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trigger_test.py
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trigger_test.py
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import tensorflow as tf
import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile
from distutils.version import StrictVersion
from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util
from object_detection.utils.label_map_util import create_category_index_from_labelmap
import cv2
import argparse
from util import get_labels
'''
for video =>
python detect_object_player.py --src "/home/musa/test.mp4" --train_model_path "/home/musa/custom_object_detector2/object_detection/models/research/object_detection/ssd_mobilenet_v1_coco_2017_11_17" --label_map_path "/home/musa/custom_object_detector2/object_detection/models/research/object_detection/data/mscoco_label_map.pbtxt"
for webcam =>
python detect_object_player.py --src "0" --train_model_path "/home/musa/custom_object_detector2/object_detection/models/research/object_detection/ssd_mobilenet_v1_coco_2017_11_17" --label_map_path "/home/musa/custom_object_detector2/object_detection/models/research/object_detection/data/mscoco_label_map.pbtxt"
'''
def create_session(fraction = 0.333):
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction = fraction)
return tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
def run_inference_for_single_image(sess, image, graph , tensor_dict):
if 'detection_masks' in tensor_dict:
# The following processing is only for single image
detection_boxes = tf.squeeze(tensor_dict['detection_boxes'], [0])
detection_masks = tf.squeeze(tensor_dict['detection_masks'], [0])
# Reframe is required to translate mask from box coordinates to image coordinates and fit the image size.
real_num_detection = tf.cast(tensor_dict['num_detections'][0], tf.int32)
detection_boxes = tf.slice(detection_boxes, [0, 0], [real_num_detection, -1])
detection_masks = tf.slice(detection_masks, [0, 0, 0], [real_num_detection, -1, -1])
detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(
detection_masks, detection_boxes, image.shape[0], image.shape[1])
detection_masks_reframed = tf.cast(
tf.greater(detection_masks_reframed, 0.5), tf.uint8)
# Follow the convention by adding back the batch dimension
tensor_dict['detection_masks'] = tf.expand_dims(
detection_masks_reframed, 0)
image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')
# Run inference
output_dict = sess.run(tensor_dict,
feed_dict={image_tensor: np.expand_dims(image, 0)})
# all outputs are float32 numpy arrays, so convert types as appropriate
output_dict['num_detections'] = int(output_dict['num_detections'][0])
output_dict['detection_classes'] = output_dict[
'detection_classes'][0].astype(np.uint8)
output_dict['detection_boxes'] = output_dict['detection_boxes'][0]
output_dict['detection_scores'] = output_dict['detection_scores'][0]
if 'detection_masks' in output_dict:
output_dict['detection_masks'] = output_dict['detection_masks'][0]
return output_dict
def detect_object_in( model_path , label_map_path , video_src):
PATH_TO_FROZEN_GRAPH = model_path + '/frozen_inference_graph.pb'
PATH_TO_LABELS = label_map_path
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(PATH_TO_FROZEN_GRAPH, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True)
with detection_graph.as_default():
with create_session() as sess:
# Get handles to input and output tensors
ops = tf.get_default_graph().get_operations()
all_tensor_names = {output.name for op in ops for output in op.outputs}
tensor_dict = {}
for key in [
'num_detections', 'detection_boxes', 'detection_scores',
'detection_classes', 'detection_masks'
]:
tensor_name = key + ':0'
if tensor_name in all_tensor_names:
tensor_dict[key] = tf.get_default_graph().get_tensor_by_name(tensor_name)
cap = cv2.VideoCapture(video_src)
video_running = True
while True:
ret , image_np = cap.read()
output_dict = run_inference_for_single_image(sess,image_np, detection_graph, tensor_dict)
detected_labels = get_labels(
output_dict['detection_boxes'],
output_dict['detection_classes'],
output_dict['detection_scores'],
category_index
)
print(detected_labels)
print("=================")
vis_util.visualize_boxes_and_labels_on_image_array(
image_np,
output_dict['detection_boxes'],
output_dict['detection_classes'],
output_dict['detection_scores'],
category_index,
instance_masks=output_dict.get('detection_masks'),
use_normalized_coordinates=True,
line_thickness=8)
cv_image = cv2.resize(image_np, (800, 600))
cv2.imshow("Press q to quit" , cv_image)
if cv2.waitKey(25) & 0xFF == ord("q"):
cap.release()
cv2.destroyAllWindows()
break
def test():
detect_object_in(
model_path = "/home/musa/custom_object_detector2/object_detection/models/research/object_detection/result3",
label_map_path = "/home/musa/custom_object_detector2/object_detection/models/research/object_detection/training/label_map.pbtxt",
video_src = "/home/musa/test.mp4")
def test2():
detect_object_in(
model_path = "/home/musa/custom_object_detector2/object_detection/models/research/object_detection/ssd_mobilenet_v1_coco_2017_11_17",
label_map_path = "/home/musa/custom_object_detector2/object_detection/models/research/object_detection/data/mscoco_label_map.pbtxt",
video_src = "/home/musa/test.mp4")
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--train_model_path', dest = "model_path" , required = True , type = str , help='the model path after generating inference graph')
parser.add_argument('--label_map_path', dest = "label_map_path" , required = True , type = str , help='path to the label map')
parser.add_argument('--src', dest = "src" , type = str, required = True , help='video src')
args = parser.parse_args()
video_src = args.src
model_path = args.model_path
label_map_path = args.label_map_path
try:
video_src = int(video_src)
except ValueError:
pass
detect_object_in(
model_path = model_path,
label_map_path = label_map_path,
video_src = video_src
)
if __name__ == "__main__":
test2()