from __future__ import division
import cv2
import numpy as np
import sys
import pickle
from optparse import OptionParser
import time
from keras_frcnn import config
from stacking import predict_stacked_model
from tensorflow.keras import backend as K
from tensorflow.keras.layers import Input
from tensorflow.keras.models import Model
from keras_frcnn import roi_helpers
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import os
import pandas as pd
import numpy as np

sys.setrecursionlimit(40000)

def main(testimages):
    config_output_filename = 'config.pickle'

    with open(config_output_filename, 'rb') as f_in:
        C = pickle.load(f_in)

    import keras_frcnn.resnet as nn

    # turn off any data augmentation at test time
    C.use_horizontal_flips = False
    C.use_vertical_flips = False
    C.rot_90 = False

    img_path = testimages

    def format_img_size(img, C):
        """ formats the image size based on config """
        img_min_side = float(C.im_size)
        (height, width, _) = img.shape

        if width <= height:
            ratio = img_min_side / width
            new_height = int(ratio * height)
            new_width = int(img_min_side)
        else:
            ratio = img_min_side / height
            new_width = int(ratio * width)
            new_height = int(img_min_side)
        img = cv2.resize(img, (new_width, new_height), interpolation=cv2.INTER_CUBIC)
        return img, ratio

    def format_img_channels(img, C):
        """ formats the image channels based on config """
        img = img[:, :, (2, 1, 0)]
        img = img.astype(np.float32)
        img[:, :, 0] -= C.img_channel_mean[0]
        img[:, :, 1] -= C.img_channel_mean[1]
        img[:, :, 2] -= C.img_channel_mean[2]
        img /= C.img_scaling_factor
        img = np.transpose(img, (2, 0, 1))
        img = np.expand_dims(img, axis=0)
        return img

    def format_img(img, C):
        """ formats an image for model prediction based on config """
        img, ratio = format_img_size(img, C)
        img = format_img_channels(img, C)
        return img, ratio

    # Method to transform the coordinates of the bounding box to its original size
    def get_real_coordinates(ratio, x1, y1, x2, y2):

        real_x1 = int(round(x1 // ratio))
        real_y1 = int(round(y1 // ratio))
        real_x2 = int(round(x2 // ratio))
        real_y2 = int(round(y2 // ratio))

        return (real_x1, real_y1, real_x2, real_y2)

    class_mapping = C.class_mapping

    if 'bg' not in class_mapping:
        class_mapping['bg'] = len(class_mapping)

    class_mapping = {v: k for k, v in class_mapping.items()}
    print(class_mapping)
    class_to_color = {class_mapping[v]: np.random.randint(0, 255, 3) for v in class_mapping}
    C.num_rois = int(32)

    num_features = 1024

    if K.image_data_format() == 'channels_first':
        input_shape_img = (3, None, None)
        input_shape_features = (num_features, None, None)
    else:
        input_shape_img = (None, None, 3)
        input_shape_features = (None, None, num_features)

    img_input = Input(shape=input_shape_img)
    roi_input = Input(shape=(C.num_rois, 4))
    feature_map_input = Input(shape=input_shape_features)

    # define the base network (resnet here, can be VGG, Inception, etc)
    shared_layers = nn.nn_base(img_input, trainable=True)

    # define the RPN, built on the base layers
    num_anchors = len(C.anchor_box_scales) * len(C.anchor_box_ratios)
    rpn_layers = nn.rpn(shared_layers, num_anchors)

    classifier = nn.classifier(feature_map_input, roi_input, C.num_rois, nb_classes=len(class_mapping), trainable=True)

    model_rpn = Model(img_input, rpn_layers)
    model_classifier_only = Model([feature_map_input, roi_input], classifier)

    model_classifier = Model([feature_map_input, roi_input], classifier)

    print('Loading weights from {}'.format(
        'E:/Document/DoAnTotNghiep/nqquang/nqquang/Project/model/model_frcnn.hdf5'))
    model_rpn.load_weights('model/model_frcnn.hdf5',
                           by_name=True)
    model_classifier.load_weights('model/model_frcnn.hdf5',
                                  by_name=True)

    model_rpn.compile(optimizer='sgd', loss='mse')
    model_classifier.compile(optimizer='sgd', loss='mse')

    all_imgs = []

    classes = {}

    bbox_threshold = 0.8

    visualise = True
    cnt = 0
    mild = []

    model = load_model('model/ensemble_model.h5', custom_objects=None, compile=False)
    filenames = os.listdir(testimages)
    for _ in filenames:
        mild.append(0)
    test_df = pd.DataFrame({'filename': filenames, 'data': mild})
    batch_size_test = 1

    test_datagen = ImageDataGenerator(rotation_range=10, width_shift_range=0.1, height_shift_range=0.1,
                                      brightness_range=[0.5, 1.5],
                                      shear_range=0.1, zoom_range=.1, fill_mode='nearest', rescale=1. / 255,
                                      horizontal_flip=True,
                                      vertical_flip=True)
    test_generator = test_datagen.flow_from_dataframe(test_df, directory=testimages, target_size=(224, 224),
                                                      x_col="filename",
                                                      y_col="data", class_mode="raw", shuffle=False,
                                                      batch_size=batch_size_test)
    count = len(filenames)
    preds = predict_stacked_model(model, test_generator, count)
    probs = []
    for i in range(count):
        pred = np.argmax(preds[i])
        probs.append(pred)
    test_df['level'] = probs

    plt.figure(figsize=(10,8))
    for index,row in test_df.iterrows():
        filename = row['filename']
        level = row['level']
        if not filename.lower().endswith(('.bmp', '.jpeg', '.jpg', '.png', '.tif', '.tiff')):
            continue
        st = time.time()
        filepath = os.path.join(img_path, filename)

        img = cv2.imread(filepath)

        X, ratio = format_img(img, C)

        if K.image_data_format() == 'channels_last':
            X = np.transpose(X, (0, 2, 3, 1))

        # get the feature maps and output from the RPN
        [Y1, Y2, F] = model_rpn.predict(X)

        R = roi_helpers.rpn_to_roi(Y1, Y2, C, K.image_data_format(), overlap_thresh=0.7)

        # convert from (x1,y1,x2,y2) to (x,y,w,h)
        R[:, 2] -= R[:, 0]
        R[:, 3] -= R[:, 1]

        # apply the spatial pyramid pooling to the proposed regions
        bboxes = {}
        probs = {}
        count_acne = 0

        for jk in range(R.shape[0] // C.num_rois + 1):
            ROIs = np.expand_dims(R[C.num_rois * jk:C.num_rois * (jk + 1), :], axis=0)
            if ROIs.shape[1] == 0:
                break

            if jk == R.shape[0] // C.num_rois:
                # pad R
                curr_shape = ROIs.shape
                target_shape = (curr_shape[0], C.num_rois, curr_shape[2])
                ROIs_padded = np.zeros(target_shape).astype(ROIs.dtype)
                ROIs_padded[:, :curr_shape[1], :] = ROIs
                ROIs_padded[0, curr_shape[1]:, :] = ROIs[0, 0, :]
                ROIs = ROIs_padded

            [P_cls, P_regr] = model_classifier_only.predict([F, ROIs])

            for ii in range(P_cls.shape[1]):

                if np.max(P_cls[0, ii, :]) < bbox_threshold or np.argmax(P_cls[0, ii, :]) == (P_cls.shape[2] - 1):
                    continue

                cls_name = class_mapping[np.argmax(P_cls[0, ii, :])]

                if cls_name not in bboxes:
                    bboxes[cls_name] = []
                    probs[cls_name] = []

                (x, y, w, h) = ROIs[0, ii, :]

                cls_num = np.argmax(P_cls[0, ii, :])
                try:
                    (tx, ty, tw, th) = P_regr[0, ii, 4 * cls_num:4 * (cls_num + 1)]
                    tx /= C.classifier_regr_std[0]
                    ty /= C.classifier_regr_std[1]
                    tw /= C.classifier_regr_std[2]
                    th /= C.classifier_regr_std[3]
                    x, y, w, h = roi_helpers.apply_regr(x, y, w, h, tx, ty, tw, th)
                except:
                    pass
                bboxes[cls_name].append(
                    [C.rpn_stride * x, C.rpn_stride * y, C.rpn_stride * (x + w), C.rpn_stride * (y + h)])
                probs[cls_name].append(np.max(P_cls[0, ii, :]))

        #all_dets = []

        for key in bboxes:
            bbox = np.array(bboxes[key])

            new_boxes, new_probs = roi_helpers.non_max_suppression_fast(bbox, np.array(probs[key]), overlap_thresh=0.5)
            for jk in range(new_boxes.shape[0]):
                (x1, y1, x2, y2) = new_boxes[jk, :]

                (real_x1, real_y1, real_x2, real_y2) = get_real_coordinates(ratio, x1, y1, x2, y2)

                cv2.rectangle(img, (real_x1, real_y1), (real_x2, real_y2), (255, 0, 0), 2)
                count_acne += 1

                # textLabel = '{}: {}'.format(key,int(100*new_probs[jk]))
                # all_dets.append((key,100*new_probs[jk]))

                # (retval,baseLine) = cv2.getTextSize(textLabel,cv2.FONT_HERSHEY_COMPLEX,1,1)
                # textOrg = (real_x1, real_y1-0)

                # cv2.rectangle(img, (textOrg[0] - 5, textOrg[1] + baseLine - 5),(textOrg[0] + retval[0] + 5, textOrg[1] - retval[1] - 5), (0, 0, 0), 2)
                # cv2.rectangle(img, (textOrg[0] - 5, textOrg[1] + baseLine - 5),(textOrg[0] + retval[0] + 5, textOrg[1] - retval[1] - 5), (255, 255, 255), -1)
                # cv2.putText(img, textLabel, textOrg, cv2.FONT_HERSHEY_DUPLEX, 1, (0, 0, 0), 1)

        # print(all_dets)
        # cv2.imshow('img', img)
        # cv2.waitKey(0)
        if(count%4!=0):
            cnt = count//4 + 1
        else:
            cnt = count//4
        plt.subplot(cnt,4,index+1)
        plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
        plt.axis('Off')
        if level==0:
            plt.title("(" + filename + ")" + 'mild: ' + str(count_acne) + ' acnes')
        elif level==1:
            plt.title("(" + filename + ")" + "moderate: " + str(count_acne) + ' acnes')
        elif level==2:
            plt.title("(" + filename + ")" + "severe: " + str(count_acne) + ' acnes')
        else:
            plt.title("(" + filename + ")" + "very severe: " + str(count_acne) + ' acnes')
        print('Elapsed time of ' + filename + ' = {}'.format(time.time() - st))

    plt.grid()
    plt.tight_layout()
    plt.show()