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### COPY ALL THE CODE INTO A JYPYTER NOTEBOOK ### | |
### THE JYPYTER NOTEBOOK NEEDS TO BE IN 'tensorflow\models\research\deeplab' ### | |
## Imports | |
import collections | |
import os | |
import io | |
import sys | |
import tarfile | |
import tempfile | |
import urllib | |
from IPython import display | |
from ipywidgets import interact | |
from ipywidgets import interactive | |
from matplotlib import gridspec | |
from matplotlib import pyplot as plt | |
import numpy as np | |
from PIL import Image | |
import cv2 | |
# import skvideo.io | |
import tensorflow as tf | |
if tf.__version__ < '1.5.0': | |
raise ImportError('Please upgrade your tensorflow installation to v1.5.0 or newer!') | |
# Needed to show segmentation colormap labels | |
sys.path.append('utils') | |
import get_dataset_colormap | |
## Select and download models | |
_MODEL_URLS = { | |
'xception_coco_voctrainaug': 'http://download.tensorflow.org/models/deeplabv3_pascal_train_aug_2018_01_04.tar.gz', | |
'xception_coco_voctrainval': 'http://download.tensorflow.org/models/deeplabv3_pascal_trainval_2018_01_04.tar.gz', | |
} | |
Config = collections.namedtuple('Config', 'model_url, model_dir') | |
def get_config(model_name, model_dir): | |
return Config(_MODEL_URLS[model_name], model_dir) | |
config_widget = interactive(get_config, model_name=_MODEL_URLS.keys(), model_dir='') | |
display.display(config_widget) | |
# Check configuration and download the model | |
_TARBALL_NAME = 'deeplab_model.tar.gz' | |
config = config_widget.result | |
model_dir = config.model_dir or tempfile.mkdtemp() | |
tf.gfile.MakeDirs(model_dir) | |
download_path = os.path.join(model_dir, _TARBALL_NAME) | |
print('downloading model to %s, this might take a while...' % download_path) | |
urllib.request.urlretrieve(config.model_url, download_path) | |
print('download completed!') | |
## Load model in TensorFlow | |
_FROZEN_GRAPH_NAME = 'frozen_inference_graph' | |
class DeepLabModel(object): | |
"""Class to load deeplab model and run inference.""" | |
INPUT_TENSOR_NAME = 'ImageTensor:0' | |
OUTPUT_TENSOR_NAME = 'SemanticPredictions:0' | |
INPUT_SIZE = 513 | |
def __init__(self, tarball_path): | |
"""Creates and loads pretrained deeplab model.""" | |
self.graph = tf.Graph() | |
graph_def = None | |
# Extract frozen graph from tar archive. | |
tar_file = tarfile.open(tarball_path) | |
for tar_info in tar_file.getmembers(): | |
if _FROZEN_GRAPH_NAME in os.path.basename(tar_info.name): | |
file_handle = tar_file.extractfile(tar_info) | |
graph_def = tf.GraphDef.FromString(file_handle.read()) | |
break | |
tar_file.close() | |
if graph_def is None: | |
raise RuntimeError('Cannot find inference graph in tar archive.') | |
with self.graph.as_default(): | |
tf.import_graph_def(graph_def, name='') | |
self.sess = tf.Session(graph=self.graph) | |
def run(self, image): | |
"""Runs inference on a single image. | |
Args: | |
image: A PIL.Image object, raw input image. | |
Returns: | |
resized_image: RGB image resized from original input image. | |
seg_map: Segmentation map of `resized_image`. | |
""" | |
width, height = image.size | |
resize_ratio = 1.0 * self.INPUT_SIZE / max(width, height) | |
target_size = (int(resize_ratio * width), int(resize_ratio * height)) | |
resized_image = image.convert('RGB').resize(target_size, Image.ANTIALIAS) | |
batch_seg_map = self.sess.run( | |
self.OUTPUT_TENSOR_NAME, | |
feed_dict={self.INPUT_TENSOR_NAME: [np.asarray(resized_image)]}) | |
seg_map = batch_seg_map[0] | |
return resized_image, seg_map | |
model = DeepLabModel(download_path) | |
## Webcam demo | |
cap = cv2.VideoCapture(0) | |
# Next line may need adjusting depending on webcam resolution | |
final = np.zeros((1, 384, 1026, 3)) | |
while True: | |
ret, frame = cap.read() | |
# From cv2 to PIL | |
cv2_im = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) | |
pil_im = Image.fromarray(cv2_im) | |
# Run model | |
resized_im, seg_map = model.run(pil_im) | |
# Adjust color of mask | |
seg_image = get_dataset_colormap.label_to_color_image( | |
seg_map, get_dataset_colormap.get_pascal_name()).astype(np.uint8) | |
# Convert PIL image back to cv2 and resize | |
frame = np.array(pil_im) | |
r = seg_image.shape[1] / frame.shape[1] | |
dim = (int(frame.shape[0] * r), seg_image.shape[1])[::-1] | |
resized = cv2.resize(frame, dim, interpolation = cv2.INTER_AREA) | |
resized = cv2.cvtColor(resized, cv2.COLOR_RGB2BGR) | |
# Stack horizontally color frame and mask | |
color_and_mask = np.hstack((resized, seg_image)) | |
cv2.imshow('frame', color_and_mask) | |
if cv2.waitKey(25) & 0xFF == ord('q'): | |
cap.release() | |
cv2.destroyAllWindows() | |
break | |
### UNCOMMENT NEXT LINES TO SAVE THE VIDEO ### | |
# output = np.expand_dims(both, axis=0) | |
# final = np.append(final, output, 0) | |
#skvideo.io.vwrite("outputvideo111.mp4", final) |
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Hey, I'm facing an error in line 133, while trying to convert BGRtoRGB.
installation/opencv-3.4.2/modules/imgproc/src/color.hpp:253: error: (-215:Assertion failed) VScn::contains(scn) && VDcn::contains(dcn) && VDepth::contains(depth) in function 'CvtHelper