app.py

import gradio as gr
import numpy as np
import os
import tensorflow as tf
import pathlib
import json
import matplotlib.pyplot as plt
from datetime import datetime
from matplotlib import pyplot as plt
from PIL import Image
import io
import pathlib

Image.MAX_IMAGE_PIXELS = None

RoboflowAPI = os.environ['RoboflowAPI']
RoboflowCocoAPI = os.environ['RoboflowCocoAPI']

if "models" in pathlib.Path.cwd().parts:
  while "models" in pathlib.Path.cwd().parts:
    os.chdir('..')
elif not pathlib.Path('models').exists():
  os.system('git clone --depth 1 https://github.com/tensorflow/models')
os.chdir('models/research/')
os.system('protoc object_detection/protos/*.proto --python_out=.')
os.system('cp object_detection/packages/tf2/setup.py .')
os.system('python -m pip install .')
from object_detection.utils import ops as utils_ops
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_utils
# patch tf1 into `utils.ops`
utils_ops.tf = tf.compat.v1

# Patch the location of gfile
tf.gfile = tf.io.gfile
os.system('python object_detection/builders/model_builder_tf2_test.py')
def load_model(model_dir):
  model = tf.saved_model.load(str(model_dir))
  model = model.signatures['serving_default']
  return model
if "Tortoise" not in os.listdir():
  os.system('mkdir "Tortoise"')
  os.chdir('Tortoise/')
  os.system(f'curl -L "https://app.roboflow.com/ds/{RoboflowAPI}" > roboflow.zip; unzip roboflow.zip; rm roboflow.zip')
  os.chdir('..')
  os.system('mkdir "COCO"')
  os.chdir('COCO/')
  os.system(f'curl -L "https://app.roboflow.com/ds/{RoboflowCocoAPI}" > roboflow.zip; unzip roboflow.zip; rm roboflow.zip')
  os.chdir('..')

PATH_TO_TEST_IMAGES_DIR = pathlib.Path("COCO" + '/test/')
TEST_IMAGE_PATHS = sorted(list(PATH_TO_TEST_IMAGES_DIR.glob("*.jpg")))
dataset = 'Tortoise'
test_record_fname = dataset + '/test/tortoise.tfrecord'
train_record_fname = dataset + '/train/tortoise.tfrecord'
label_map_pbtxt_fname = dataset + '/train/tortoise_label_map.pbtxt'
PATH_TO_LABELS = dataset + '/train/tortoise_label_map.pbtxt'
category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=False)


test_data_json = 'COCO/test/_annotations.coco.json'
with open(test_data_json, 'r') as f:
  test_metadata = json.load(f)
for im in test_metadata['images']:
            im['date_captured'] = str(datetime.strptime(im['file_name'][6:21],"%Y%m%d-%H%M%S"))

image_id_to_datetime = {im['id']:im['date_captured'] for im in test_metadata['images']}
image_path_to_id = {im['file_name']: im['id'] 
                    for im in test_metadata['images']}

faster_rcnn_model = load_model('../../Faster RCNN/saved_model')

def run_inference_for_single_image(model, image):
  '''Run single image through tensorflow object detection saved_model.

  This function runs a saved_model on a (single) provided image and returns
  inference results in numpy arrays.

  Args:
    model: tensorflow saved_model. This model can be obtained using 
      export_inference_graph.py.
    image: uint8 numpy array with shape (img_height, img_width, 3)

  Returns:
    output_dict: a dictionary holding the following entries:
      `num_detections`: an integer
      `detection_boxes`: a numpy (float32) array of shape [N, 4]
      `detection_classes`: a numpy (uint8) array of shape [N]
      `detection_scores`: a numpy (float32) array of shape [N]
      `detection_features`: a numpy (float32) array of shape [N, 7, 7, 2048]
  '''
  image = np.asarray(image)
  # The input needs to be a tensor, convert it using `tf.convert_to_tensor`.
  input_tensor = tf.convert_to_tensor(image)
  # The model expects a batch of images, so add an axis with `tf.newaxis`.
  input_tensor = input_tensor[tf.newaxis,...]

  # Run inference
  output_dict = model(input_tensor)
  # All outputs are batches tensors.
  # Convert to numpy arrays, and take index [0] to remove the batch dimension.
  # We're only interested in the first num_detections.
  num_dets = output_dict.pop('num_detections')
  num_detections = int(num_dets)
  for key,value in output_dict.items():
    output_dict[key] = value[0, :num_detections].numpy() 
  output_dict['num_detections'] = num_detections

  # detection_classes should be ints.
  output_dict['detection_classes'] = output_dict['detection_classes'].astype(
      np.int64)
  return output_dict

def embed_date_captured(date_captured):
  """Encodes the datetime of the image.

  Takes a datetime object and encodes it into a normalized embedding of shape 
  [5], using hard-coded normalization factors for year, month, day, hour,
  minute.

  Args:
    date_captured: A datetime object.

  Returns:
    A numpy float32 embedding of shape [5].
  """
  embedded_date_captured = []
  month_max = 12.0
  day_max = 31.0
  hour_max = 24.0
  minute_max = 60.0
  min_year = 1990.0
  max_year = 2030.0

  year = (date_captured.year-min_year)/float(max_year-min_year)
  embedded_date_captured.append(year)

  month = (date_captured.month-1)/month_max
  embedded_date_captured.append(month)

  day = (date_captured.day-1)/day_max
  embedded_date_captured.append(day)

  hour = date_captured.hour/hour_max
  embedded_date_captured.append(hour)

  minute = date_captured.minute/minute_max
  embedded_date_captured.append(minute)

  return np.asarray(embedded_date_captured)

def embed_position_and_size(box):
  """Encodes the bounding box of the object of interest.

  Takes a bounding box and encodes it into a normalized embedding of shape 
  [4] - the center point (x,y) and width and height of the box.

  Args:
    box: A bounding box, formatted as [ymin, xmin, ymax, xmax].

  Returns:
    A numpy float32 embedding of shape [4].
  """
  ymin = box[0]
  xmin = box[1]
  ymax = box[2]
  xmax = box[3]
  w = xmax - xmin
  h = ymax - ymin
  x = xmin + w / 2.0
  y = ymin + h / 2.0
  return np.asarray([x, y, w, h])

def get_context_feature_embedding(date_captured, detection_boxes,
                                  detection_features, detection_scores):
  """Extracts representative feature embedding for a given input image.

  Takes outputs of a detection model and focuses on the highest-confidence
  detected object. Starts with detection_features and uses average pooling to
  remove the spatial dimensions, then appends an embedding of the box position
  and size, and an embedding of the date and time the image was captured,
  returning a one-dimensional representation of the object.

  Args:
    date_captured: A datetime string of format '%Y-%m-%d %H:%M:%S'.
    detection_features: A numpy (float32) array of shape [N, 7, 7, 2048].
    detection_boxes: A numpy (float32) array of shape [N, 4].
    detection_scores: A numpy (float32) array of shape [N].

  Returns:
    A numpy float32 embedding of shape [2057].
  """
  date_captured = datetime.strptime(date_captured,'%Y-%m-%d %H:%M:%S')
  temporal_embedding = embed_date_captured(date_captured)
  embedding = detection_features[0]
  pooled_embedding = np.mean(np.mean(embedding, axis=1), axis=0)
  box = detection_boxes[0]
  position_embedding = embed_position_and_size(box)
  bb_embedding = np.concatenate((pooled_embedding, position_embedding))
  embedding = np.expand_dims(np.concatenate((bb_embedding,temporal_embedding)),
                             axis=0)
  score = detection_scores[0]
  return embedding, score

def run_inference(model, image_path, date_captured, resize_image=True):
  """Runs inference over a single input image and extracts contextual features.

  Args:
    model: A tensorflow saved_model object.
    image_path: Absolute path to the input image.
    date_captured: A datetime string of format '%Y-%m-%d %H:%M:%S'.
    resize_image: Whether to resize the input image before running inference.

  Returns:
    context_feature: A numpy float32 array of shape [2057].
    score: A numpy float32 object score for the embedded object.
    output_dict: The saved_model output dictionary for the image.
  """
  with open(image_path,'rb') as f:
    image = Image.open(f)
    if resize_image:
      image.thumbnail((640,640),Image.LANCZOS)
    image_np = np.array(image)

  # Actual detection.
  output_dict = run_inference_for_single_image(model, image_np)

  context_feature, score = get_context_feature_embedding(
      date_captured, output_dict['detection_boxes'],
      output_dict['detection_features'], output_dict['detection_scores'])
  return context_feature, score, output_dict

import posixpath
context_features = []
scores = []
faster_rcnn_results = {}
for image_path in TEST_IMAGE_PATHS:
    head,tail = posixpath.split(image_path)
    image_id = image_path_to_id[str(tail)]
    date_captured = image_id_to_datetime[image_id]
    context_feature, score, results = run_inference(
        faster_rcnn_model, image_path, date_captured)
    faster_rcnn_results[image_id] = results
    context_features.append(context_feature)
    scores.append(score)

# Concatenate all extracted context embeddings into a contextual memory bank.
context_features_matrix = np.concatenate(context_features, axis=0)
context_rcnn_model = load_model('../../Context RCNN/saved_model')
context_padding_size = 2000

def run_context_rcnn_inference_for_single_image(
    model, image, context_features, context_padding_size):
  '''Run single image through a Context R-CNN saved_model.

  This function runs a saved_model on a (single) provided image and provided 
  contextual features and returns inference results in numpy arrays.

  Args:
    model: tensorflow Context R-CNN saved_model. This model can be obtained
      using export_inference_graph.py and setting side_input fields. 
      Example export call - 
        python export_inference_graph.py \
          --input_type image_tensor \
          --pipeline_config_path /path/to/context_rcnn_model.config \
          --trained_checkpoint_prefix /path/to/context_rcnn_model.ckpt \
          --output_directory /path/to/output_dir \
          --use_side_inputs True \
          --side_input_shapes 1,2000,2057/1 \
          --side_input_names context_features,valid_context_size \
          --side_input_types float,int \
          --input_shape 1,-1,-1,3

    image: uint8 numpy array with shape (img_height, img_width, 3)
    context_features: A numpy float32 contextual memory bank of shape 
      [num_context_examples, 2057]
    context_padding_size: The amount of expected padding in the contextual
      memory bank, defined in the Context R-CNN config as 
      max_num_context_features.

  Returns:
    output_dict: a dictionary holding the following entries:
      `num_detections`: an integer
      `detection_boxes`: a numpy (float32) array of shape [N, 4]
      `detection_classes`: a numpy (uint8) array of shape [N]
      `detection_scores`: a numpy (float32) array of shape [N]
  '''
  image = np.asarray(image)
  # The input image needs to be a tensor, convert it using 
  # `tf.convert_to_tensor`.
  image_tensor = tf.convert_to_tensor(
      image, name='image_tensor')[tf.newaxis,...]

  context_features = np.asarray(context_features)
  valid_context_size = context_features.shape[0]
  valid_context_size_tensor = tf.convert_to_tensor(
      valid_context_size, name='valid_context_size')[tf.newaxis,...]
  padded_context_features = np.pad(
      context_features,
      ((0,context_padding_size-valid_context_size),(0,0)), mode='constant')
  padded_context_features_tensor = tf.convert_to_tensor(
      padded_context_features,
      name='context_features',
      dtype=tf.float32)[tf.newaxis,...]

  # Run inference
  output_dict = model(
      inputs=image_tensor,
      context_features=padded_context_features_tensor,
      valid_context_size=valid_context_size_tensor)
  # All outputs are batches tensors.
  # Convert to numpy arrays, and take index [0] to remove the batch dimension.
  # We're only interested in the first num_detections.
  num_dets = output_dict.pop('num_detections')
  num_detections = int(num_dets)
  for key,value in output_dict.items():
    output_dict[key] = value[0, :num_detections].numpy() 
  output_dict['num_detections'] = num_detections

  # detection_classes should be ints.
  output_dict['detection_classes'] = output_dict['detection_classes'].astype(np.int64)
  return output_dict
def show_context_rcnn_inference(
    model, image_path, context_features, faster_rcnn_output_dict,
    context_padding_size, resize_image=True):
  """Runs inference over a single input image and visualizes Faster R-CNN vs. 
  Context R-CNN results.

  Args:
    model: A tensorflow saved_model object.
    image_path: Absolute path to the input image.
    context_features: A numpy float32 contextual memory bank of shape 
      [num_context_examples, 2057]
    faster_rcnn_output_dict: The output_dict corresponding to this input image
      from the single-frame Faster R-CNN model, which was previously used to
      build the memory bank.
    context_padding_size: The amount of expected padding in the contextual
      memory bank, defined in the Context R-CNN config as 
      max_num_context_features.
    resize_image: Whether to resize the input image before running inference.

  Returns:
    context_rcnn_image_np: Numpy image array showing Context R-CNN Results.
    faster_rcnn_image_np: Numpy image array showing Faster R-CNN Results.
  """

  # the array based representation of the image will be used later in order to prepare the
  # result image with boxes and labels on it.
  with open(image_path,'rb') as f:
    image = Image.open(f)
    if resize_image:
      image.thumbnail((640,640),Image.LANCZOS)
    image_np = np.array(image)
    image.thumbnail((400,400),Image.LANCZOS)
    context_rcnn_image_np = np.array(image)
    
  faster_rcnn_image_np = np.copy(context_rcnn_image_np)

  # Actual detection.
  output_dict = run_context_rcnn_inference_for_single_image(
      model, image_np, context_features, 2000)

  # Visualization of the results of a context_rcnn detection.
  vis_utils.visualize_boxes_and_labels_on_image_array(
      context_rcnn_image_np,
      output_dict['detection_boxes'],
      output_dict['detection_classes'],
      output_dict['detection_scores'],
      category_index,
      use_normalized_coordinates=True,
      line_thickness=2)
  
  # Visualization of the results of a faster_rcnn detection.
  vis_utils.visualize_boxes_and_labels_on_image_array(
      faster_rcnn_image_np,
      faster_rcnn_output_dict['detection_boxes'],
      faster_rcnn_output_dict['detection_classes'],
      faster_rcnn_output_dict['detection_scores'],
      category_index,
      use_normalized_coordinates=True,
      line_thickness=2)
  return context_rcnn_image_np, faster_rcnn_image_np


def segment(image):
  
  plt.rcParams['axes.grid'] = False
  plt.rcParams['xtick.labelsize'] = False
  plt.rcParams['ytick.labelsize'] = False
  plt.rcParams['xtick.top'] = False
  plt.rcParams['xtick.bottom'] = False
  plt.rcParams['ytick.left'] = False
  plt.rcParams['ytick.right'] = False
  plt.rcParams['figure.figsize'] = [7.5,5]
  date_captured = datetime.strptime(Image.open(image.name)._getexif()[36867], '%Y:%m:%d %H:%M:%S').strftime('%Y-%m-%d %H:%M:%S')
  context_feature, score, results = run_inference(
      faster_rcnn_model, image.name, date_captured)
  faster_rcnn_output_dict = results
  context_rcnn_image, faster_rcnn_image = show_context_rcnn_inference(
      context_rcnn_model, image.name, context_features_matrix,
      faster_rcnn_output_dict, context_padding_size)
  plt.subplot(1,2,1, frameon=False)
  plt.imshow(faster_rcnn_image)
  plt.title('Faster R-CNN')
  plt.subplot(1,2,2, frameon=False)
  plt.imshow(context_rcnn_image)
  plt.title('Context R-CNN')
  buf = io.BytesIO()
  plt.savefig(buf, dpi=1600, bbox_inches='tight')
  buf.seek(0)
  img = Image.open(buf)
  return img
examples = os.listdir('../../Examples')
examples = ['../../Examples/' + item for item in examples]
title="Context R-CNN"
description=f'<p class="has-line-data" data-line-start="0" data-line-end="1">Gradio demo for <strong>Context R-CNN</strong>: <a href="https://arxiv.org/abs/1912.03538">[Paper]</a>.</p><p class="has-line-data" data-line-start="2" data-line-end="3">Context R-CNN is an object detection algorithm that uses contextual features to improve object detection. It is based on Faster R-CNN, but it adds a module that can incorporate contextual features from surrounding frames. This allows Context R-CNN to better identify objects that are partially obscured or that are moving quickly.</p><p class="has-line-data" data-line-start="4" data-line-end="5">The contextual features are stored in a memory bank, which is built up over time as the camera captures images. The memory bank is indexed using an attention mechanism, which allows Context R-CNN to focus on the most relevant contextual features for each object.</p><p class="has-line-data" data-line-start="6" data-line-end="7">Context R-CNN has been shown to improve object detection performance on a variety of datasets, including camera trap data and traffic camera data. It is a promising approach for improving object detection in static monitoring cameras, where the sampling rate is low and the objects may exhibit long-term behavior.</p><p class="has-line-data" data-line-start="8" data-line-end="9">This application of Context R-CNN demonstrates its potential for use in camera trap images of Gopher Tortoises in the wild. It also shows how Context R-CNN can improve object detection performance over existing Faster R-CNN implementations. Both models of R-CNN were trained on the exact same datasets for best comparison. Context R-CNN improves upon Faster R-CNN by building a contextual memory bank, such contextual information can include the position of other objects in the scene, the motion of the objects, and the time of day. The contextual feature matrix used by Context R-CNN model was build using Faster R-CNN model.</p><p class="has-line-data" data-line-start="11" data-line-end="12"><strong>The examples images provided in this demo were not used to train or test the models.</strong></p><p class="has-line-data" data-line-start="13" data-line-end="14">Note: The model requires the date taken attribute to be present in the metadata of the uploaded images in order to process them correctly.</p></br>Training instructions for Context R-CNN can be found on this <a href="https://github.com/prakrutpatel/Context-RCNN-Tortoises">Github</a>'
gr.Interface(fn=segment, inputs = "file",outputs = gr.Image(type="pil", width=50, height=20) ,title=title, description=description ,examples=examples,cache_examples=True).launch()