Merge pull request #1 from MinMolang/MinMolang-patch-1

Create egohands_dataset_clean_4classes

egohands_dataset_clean_4classes

@@ -0,0 +1,243 @@
+import scipy.io as sio
+import numpy as np
+import os
+import gc
+import six.moves.urllib as urllib
+import cv2
+import time
+import xml.etree.cElementTree as ET
+import random
+import shutil as sh
+from shutil import copyfile
+import zipfile
+
+import csv
+
+
+def save_csv(csv_path, csv_content):
+    with open(csv_path, 'w') as csvfile:
+        wr = csv.writer(csvfile)
+        for i in range(len(csv_content)):
+            wr.writerow(csv_content[i])
+
+
+def get_bbox_visualize(base_path, dir):
+    image_path_array = []
+    for root, dirs, filenames in os.walk(base_path + dir):
+        for f in filenames:
+            if(f.split(".")[1] == "jpg"):
+                img_path = base_path + dir + "/" + f
+                image_path_array.append(img_path)
+
+    #sort image_path_array to ensure its in the low to high order expected in polygon.mat
+    image_path_array.sort()
+    boxes = sio.loadmat(
+        base_path + dir + "/polygons.mat")
+    # there are 100 of these per folder in the egohands dataset
+    polygons = boxes["polygons"][0] #polygons는 100개, boxes는 4개 , polygons는 100 *1 이어서 뒤에 인덱스를 1로 바꾸면 달라지 ㅁ
+    pointindex = 0
+    #classLabel
+    hands =  ['myleft' ,'myright', 'yourleft', 'yourright']
+    for first in polygons:
+        font = cv2.FONT_HERSHEY_SIMPLEX
+
+        img_id = image_path_array[pointindex]
+        img = cv2.imread(img_id)
+
+        img_params = {}
+        img_params["width"] = np.size(img, 1)
+        img_params["height"] = np.size(img, 0)
+        head, tail = os.path.split(img_id)
+        img_params["filename"] = tail
+        img_params["path"] = os.path.abspath(img_id)
+        img_params["type"] = "train"
+        pointindex += 1
+
+        boxarray = []
+        csvholder = []
+        cindex = 0  # for hands(label list) index
+        for pointlist in first:
+            # print(len(first)) :  #four
+            pst = np.empty((0, 2), int)
+            #print(np.shape(pst)) #(0,2)
+            max_x = max_y = min_x = min_y = height = width = 0
+
+            findex = 0
+
+            for point in pointlist:
+                if(len(point) == 2):
+
+                    x = int(point[0])
+                    y = int(point[1])
+                    #print('x', x)
+                    #print('y', y)
+
+                    if(findex == 0):
+                        min_x = x
+                        min_y = y
+
+                    findex += 1
+                    max_x = x if (x > max_x) else max_x
+                    min_x = x if (x < min_x) else min_x
+                    max_y = y if (y > max_y) else max_y
+                    min_y = y if (y < min_y) else min_y
+                    # print(index, "====", len(point))
+                    appeno = np.array([[x, y]])
+                    pst = np.append(pst, appeno, axis=0)
+                    #white dot hand edge
+                    cv2.putText(img, ".", (x, y), font, 0.7,
+                                (255, 255, 255), 2, cv2.LINE_AA)
+            #hold : for making hand blob
+            hold = {}
+            hold['minx'] = min_x
+            hold['miny'] = min_y
+            hold['maxx'] = max_x
+            hold['maxy'] = max_y
+
+            if (min_x > 0 and min_y > 0 and max_x > 0 and max_y > 0):
+                boxarray.append(hold)
+                # in canse of just hands class 4th parameters 'hands', but if myleft, myright, yourleft,yourright 4 classes : hands[cindex]
+                labelrow = [tail,
+                            np.size(img, 1), np.size(img, 0),hands[cindex], min_x, min_y, max_x, max_y]
+                csvholder.append(labelrow)
+
+            cv2.polylines(img, [pst], True, (0, 255, 255), 1)
+            cv2.rectangle(img, (min_x, max_y),
+                          (max_x, min_y), (0, 255, 0), 1)
+            cindex+=1 #for class change
+
+        csv_path = img_id.split(".")[0]
+        if not os.path.exists(csv_path + ".csv"):
+            cv2.putText(img, "DIR : " + dir + " - " + tail, (20, 50),
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.75, (77, 255, 9), 2)
+            cv2.imshow('Verifying annotation ', img)
+            save_csv(csv_path + ".csv", csvholder)
+            print("===== saving csv file for ", tail)
+        cv2.waitKey(2)  # close window when a key press is detected
+
+
+
+
+def create_directory(dir_path):
+    if not os.path.exists(dir_path):
+        os.makedirs(dir_path)
+
+# combine all individual csv files for each image into a single csv file per folder.
+
+
+def generate_label_files(image_dir):
+    header = ['filename', 'width', 'height',
+              'class', 'xmin', 'ymin', 'xmax', 'ymax']
+    for root, dirs, filenames in os.walk(image_dir):
+        for dir in dirs:
+            csvholder = []
+            csvholder.append(header)
+            loop_index = 0
+            for f in os.listdir(image_dir + dir):
+                if(f.split(".")[1] == "csv"):
+                    loop_index += 1
+                    #print(loop_index, f)
+                    csv_file = open(image_dir + dir + "/" + f, 'r')
+                    reader = csv.reader(csv_file)
+                    for row in reader:
+                        csvholder.append(row)
+                    csv_file.close()
+                    os.remove(image_dir + dir + "/" + f)
+            save_csv(image_dir + dir + "/" + dir + "_labels.csv", csvholder)
+            print("Saved label csv for ", dir, image_dir +
+                  dir + "/" + dir + "_labels.csv")
+
+
+# Split data, copy to train/test folders
+def split_data_test_eval_train(image_dir):
+    create_directory("images")
+    create_directory("images/train")
+    create_directory("images/test")
+
+    data_size = 4000
+    loop_index = 0
+    data_sampsize = int(0.1 * data_size)
+    test_samp_array = random.sample(range(data_size), k=data_sampsize)
+
+    for root, dirs, filenames in os.walk(image_dir):
+        for dir in dirs:
+            for f in os.listdir(image_dir + dir):
+                if(f.split(".")[1] == "jpg"):
+                    loop_index += 1
+                    print(loop_index, f)
+
+                    if loop_index in test_samp_array:
+                        os.rename(image_dir + dir +
+                                  "/" + f, "images/test/" + f)
+                        os.rename(image_dir + dir +
+                                  "/" + f.split(".")[0] + ".csv", "images/test/" + f.split(".")[0] + ".csv")
+                    else:
+                        os.rename(image_dir + dir +
+                                  "/" + f, "images/train/" + f)
+                        os.rename(image_dir + dir +
+                                  "/" + f.split(".")[0] + ".csv", "images/train/" + f.split(".")[0] + ".csv")
+                    print(loop_index, image_dir + f)
+            print(">   done scanning director ", dir)
+            os.remove(image_dir + dir + "/polygons.mat")
+            os.rmdir(image_dir + dir)
+
+        print("Train/test content generation complete!")
+        generate_label_files("images/")
+
+
+def generate_csv_files(image_dir):
+    for root, dirs, filenames in os.walk(image_dir):
+        for dir in dirs:
+            get_bbox_visualize(image_dir, dir)
+
+    print("CSV generation complete!\nGenerating train/test/eval folders")
+    split_data_test_eval_train("egohands/_LABELLED_SAMPLES/")
+
+
+# rename image files so we can have them all in a train/test/eval folder.
+def rename_files(image_dir):
+    print("Renaming files")
+    loop_index = 0
+    for root, dirs, filenames in os.walk(image_dir):
+        for dir in dirs:
+            for f in os.listdir(image_dir + dir):
+                if (dir not in f):
+                    if(f.split(".")[1] == "jpg"):
+                        loop_index += 1
+                        os.rename(image_dir + dir +
+                                  "/" + f, image_dir + dir +
+                                  "/" + dir + "_" + f)
+                else:
+                    break
+
+    generate_csv_files("egohands/_LABELLED_SAMPLES/")
+
+def extract_folder(dataset_path):
+    print("Egohands dataset already downloaded.\nGenerating CSV files")
+    if not os.path.exists("egohands"):
+        zip_ref = zipfile.ZipFile(dataset_path, 'r')
+        print("> Extracting Dataset files")
+        zip_ref.extractall("egohands")
+        print("> Extraction complete")
+        zip_ref.close()
+        rename_files("egohands/_LABELLED_SAMPLES/")
+
+def download_egohands_dataset(dataset_url, dataset_path):
+    is_downloaded = os.path.exists(dataset_path)
+    if not is_downloaded:
+        print(
+            "> downloading egohands dataset. This may take a while (1.3GB, say 3-5mins). Coffee break?")
+        opener = urllib.request.URLopener()
+        opener.retrieve(dataset_url, dataset_path)
+        print("> download complete")
+        extract_folder(dataset_path);
+
+    else:
+        extract_folder(dataset_path)
+
+
+EGOHANDS_DATASET_URL = "http://vision.soic.indiana.edu/egohands_files/egohands_data.zip"
+EGO_HANDS_FILE = "egohands_data.zip"
+
+# download_egohands_dataset(EGOHANDS_DATASET_URL, EGO_HANDS_FILE)
+extract_folder(EGO_HANDS_FILE)