OneShot.py

# -*- coding: utf-8 -*-
"""Sia.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1CY9w2Y6dvTY9Rpi9B3nIpKzGId3e4SxR

#Run Always
Connect to your GDrive (with atleast 8 GB free space) and move to project directory
#Change accordingly
cd  drive/My Drive/*Directory with project in it *
"""

from google.colab import drive
drive.mount('/content/drive')

cd drive/My\ Drive/Colab Notebooks/

"""#Run Once
**Download Project Only once**

(If you don't have the folder siamese-mask-rcnn already)
"""

!git clone https://github.com/bethgelab/siamese-mask-rcnn.git

"""#Run Always
**Move to project folder**
"""

cd siamese-mask-rcnn

"""#Run Once 
Download COCO DATASET and COCO API 

Download pre-trained model
"""

cd data

!wget http://images.cocodataset.org/zips/val2017.zip
!unzip val2017 -d coco
!wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip
!unzip annotations_trainval2017.zip -d coco
!git clone https://github.com/cocodataset/cocoapi.git

cd ..

!mkdir checkpoints

cd checkpoints

!wget https://github.com/bethgelab/siamese-mask-rcnn/releases/download/v0.1/small_siamese_mrcnn_0160.h5

cd ..

"""#Run Always
**Install COCO API and Dependency**

From siamese-mask-rcnn folder
"""

! sudo pip install numpy scipy Pillow cython matplotlib scikit-image tensorflow==1.5.0 keras==2.1.6 opencv-python h5py

#(Run from siamese-mask-rcnn folder)
! (cd data/cocoapi/PythonAPI/ && sudo make install)

"""#Run Always
Import Libraries
"""

# Commented out IPython magic to ensure Python compatibility.
# %load_ext autoreload
# %autoreload 2
# %matplotlib inline
#%load_ext line_profiler

import tensorflow as tf
tf.logging.set_verbosity(tf.logging.INFO)
sess_config = tf.ConfigProto()

import sys
import os

COCO_DATA = 'data/coco/'
MASK_RCNN_MODEL_PATH = 'lib/Mask_RCNN/'

if MASK_RCNN_MODEL_PATH not in sys.path:
    sys.path.append(MASK_RCNN_MODEL_PATH)
    
from samples.coco import coco
from mrcnn import utils
from mrcnn import model as modellib
from mrcnn import visualize
    
from lib import utils as siamese_utils
from lib import model as siamese_model
from lib import config as siamese_config
   
import time
import datetime
import random
import numpy as np
import skimage.io
import imgaug
import pickle
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from collections import OrderedDict
from PIL import Image

# Root directory of the project
ROOT_DIR = os.getcwd()

# Directory to save logs and trained model
MODEL_DIR = os.path.join(ROOT_DIR, "logs")

coco_val = siamese_utils.IndexedCocoDataset()
coco_object = coco_val.load_coco(COCO_DATA, "val", year="2017", return_coco=True)
coco_val.prepare()
coco_val.build_indices()

"""#Small/Large"""

class SmallEvalConfig(siamese_config.Config):
    # Set batch size to 1 since we'll be running inference on
    # one image at a time. Batch size = GPU_COUNT * IMAGES_PER_GPU
    GPU_COUNT = 1
    IMAGES_PER_GPU = 1
    NUM_CLASSES = 1 + 1
    NAME = 'coco'
    EXPERIMENT = 'evaluation'
    CHECKPOINT_DIR = 'checkpoints/'
    NUM_TARGETS = 1
    
class LargeEvalConfig(siamese_config.Config):
    # Set batch size to 1 since we'll be running inference on
    # one image at a time. Batch size = GPU_COUNT * IMAGES_PER_GPU
    GPU_COUNT = 1
    IMAGES_PER_GPU = 1
    NUM_CLASSES = 1 + 1
    NAME = 'coco'
    EXPERIMENT = 'evaluation'
    CHECKPOINT_DIR = 'checkpoints/'
    NUM_TARGETS = 1
    
    # Large image sizes
    TARGET_MAX_DIM = 192
    TARGET_MIN_DIM = 150
    IMAGE_MIN_DIM = 800
    IMAGE_MAX_DIM = 1024
    # Large model size
    FPN_CLASSIF_FC_LAYERS_SIZE = 1024
    FPN_FEATUREMAPS = 256
    # Large number of rois at all stages
    RPN_ANCHOR_STRIDE = 1
    RPN_TRAIN_ANCHORS_PER_IMAGE = 256
    POST_NMS_ROIS_TRAINING = 2000
    POST_NMS_ROIS_INFERENCE = 1000
    TRAIN_ROIS_PER_IMAGE = 200
    DETECTION_MAX_INSTANCES = 100
    MAX_GT_INSTANCES = 100

"""#Download Large model to work with it"""

cd checkpoints

!wget https://github.com/bethgelab/siamese-mask-rcnn/releases/download/v0.2/large_siamese_mrcnn_coco_full_0320.h5

cd ..

"""#Select small or large"""

model_size = 'small' #or'small' # or 'large'

if model_size == 'small':
    config = SmallEvalConfig()
elif model_size == 'large':
    config = LargeEvalConfig()
    
config.display()

# Provide training schedule of the model
# When evaluationg intermediate steps the tranining schedule must be provided
train_schedule = OrderedDict()
if model_size == 'small':
    train_schedule[1] = {"learning_rate": config.LEARNING_RATE, "layers": "heads"}
    train_schedule[120] = {"learning_rate": config.LEARNING_RATE, "layers": "4+"}
    train_schedule[160] = {"learning_rate": config.LEARNING_RATE/10, "layers": "all"}
elif model_size == 'large':
    train_schedule[1] = {"learning_rate": config.LEARNING_RATE, "layers": "heads"}
    train_schedule[240] = {"learning_rate": config.LEARNING_RATE, "layers": "all"}
    train_schedule[320] = {"learning_rate": config.LEARNING_RATE/10, "layers": "all"}

if model_size == 'small':
    checkpoint = 'checkpoints/small_siamese_mrcnn_0160.h5'
elif model_size == 'large':
    checkpoint = 'checkpoints/large_siamese_mrcnn_coco_full_0320.h5'

model = siamese_model.SiameseMaskRCNN(mode="inference", model_dir=MODEL_DIR, config=config)
model.load_checkpoint(checkpoint, training_schedule=train_schedule)

"""#Important Cell
This will select any random image from siamese-mask-rcnn>data>coco>val2017
And gives output

This cell can be re-runs as many times as you want, it will give different result each time


Debug this and our project is done

#For large, target size should be (192,192)
#For small target size is (96,96)
"""

# Select category
category = 1
image_id = np.random.choice(coco_val.category_image_index[category])   

# Load target
#target = siamese_utils.get_one_target(category, coco_val, config)
from google.colab import files
from io import BytesIO
uploaded = files.upload()
for i in uploaded.keys():
  ti = uploaded[i]
  print(ti)
ti2 = Image.open(BytesIO(ti))
ti3 = ti2.resize((96, 96), Image.ANTIALIAS)
target=np.array(ti3)
imgplot=plt.imshow(target)
print(target.shape)
plt.show()
# Load image
#image = coco_val.load_image(image_id)
#print("image_id", image_id)
uploaded = files.upload()
for i in uploaded.keys():
  ti = uploaded[i]
image=np.array(Image.open(BytesIO(ti)))
imgplot = plt.imshow(image)
plt.show()


print(image.shape)
# Run detection
results = model.detect([[target]], [image], verbose=1)
r = results[0]
# Display results
siamese_utils.display_results(target, image, r['rois'], r['masks'], r['class_ids'], r['scores'])