amankharwal/landmark_detection.py Secret

## landmark_detection.py
from sklearn.preprocessing import LabelEncoder
lencoder = LabelEncoder()
lencoder.fit(df["landmark_id"])

def encode_label(lbl):
    return lencoder.transform(lbl)

def decode_label(lbl):
    return lencoder.inverse_transform(lbl)

def get_image_from_number(num):
    fname, label = df.loc[num,:]
    fname = fname + ".jpg"
    f1 = fname[0]
    f2 = fname[1]
    f3 = fname[2]
    path = os.path.join(f1,f2,f3,fname)
    im = cv2.imread(os.path.join(base_path,path))
    return im, label

print("4 sample images from random classes:")
fig=plt.figure(figsize=(16, 16))
for i in range(1,5):
    a = random.choices(os.listdir(base_path), k=3)
    folder = base_path+'/'+a[0]+'/'+a[1]+'/'+a[2]
    random_img = random.choice(os.listdir(folder))
    img = np.array(Image.open(folder+'/'+random_img))
    fig.add_subplot(1, 4, i)
    plt.imshow(img)
    plt.axis('off')

plt.show()
	from sklearn.preprocessing import LabelEncoder
	lencoder = LabelEncoder()
	lencoder.fit(df["landmark_id"])

	def encode_label(lbl):
	return lencoder.transform(lbl)

	def decode_label(lbl):
	return lencoder.inverse_transform(lbl)

	def get_image_from_number(num):
	fname, label = df.loc[num,:]
	fname = fname + ".jpg"
	f1 = fname[0]
	f2 = fname[1]
	f3 = fname[2]
	path = os.path.join(f1,f2,f3,fname)
	im = cv2.imread(os.path.join(base_path,path))
	return im, label

	print("4 sample images from random classes:")
	fig=plt.figure(figsize=(16, 16))
	for i in range(1,5):
	a = random.choices(os.listdir(base_path), k=3)
	folder = base_path+'/'+a[0]+'/'+a[1]+'/'+a[2]
	random_img = random.choice(os.listdir(folder))
	img = np.array(Image.open(folder+'/'+random_img))
	fig.add_subplot(1, 4, i)
	plt.imshow(img)
	plt.axis('off')

	plt.show()