mask

# import the necessary packages
from tkinter import W

import cv2
import os, os.path
import numpy as np

# debug info OpenCV version
print("OpenCV version: " + cv2.__version__)

# image path and valid extensions
imageDir = "/home/eden/data/VOCdevkit/VOC2007/JPEGImages"  # specify your path here
image_path_list = []
valid_image_extensions = [".jpg", ".jpeg", ".png", ".tif", ".tiff"]  # specify your valid extensions here
valid_image_extensions = [item.lower() for item in valid_image_extensions]  # sorting endings

# create a list of all the images in directory and
# append images with a vaild extention to image_path_list
for file in os.listdir(imageDir):
    extension = os.path.splitext(file)[1]
    if extension.lower() not in valid_image_extensions:
        continue
    image_path_list.append(os.path.join(imageDir, file))
image_path_list = sorted(image_path_list)

# loop through image_path_list to open each image
for imagePath in image_path_list:
    image = cv2.imread(imagePath)
    # resize the image blur it and convert it to HSV color space
    # image =imutils.resize(image, width=500, height=375)
    blurred = cv2.GaussianBlur(image, (5, 5), 0)
    hsv = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)

    # define the lower and upper boundaries in the HSV coordiantes.
    lower = np.array([90, 85, 0])
    upper = np.array([120, 255, 255])

    # define a filtering kernel
    kernel = np.ones((5, 5), np.uint8)

    # construct a mask for the desired color range, then perform a series of
    # erosions and dilations (or opening and closing) to remove any small noise left
    mask = cv2.inRange(hsv, lower, upper)
    mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)  # opening
    mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)  # closing
    res = cv2.bitwise_and(image, image, mask=mask)

    # find contours in the mask and initialize the current
    # (x, y) center of the objects
    cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,
                            cv2.CHAIN_APPROX_SIMPLE)[-2]
    center = None

    # only proceed if at least one contour was found
    if len(cnts) > 0:
        for i in range(len(cnts)):
            x, y, w, h = cv2.boundingRect(cnts[i])  # retrieves a bounding rectangle pixel locations
            M = cv2.moments(cnts[i])  # the contour moments
            center = (int(M["m10"] / M["m00"]), int(M["m01"] / M["m00"]))  # x and Y center of mass
            # only proceed if the rectangle meets a minimum size
            # only look in the bottom third part of the image, overlook the car
            if 250 < center[1] < 350:
                print(w,h)
                if 20 < w < 50 and 20 < h < 40:
                    # draw the box and centroid on the image
                    cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
                       #annotate the bounding box to a new file
                    up_x = x
                    up_y = y
                    down_x = x + w
                    down_y = y + h
                    with open(imagePath +".txt","a+")  as dataFile:
                        dataFile.write(" ".join([" cone",str(up_x),str(up_y),str(down_x),str(down_y)]))
#
    # #show the resulted image (and optionally mask and res)
    # cv2.imshow('image', image)
    # #     # cv2.imshow('mask',mask)
    # #     # cv2.imshow('res',res)
    # # #    #0 = wait indefinitely
    # # #    #exit when escape key is pressed
    # key = cv2.waitKey(0)
    # if key == 27:  # escape
    #     break
    # #
    # #    # close any open windows
    # cv2.destroyAllWindows()
#
# xml_files = []
# #get the text files
# def get_txt_files(path):
#     for file in os.listdir(path):
#         extension = os.path.splitext(file)[1]
#         if extension == ".txt":
#             xml_files.append(os.path.join(path, file))
#         else:
#             continue
#
# get_txt_files

# xml_files = sorted(xml_files)
# #the xml format
# def create_xml(text,name):
#     xmls = ['<annotation>\n', '\t<folder>VOC2007</folder>\n', '\t<filename>000026.jpg</filename>\n', '\t<source>\n', '\t\t<database>The VOC2007 Database</database>\n', '\t\t<annotation>PASCAL VOC2007</annotation>\n', '\t\t<image>flickr</image>\n', '\t\t<flickrid>192073981</flickrid>\n', '\t</source>\n', '\t<owner>\n', '\t\t<flickrid>tobeng</flickrid>\n', '\t\t<name>kfir_yotam</name>\n', '\t</owner>\n', '\t<size>\n', '\t\t<width>1280</width>\n', '\t\t<height>720</height>\n', '\t\t<depth>3</depth>\n', '\t</size>\n', '\t<segmented>0</segmented>\n', '\t<object>\n', '\t\t<name>car</name>\n', '\t\t<pose>unspecified</pose>\n', '\t\t<truncated>0</truncated>\n', '\t\t<difficult>0</difficult>\n', '\t\t<bndbox>\n', '\t\t\t<xmin>90</xmin>\n', '\t\t\t<ymin>125</ymin>\n', '\t\t\t<xmax>337</xmax>\n', '\t\t\t<ymax>212</ymax>\n', '\t\t</bndbox>\n', '\t</object>\n', '</annotation>\n']
#     annotation_data = text.read()
#     file_name = name
#     object_name = "cone"
#     x_min = annotation_data.split(' ')[2]
#     y_min = annotation_data.split(' ')[3]
#     x_max = annotation_data.split(' ')[4]
#     y_max = annotation_data.split(' ')[5]
#     xmls[2]= '\t<filename>'+file_name+'</filename>\n'
#     xmls[20]= '\t\t<name>'+object_name+'</name>\n'
#     xmls[25]= '\t\t\t<xmin>'+x_min+'</xmin>\n'
#     xmls[26]= '\t\t\t<ymin>'+y_min+'</ymin>\n'
#     xmls[27]= '\t\t\t<xmax>'+x_max+'</xmax>\n'
#     xmls[28]= '\t\t\t<ymax>'+y_max+'</ymax>\n'
#     return xmls
#
# #write a list of XML to file
# def write_to_file(xmls,file_name):
#     f = open(file_name,'w')
#     for line in xmls:
#         f.write(line)
#     f.close()
#
# def convert(file_name, text):
#     xmls = create_xml(text,name)
#     write_to_file(xmls,file_name.split('.')[0]+'.xml')
# #loop through .txt files and create .xml
# for j in range(len(xml_files)):
#     text = open(xml_files[j],'r')
#     name = os.path.basename(xml_files[j])
#     name = os.path.splitext(name)[0]
#     convert(name, text)
#
#