In [1]:
import os
import cv2
import time
import argparse
import multiprocessing
import numpy as np
import tensorflow as tf
from matplotlib import pyplot as plt
%matplotlib inline
In [2]:
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util
In [3]:
CWD_PATH = os.getcwd()
# Path to frozen detection graph. This is the actual model that is used for the object detection.
MODEL_NAME = 'ssd_mobilenet_v1_coco_11_06_2017'
PATH_TO_CKPT = os.path.join(CWD_PATH, 'object_detection', MODEL_NAME, 'frozen_inference_graph.pb')
# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = os.path.join(CWD_PATH, 'object_detection', 'data', 'mscoco_label_map.pbtxt')
In [4]:
NUM_CLASSES = 90
# Loading label map
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)
In [5]:
def detect_objects(image_np, sess, detection_graph):
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
image_np_expanded = np.expand_dims(image_np, axis=0)
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
# Each box represents a part of the image where a particular object was detected.
boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
# Each score represent how level of confidence for each of the objects.
# Score is shown on the result image, together with the class label.
scores = detection_graph.get_tensor_by_name('detection_scores:0')
classes = detection_graph.get_tensor_by_name('detection_classes:0')
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
# Actual detection.
(boxes, scores, classes, num_detections) = sess.run(
[boxes, scores, classes, num_detections],
feed_dict={image_tensor: image_np_expanded})
# Visualization of the results of a detection.
vis_util.visualize_boxes_and_labels_on_image_array(
image_np,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index,
use_normalized_coordinates=True,
line_thickness=8)
return image_np
In [6]:
# First test on images
PATH_TO_TEST_IMAGES_DIR = 'object_detection/test_images'
TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(1, 3) ]
# Size, in inches, of the output images.
IMAGE_SIZE = (12, 8)
In [7]:
def load_image_into_numpy_array(image):
(im_width, im_height) = image.size
return np.array(image.getdata()).reshape(
(im_height, im_width, 3)).astype(np.uint8)
In [8]:
from PIL import Image
for image_path in TEST_IMAGE_PATHS:
image = Image.open(image_path)
image_np = load_image_into_numpy_array(image)
plt.imshow(image_np)
print(image.size, image_np.shape)
In [9]:
#Load a frozen TF model
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='')
In [10]:
with detection_graph.as_default():
with tf.Session(graph=detection_graph) as sess:
for image_path in TEST_IMAGE_PATHS:
image = Image.open(image_path)
image_np = load_image_into_numpy_array(image)
image_process = detect_objects(image_np, sess, detection_graph)
print(image_process.shape)
plt.figure(figsize=IMAGE_SIZE)
plt.imshow(image_process)
In [11]:
# Import everything needed to edit/save/watch video clips
from moviepy.editor import VideoFileClip
from IPython.display import HTML
In [12]:
def process_image(image):
# NOTE: The output you return should be a color image (3 channel) for processing video below
# you should return the final output (image with lines are drawn on lanes)
with detection_graph.as_default():
with tf.Session(graph=detection_graph) as sess:
image_process = detect_objects(image, sess, detection_graph)
return image_process
In [13]:
white_output = 'video1_out.mp4'
clip1 = VideoFileClip("video1.mp4").subclip(0,2)
white_clip = clip1.fl_image(process_image) #NOTE: this function expects color images!!s
%time white_clip.write_videofile(white_output, audio=False)
In [14]:
HTML("""
<video width="960" height="540" controls>
<source src="{0}">
</video>
""".format(white_output))
Out[14]:
In [15]:
white_output1 = 'video2_out.mp4'
clip1 = VideoFileClip("video2.mp4").subclip(0,2)
white_clip = clip1.fl_image(process_image) #NOTE: this function expects color images!!s
%time white_clip.write_videofile(white_output1, audio=False)
In [16]:
HTML("""
<video width="960" height="540" controls>
<source src="{0}">
</video>
""".format(white_output1))
Out[16]:
In [17]:
white_output3 = 'video3_out.mp4'
clip3 = VideoFileClip("video3.mp4").subclip(12,14)
white_clip = clip3.fl_image(process_image) #NOTE: this function expects color images!!s
%time white_clip.write_videofile(white_output3, audio=False)
In [18]:
HTML("""
<video width="960" height="540" controls>
<source src="{0}">
</video>
""".format(white_output3))
Out[18]:
In [19]:
# Merge videos
from moviepy.editor import VideoFileClip, concatenate_videoclips
clip1 = VideoFileClip("video1_out.mp4")
clip2 = VideoFileClip("video2_out.mp4")
clip3 = VideoFileClip("video3_out.mp4")
final_clip = concatenate_videoclips([clip1,clip2,clip3], method="compose")
final_clip.write_videofile("final_results.mp4",bitrate="5000k")
In [20]:
from moviepy.editor import *
clip = VideoFileClip("final_results.mp4")
clip.write_gif("final_results.gif")