#!/usr/bin/env python

import cv2, re, glob
import numpy             as np
import matplotlib.pyplot as plt
from itertools import izip

""" Convert numPy matrices with rectangles and confidences to sorted list of detections."""
def convert2detections(rects, confs, crop_factor = 0.125):
    if rects is None:
        return []

    dts = zip(*[rects.tolist(), confs.tolist()])
    dts = zip(dts[0][0], dts[0][1])
    dts = [Detection(r,c) for r, c in dts]

    dts.sort(lambda x, y : -1  if (x.conf - y.conf) > 0 else 1)

    for dt in dts:
        dt.crop(crop_factor)

    return dts

""" Create new instance of soft cascade."""
def cascade(min_scale, max_scale, nscales, f):
    # where we use nms cv::SCascade::DOLLAR == 2
    c = cv2.SCascade(min_scale, max_scale, nscales, 2)
    xml = cv2.FileStorage(f, 0)
    dom = xml.getFirstTopLevelNode()
    assert c.load(dom)
    return c

""" Compute prefix sum for en array"""
def cumsum(n):
    cum = []
    y = 0
    for i in n:
        y += i
        cum.append(y)
    return cum

""" Compute x and y arrays for ROC plot"""
def computeROC(confidenses, tp, nannotated, nframes, ignored):
    confidenses, tp, ignored = zip(*sorted(zip(confidenses, tp, ignored), reverse = True))

    fp = [(1 - x) for x in tp]
    fp = [(x - y) for x, y in izip(fp, ignored)]

    fp = cumsum(fp)
    tp = cumsum(tp)
    miss_rate = [(1 - x / (nannotated + 0.000001)) for x in tp]
    fppi = [x / float(nframes) for x in fp]

    return fppi, miss_rate

""" Crop rectangle by factor"""
def crop_rect(rect, factor):
    val_x = factor * float(rect[2])
    val_y = factor * float(rect[3])
    x = [int(rect[0] + val_x), int(rect[1] + val_y), int(rect[2] - 2.0 * val_x), int(rect[3] - 2.0 * val_y)]
    return x

"""Initialize plot axises"""
def initPlot(name = "ROC curve Bahnhof"):

    fig, ax = plt.subplots()
    fig.canvas.draw()

    plt.xlabel("fppi")
    plt.ylabel("miss rate")
    plt.title(name)
    plt.grid(True)
    plt.xscale('log')
    plt.yscale('log')

"""Show resulted plot"""
def showPlot(file_name):
    # plt.savefig(file_name)
    plt.axis((pow(10, -3), pow(10, 1), 0.0, 1))
    plt.yticks( [0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.64, 0.8, 1], ['.05', '.10', '.20', '.30', '.40', '.50', '.64', '.80', '1'] )
    plt.show()

def match(gts, dts):
    # Cartesian product for each detection BB_dt with each BB_gt
    overlaps = [[dt.overlap(gt) for gt in gts]for dt in dts]

    matches_gt     = [0]*len(gts)
    matches_dt     = [0]*len(dts)
    matches_ignore = [0]*len(dts)

    for idx, row in enumerate(overlaps):
        imax = row.index(max(row))

        # try to match ground thrush
        if (matches_gt[imax] == 0 and row[imax] > 0.5):
            matches_gt[imax] = 1
            matches_dt[idx]  = 1

    for idx, dt in enumerate(dts):
        # try to math ignored
        if matches_dt[idx] == 0:
            row = gts
            row = [i for i in row if (i[3] - i[1]) < 53 or (i[3] - i[1]) >  256]
            for each in row:
                if dts[idx].overlapIgnored(each) > 0.5:
                    matches_ignore[idx] = 1
    return matches_dt, matches_ignore


def plotLogLog(fppi, miss_rate, c):
    print
    plt.loglog(fppi, miss_rate, color = c, linewidth = 2)


def draw_rects(img, rects, color, l = lambda x, y : x + y):
    if rects is not None:
        for x1, y1, x2, y2 in rects:
            cv2.rectangle(img, (x1, y1), (l(x1, x2), l(y1, y2)), color, 2)

def draw_dt(img, dts, color, l = lambda x, y : x + y):
    if dts is not None:
        for dt in dts:
            bb = dt.bb
            x1, y1, x2, y2 = dt.bb[0], dt.bb[1], dt.bb[2], dt.bb[3]

            cv2.rectangle(img, (x1, y1), (l(x1, x2), l(y1, y2)), color, 2)

class Annotation:
    def __init__(self, bb):
        self.bb = bb

class Detection:
    def __init__(self, bb, conf):
        self.bb = bb
        self.conf = conf
        self.matched = False

    def crop(self, factor):
        self.bb = crop_rect(self.bb, factor)

    # we use rect-style for dt and box style for gt. ToDo: fix it
    def overlap(self, b):

        a = self.bb
        w = min( a[0] + a[2], b[2]) - max(a[0], b[0]);
        h = min( a[1] + a[3], b[3]) - max(a[1], b[1]);

        cross_area = 0.0 if (w < 0 or h < 0) else float(w * h)
        union_area = (a[2] * a[3]) + ((b[2] - b[0]) * (b[3] - b[1])) - cross_area;

        return cross_area / union_area

        # we use rect-style for dt and box style for gt. ToDo: fix it
    def overlapIgnored(self, b):

        a = self.bb
        w = min( a[0] + a[2], b[2]) - max(a[0], b[0]);
        h = min( a[1] + a[3], b[3]) - max(a[1], b[1]);

        cross_area = 0.0 if (w < 0 or h < 0) else float(w * h)
        self_area = (a[2] * a[3]);

        return cross_area / self_area

    def mark_matched(self):
        self.matched = True


def parse_inria(ipath, f):
    bbs = []
    path = None
    for l in f:
        box = None
        if l.startswith("Bounding box"):
            b = [x.strip() for x in l.split(":")[1].split("-")]
            c = [x[1:-1].split(",") for x in b]
            d = [int(x) for x in sum(c, [])]
            bbs.append(d)

        if l.startswith("Image filename"):
            path = l.split('"')[-2]

    return Sample(path, bbs)

def glob_set(pattern):
    return [__n for __n in glob.iglob(pattern)] #glob.iglob(pattern)

# parse ETH idl file
def parse_idl(f):
    map = {}
    for l in open(f):
        l = re.sub(r"^\"left\/", "{\"", l)
        l = re.sub(r"\:", ":[", l)
        l = re.sub(r"(\;|\.)$", "]}", l)
        map.update(eval(l))
    return map

def norm_box(box, ratio):
    middle = float(box[0] + box[2]) / 2.0
    new_half_width = float(box[3] - box[1]) * ratio / 2.0
    return (int(round(middle - new_half_width)), box[1], int(round(middle + new_half_width)), box[3])


def norm_acpect_ratio(boxes, ratio):
    return [ norm_box(box, ratio)  for box in boxes]