# -*- coding:utf-8 -*-
# ------------------------
# written by <NAME>
# 2018-10
# ------------------------

import os
import skimage.io
from skimage.color import rgb2gray
from scipy.io import loadmat
import numpy as np
import cv2
import math
import warnings
import random
import h5py
import scipy
warnings.filterwarnings("ignore")
root = "../../data/shtu_dataset"


def gaussian_kernel(image, points):
    if image.shape[-1] == 3:
        image = rgb2gray(image)
    image_density = np.zeros(image.shape)
    h, w = image_density.shape

    if len(points) == 0:
        return image_density

    for j in range(len(points)):
        f_sz = 15
        sigma = 4.0
        # convert x, y to int
        x = min(w, max(0, int(points[j, 0])))
        y = min(h, max(0, int(points[j, 1])))
        gap = f_sz // 2

        x1 = x - gap if x - gap > 0 else 0
        x2 = x + gap if x + gap < w else w - 1
        y1 = y - gap if y - gap > 0 else 0
        y2 = y + gap if y + gap < h else h - 1
        # generate 2d gaussian kernel
        kx = cv2.getGaussianKernel(y2 - y1 + 1, sigma=sigma)
        ky = cv2.getGaussianKernel(x2 - x1 + 1, sigma=sigma)
        gaussian = np.multiply(kx, ky.T)

        image_density[y1:y2 + 1, x1:x2 + 1] += gaussian

    return image_density


def gaussian_filter_density(gt):
    density = np.zeros(gt.shape, dtype=np.float32)
    gt_count = np.count_nonzero(gt)
    if gt_count == 0:
        return density
    pts = np.array(list(zip(np.nonzero(gt)[1], np.nonzero(gt)[0])))
    leafsize = 2048
    # build kdtree
    tree = scipy.spatial.KDTree(pts.copy(), leafsize=leafsize)
    # query kdtree
    distances, locations = tree.query(pts, k=4)

    print('generate density...')
    for i, pt in enumerate(pts):
        pt2d = np.zeros(gt.shape, dtype=np.float32)
        pt2d[math.floor(pt[1]), math.floor(pt[0])] = 1.
        if gt_count > 1:
            sigma = (distances[i][1]+distances[i][2]+distances[i][3])*0.1
        else:
            sigma = np.average(np.array(gt.shape))/2./2. #case: 1 point
        density += scipy.ndimage.filters.gaussian_filter(pt2d, sigma, mode='constant')
    print('done.')
    return density


def extract_data(mode="train", patch_number=9, part="A"):
    num_images = 300 if mode == "train" else 182
    # original path
    dataset_path = os.path.join(root, "original/part_A_final/train_data")
    images_path = os.path.join(dataset_path, "images")
    gt_path = os.path.join(dataset_path, "ground_truth")
    # preprocessed path
    preprocessed_image = os.path.join(root, "preprocessed/train/")
    preprocessed_density = os.path.join(root, "preprocessed/train_density/")

    # convert images to gray-density for each
    for index in range(1, num_images + 1):
        if index % 10 == 9:
            print("{0} images have been processed".format(index + 1))
        image_path = os.path.join(images_path, "IMG_{0}.jpg".format(index))
        ground_truth_path = os.path.join(gt_path, "GT_IMG_{0}.mat".format(index))
        image = skimage.io.imread(image_path)
        # convert to gray map
        mat = loadmat(ground_truth_path)
        image_info = mat["image_info"]
        ann_points = image_info[0][0][0][0][0]
        k = np.zeros((image.shape[0], image.shape[1]))
        for i in range(0, len(ann_points)):
            if int(ann_points[i][1] < image.shape[0] and int(ann_points[i][0] < image.shape[1])):
                k[int(ann_points[i][1]), int(ann_points[i][0])] = 1
        image_density = gaussian_filter_density(k)
        # split image into 9 patches where patch is 1/4 size
        h = image.shape[0]
        w = image.shape[1]
        w_block = math.floor(w / 4)
        h_block = math.floor(h / 4)
        # get 0-3
        for j in range(4):
            # 0, 1, 2, 3
            x = j % 2
            y = j // 2
            w_b = w // 2
            h_b = h // 2
            image_sample = image[y * h_b:(y + 1) * h_b, x * w_b:(x + 1) * w_b]
            image_density_sample = image_density[y * h_b:(y + 1) * h_b, x * w_b:(x + 1) * w_b]
            img_idx = "{0}_{1}".format(index, j)
            skimage.io.imsave(os.path.join(preprocessed_image, "{0}.jpg".format(img_idx)), image_sample)
            with h5py.File(os.path.join(preprocessed_density, "{0}.h5".format(img_idx))) as hf:
                hf['density'] = image_density_sample
        for j in range(4, patch_number):
            x = math.floor((w - 2 * w_block) * random.random() + w_block)
            y = math.floor((h - 2 * h_block) * random.random() + h_block)
            image_sample = image[y - h_block:y + h_block, x - w_block:x + w_block]
            image_density_sample = image_density[y - h_block:y + h_block, x - w_block:x + w_block]
            img_idx = "{0}_{1}".format(index, j)
            skimage.io.imsave(os.path.join(preprocessed_image, "{0}.jpg".format(img_idx)), image_sample)
            with h5py.File(os.path.join(preprocessed_density, "{0}.h5".format(img_idx))) as hf:
                hf['density'] = image_density_sample


def extract_test_data(part="A"):
    num_images = 183 if part == "A" else 317
    test_data_path = os.path.join(root, "original/part_{part}_final/test_data/images".format(part=part))
    test_ground_path = os.path.join(root, "original/part_{part}_final/test_data/ground_truth".format(part=part))
    test_density_path = os.path.join(root, "preprocessed/test_density")
    print("begin to preprocess test data........")
    for index in range(133, num_images):
        if index % 10 == 0:
            print("{num} images are done".format(num=index))
        image_path = os.path.join(test_data_path, "IMG_{0}.jpg".format(index))
        ground_truth_path = os.path.join(test_ground_path, "GT_IMG_{0}.mat".format(index))
        # load mat and image
        image = skimage.io.imread(image_path)
        mat = loadmat(ground_truth_path)
        image_info = mat["image_info"]
        ann_points = image_info[0][0][0][0][0]
        k = np.zeros((image.shape[0], image.shape[1]))
        for i in range(0, len(ann_points)):
            if int(ann_points[i][1] < image.shape[0] and int(ann_points[i][0] < image.shape[1])):
                k[int(ann_points[i][1]), int(ann_points[i][0])] = 1
        image_density = gaussian_filter_density(k)
        with h5py.File(os.path.join(test_density_path, "IMG_{0}.h5".format(index))) as hf:
            hf['density'] = image_density


extract_data()
extract_test_data()
# -*- coding:utf-8 -*-
# ------------------------
# written by <NAME>
# 2018-10
# ------------------------

import os
import skimage.io
from skimage.color import rgb2gray
from scipy.io import loadmat
import numpy as np
import cv2
import math
import warnings
import random
import h5py
import scipy
warnings.filterwarnings("ignore")
root = "../../data/shtu_dataset"


def gaussian_kernel(image, points):
    if image.shape[-1] == 3:
        image = rgb2gray(image)
    image_density = np.zeros(image.shape)
    h, w = image_density.shape

    if len(points) == 0:
        return image_density

    for j in range(len(points)):
        f_sz = 15
        sigma = 4.0
        # convert x, y to int
        x = min(w, max(0, int(points[j, 0])))
        y = min(h, max(0, int(points[j, 1])))
        gap = f_sz // 2

        x1 = x - gap if x - gap > 0 else 0
        x2 = x + gap if x + gap < w else w - 1
        y1 = y - gap if y - gap > 0 else 0
        y2 = y + gap if y + gap < h else h - 1
        # generate 2d gaussian kernel
        kx = cv2.getGaussianKernel(y2 - y1 + 1, sigma=sigma)
        ky = cv2.getGaussianKernel(x2 - x1 + 1, sigma=sigma)
        gaussian = np.multiply(kx, ky.T)

        image_density[y1:y2 + 1, x1:x2 + 1] += gaussian

    return image_density


def gaussian_filter_density(gt):
    density = np.zeros(gt.shape, dtype=np.float32)
    gt_count = np.count_nonzero(gt)
    if gt_count == 0:
        return density
    pts = np.array(list(zip(np.nonzero(gt)[1], np.nonzero(gt)[0])))
    leafsize = 2048
    # build kdtree
    tree = scipy.spatial.KDTree(pts.copy(), leafsize=leafsize)
    # query kdtree
    distances, locations = tree.query(pts, k=4)

    print('generate density...')
    for i, pt in enumerate(pts):
        pt2d = np.zeros(gt.shape, dtype=np.float32)
        pt2d[math.floor(pt[1]), math.floor(pt[0])] = 1.
        if gt_count > 1:
            sigma = (distances[i][1]+distances[i][2]+distances[i][3])*0.1
        else:
            sigma = np.average(np.array(gt.shape))/2./2. #case: 1 point
        density += scipy.ndimage.filters.gaussian_filter(pt2d, sigma, mode='constant')
    print('done.')
    return density


def extract_data(mode="train", patch_number=9, part="A"):
    num_images = 300 if mode == "train" else 182
    # original path
    dataset_path = os.path.join(root, "original/part_A_final/train_data")
    images_path = os.path.join(dataset_path, "images")
    gt_path = os.path.join(dataset_path, "ground_truth")
    # preprocessed path
    preprocessed_image = os.path.join(root, "preprocessed/train/")
    preprocessed_density = os.path.join(root, "preprocessed/train_density/")

    # convert images to gray-density for each
    for index in range(1, num_images + 1):
        if index % 10 == 9:
            print("{0} images have been processed".format(index + 1))
        image_path = os.path.join(images_path, "IMG_{0}.jpg".format(index))
        ground_truth_path = os.path.join(gt_path, "GT_IMG_{0}.mat".format(index))
        image = skimage.io.imread(image_path)
        # convert to gray map
        mat = loadmat(ground_truth_path)
        image_info = mat["image_info"]
        ann_points = image_info[0][0][0][0][0]
        k = np.zeros((image.shape[0], image.shape[1]))
        for i in range(0, len(ann_points)):
            if int(ann_points[i][1] < image.shape[0] and int(ann_points[i][0] < image.shape[1])):
                k[int(ann_points[i][1]), int(ann_points[i][0])] = 1
        image_density = gaussian_filter_density(k)
        # split image into 9 patches where patch is 1/4 size
        h = image.shape[0]
        w = image.shape[1]
        w_block = math.floor(w / 4)
        h_block = math.floor(h / 4)
        # get 0-3
        for j in range(4):
            # 0, 1, 2, 3
            x = j % 2
            y = j // 2
            w_b = w // 2
            h_b = h // 2
            image_sample = image[y * h_b:(y + 1) * h_b, x * w_b:(x + 1) * w_b]
            image_density_sample = image_density[y * h_b:(y + 1) * h_b, x * w_b:(x + 1) * w_b]
            img_idx = "{0}_{1}".format(index, j)
            skimage.io.imsave(os.path.join(preprocessed_image, "{0}.jpg".format(img_idx)), image_sample)
            with h5py.File(os.path.join(preprocessed_density, "{0}.h5".format(img_idx))) as hf:
                hf['density'] = image_density_sample
        for j in range(4, patch_number):
            x = math.floor((w - 2 * w_block) * random.random() + w_block)
            y = math.floor((h - 2 * h_block) * random.random() + h_block)
            image_sample = image[y - h_block:y + h_block, x - w_block:x + w_block]
            image_density_sample = image_density[y - h_block:y + h_block, x - w_block:x + w_block]
            img_idx = "{0}_{1}".format(index, j)
            skimage.io.imsave(os.path.join(preprocessed_image, "{0}.jpg".format(img_idx)), image_sample)
            with h5py.File(os.path.join(preprocessed_density, "{0}.h5".format(img_idx))) as hf:
                hf['density'] = image_density_sample


def extract_test_data(part="A"):
    num_images = 183 if part == "A" else 317
    test_data_path = os.path.join(root, "original/part_{part}_final/test_data/images".format(part=part))
    test_ground_path = os.path.join(root, "original/part_{part}_final/test_data/ground_truth".format(part=part))
    test_density_path = os.path.join(root, "preprocessed/test_density")
    print("begin to preprocess test data........")
    for index in range(133, num_images):
        if index % 10 == 0:
            print("{num} images are done".format(num=index))
        image_path = os.path.join(test_data_path, "IMG_{0}.jpg".format(index))
        ground_truth_path = os.path.join(test_ground_path, "GT_IMG_{0}.mat".format(index))
        # load mat and image
        image = skimage.io.imread(image_path)
        mat = loadmat(ground_truth_path)
        image_info = mat["image_info"]
        ann_points = image_info[0][0][0][0][0]
        k = np.zeros((image.shape[0], image.shape[1]))
        for i in range(0, len(ann_points)):
            if int(ann_points[i][1] < image.shape[0] and int(ann_points[i][0] < image.shape[1])):
                k[int(ann_points[i][1]), int(ann_points[i][0])] = 1
        image_density = gaussian_filter_density(k)
        with h5py.File(os.path.join(test_density_path, "IMG_{0}.h5".format(index))) as hf:
            hf['density'] = image_density


extract_data()
extract_test_data()
# -*- coding:utf-8 -*- # ------------------------ # written by <NAME> # 2018-10 # ------------------------ # convert x, y to int # generate 2d gaussian kernel # build kdtree # query kdtree #case: 1 point # original path # preprocessed path # convert images to gray-density for each # convert to gray map # split image into 9 patches where patch is 1/4 size # get 0-3 # 0, 1, 2, 3 # load mat and image