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Author: siddharthoza

display_all_gestures.py

@@ -0,0 +1,42 @@
+import cv2, os, random
+import numpy as np
+
+def get_image_size():
+	img = cv2.imread('gestures/0/100.jpg', 0)
+	return img.shape
+
+gestures = os.listdir('gestures/')
+gestures.sort(key = int)
+begin_index = 0
+end_index = 5
+image_x, image_y = get_image_size()
+
+if len(gestures)%5 != 0:
+	rows = int(len(gestures)/5)+1
+else:
+	rows = int(len(gestures)/5)
+
+full_img = None
+for i in range(rows):
+	col_img = None
+	for j in range(begin_index, end_index):
+		img_path = "gestures/%s/%d.jpg" % (j, random.randint(1, 1200))
+		img = cv2.imread(img_path, 0)
+		if np.any(img == None):
+			img = np.zeros((image_y, image_x), dtype = np.uint8)
+		if np.any(col_img == None):
+			col_img = img
+		else:
+			col_img = np.hstack((col_img, img))
+
+	begin_index += 5
+	end_index += 5
+	if np.any(full_img == None):
+		full_img = col_img
+	else:
+		full_img = np.vstack((full_img, col_img))
+
+
+cv2.imshow("gestures", full_img)
+cv2.imwrite('full_img.jpg', full_img)
+cv2.waitKey(0)

load_images.py

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+import cv2
+from glob import glob
+import numpy as np
+import random
+from sklearn.utils import shuffle
+import pickle
+import os
+
+def pickle_images_labels():
+	images_labels = []
+	images = glob("gestures/*/*.jpg")
+	images.sort()
+	for image in images:
+		print(image)
+		label = image[image.find(os.sep)+1: image.rfind(os.sep)]
+		img = cv2.imread(image, 0)
+		images_labels.append((np.array(img, dtype=np.uint8), int(label)))
+	return images_labels
+
+images_labels = pickle_images_labels()
+images_labels = shuffle(shuffle(shuffle(shuffle(images_labels))))
+images, labels = zip(*images_labels)
+print("Length of images_labels", len(images_labels))
+
+train_images = images[:int(5/6*len(images))]
+print("Length of train_images", len(train_images))
+with open("train_images", "wb") as f:
+	pickle.dump(train_images, f)
+del train_images
+
+train_labels = labels[:int(5/6*len(labels))]
+print("Length of train_labels", len(train_labels))
+with open("train_labels", "wb") as f:
+	pickle.dump(train_labels, f)
+del train_labels
+
+test_images = images[int(5/6*len(images)):int(11/12*len(images))]
+print("Length of test_images", len(test_images))
+with open("test_images", "wb") as f:
+	pickle.dump(test_images, f)
+del test_images
+
+test_labels = labels[int(5/6*len(labels)):int(11/12*len(images))]
+print("Length of test_labels", len(test_labels))
+with open("test_labels", "wb") as f:
+	pickle.dump(test_labels, f)
+del test_labels
+
+val_images = images[int(11/12*len(images)):]
+print("Length of test_images", len(val_images))
+with open("val_images", "wb") as f:
+	pickle.dump(val_images, f)
+del val_images
+
+val_labels = labels[int(11/12*len(labels)):]
+print("Length of val_labels", len(val_labels))
+with open("val_labels", "wb") as f:
+	pickle.dump(val_labels, f)
+del val_labels