHEY_COMPLEX, 0.5, ( 10,  10, 200), 1)
            """
            cv2.putText(pad_img, inf_time_message,    (10, img_height + 15), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 128, 128), 1)
            cv2.putText(pad_img, parsing_time_message,(10, img_height + 30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 128, 128), 1)
            cv2.putText(pad_img, render_time_message, (15, img_height + 45), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 128, 128), 1)
            cv2.putText(pad_img, frame_time_message,  (10, img_height + 60), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 128, 128), 1)
            cv2.putText(pad_img, async_mode_message,  (10, img_height + 75), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 128, 128), 1)
 
        # 表示
        render_start = time.time()
        pad_img[:img_height, :img_width] = frame        # 表示用領域に画像をコピー
        
        if not no_disp :
            cv2.imshow("Detection Results", pad_img)        # 表示
        else :
            print(f'frame_number: {frame_number:5d}')
        
        if writer:
            # 保存が設定されているときは画像を保存
            writer.write(pad_img)
        
        render_end = time.time()
        render_time = render_end - render_start
        
        wait_start = time.time()
        if is_async_mode:
            # 非同期モードではフレームバッファ入れ替え
            cur_request_id, next_request_id = next_request_id, cur_request_id
            frame = next_frame
        
        key = cv2.waitKey(wait_key_code)
        if key == 27:
            # ESCキー
            break
        if key == 9:
            # TABキー
            exec_net.requests[cur_request_id].wait()    # ASYNC→SYNC切り替え時にREQUEST_BUSYでアボートする不具合の対策
            is_async_mode = not is_async_mode
            log.info("Switched to {} mode".format("async" if is_async_mode else "sync"))
        
        wait_end = time.time()
        wait_time = wait_end - wait_start
        
        cur_time = time.time()
        frame_time = cur_time - prev_time         # 1フレームの処理時間
        prev_time = cur_time
        if log_f :
            log_f.write(f'{frame_number:5d}, {frame_time * 1000:.3f}, {preprocess_time * 1000:.3f}, {inf_time * 1000:.3f}, {parse_time * 1000:.3f}, {render_time * 1000:.3f}, {wait_time * 1000:.3f}\n')
        frame_number = frame_number + 1
    
    # 後片付け
    if writer:
        writer.release()
    if log_f :
        log_f.close()
    cv2.destroyAllWindows()

if __name__ == '__main__':
    sys.exit(main() or 0)