image_detection.py

######## Image Object Detection Using Tensorflow-trained Classifier #########
#
# Author: Evan Juras
# Date: 1/15/18
# Description: 
# This program uses a TensorFlow-trained neural network to perform object detection.
# It loads the classifier and uses it to perform object detection on an image.
# It draws boxes, scores, and labels around the objects of interest in the image.

## Some of the code is copied from Google's example at
## https://github.com/tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb

## and some is copied from Dat Tran's example at
## https://github.com/datitran/object_detector_app/blob/master/object_detection_app.py

## but I changed it to make it more understandable to me.

# Import packages
import os
import cv2
import numpy as np
import tensorflow as tf
import sys

# This is needed since the notebook is stored in the object_detection folder.
sys.path.append("..")

# Import utilites
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util

# Name of the directory containing the object detection module we're using
MODEL_NAME = 'inference_graph'
IMAGE_NAME = '4Shrimp.jpg'

# Grab path to current working directory
CWD_PATH = os.getcwd()

# Path to frozen detection graph .pb file, which contains the model that is used
# for object detection.
PATH_TO_CKPT = os.path.join(CWD_PATH,MODEL_NAME,'frozen_inference_graph.pb')

# Path to label map file
PATH_TO_LABELS = os.path.join(CWD_PATH,'training','label_map.pbtxt')

# Path to image
PATH_TO_IMAGE = os.path.join(CWD_PATH,'images/test/',IMAGE_NAME)

# Number of classes the object detector can identify
NUM_CLASSES = 1

# Load the label map.
# Label maps map indices to category names, so that when our convolution
# network predicts `5`, we know that this corresponds to `king`.
# Here we use internal utility functions, but anything that returns a
# dictionary mapping integers to appropriate string labels would be fine
label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
category_index = label_map_util.create_category_index(categories)

# Load the Tensorflow model into memory.
detection_graph = tf.Graph()
with detection_graph.as_default():
    od_graph_def = tf.GraphDef()
    with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
        serialized_graph = fid.read()
        od_graph_def.ParseFromString(serialized_graph)
        tf.import_graph_def(od_graph_def, name='')

    sess = tf.Session(graph=detection_graph)

# Define input and output tensors (i.e. data) for the object detection classifier

# Input tensor is the image
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')

# Output tensors are the detection boxes, scores, and classes
# Each box represents a part of the image where a particular object was detected
detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')

# Each score represents level of confidence for each of the objects.
# The score is shown on the result image, together with the class label.
detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')

# Number of objects detected
num_detections = detection_graph.get_tensor_by_name('num_detections:0')

# Load image using OpenCV and
# expand image dimensions to have shape: [1, None, None, 3]
# i.e. a single-column array, where each item in the column has the pixel RGB value
image = cv2.imread(PATH_TO_IMAGE)
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image_expanded = np.expand_dims(image_rgb, axis=0)

# Perform the actual detection by running the model with the image as input
(boxes, scores, classes, num) = sess.run(
    [detection_boxes, detection_scores, detection_classes, num_detections],
    feed_dict={image_tensor: image_expanded})

# Draw the results of the detection (aka 'visulaize the results')

minimum_score = 0.80

vis_util.visualize_boxes_and_labels_on_image_array(
    image,
    np.squeeze(boxes),
    np.squeeze(classes).astype(np.int32),
    np.squeeze(scores),
    category_index,
    use_normalized_coordinates=True,
    line_thickness=8,
    min_score_thresh=minimum_score)


# For contour care (Not use yet, because can do this function with detected value)
"""
img_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
ret, img_binary = cv2.threshold(img_gray, 200, 255, 0)
contours, hierarchy = cv2.findContours(img_binary, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)

for c in contours:
    c = c.astype("float")
    c = c.astype("int")
    x,y,w,h = cv2.boundingRect(c)

    if not (200 < w <800 and 100 < h < 600):
        continue
    crop_img = image[y:y+h, x:x+w]
    print("-----------------------------------------")
    print("Detected width: ", w, "px")
    print("Detected height: ", h, "px")
    print("-----------------------------------------")
    cv2.imshow("cropped", crop_img)
    cv2.waitKey(0)
    cv2.destroyAllWindows()
"""


#All the results have been drawn on image. Now display the image.
cv2.imshow('Shrimp detector', image)

# Press any key to close the image
cv2.waitKey(0)

# Clean up
cv2.destroyAllWindows()

image_height, image_width, whatIsThis = image.shape

for i in range(0, len(boxes[0])):
    if minimum_score > scores[0][i]:
        continue

    boxInfo = boxes[0][i]
    h_start = int(round(image_height * boxInfo[0]))
    h_end = int(round(image_height * boxInfo[2]))
    w_start = int(round(image_width * boxInfo[1]))
    w_end = int(round(image_width * boxInfo[3]))

    print("--- Object #", i+1, "---------------------------------")
    print("- width:  ", w_end - w_start)
    print("- height: ", h_end - h_start)
    print("- score:  ", scores[0][i]*100,"(%)")
    print("------------------------------------------------")

    crop_image = image[h_start:h_end, w_start:w_end]
    cv2.imshow("crop_image", crop_image)
    cv2.waitKey()
    cv2.destroyAllWindows()