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ball_detection.py
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import numpy as np
import time
import os
import tensorflow as tf
from PIL import Image
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_util
import cv2
# requires, tf-gpu 1.12, cuda 9.0, cuDNN v7.1.4, driver 390
class BallDetection:
def __init__(self):
self.now = time.time()
self.cwd = os.getcwd()
self.num_classes = 1
# Path to frozen detection graph. This is the actual model that is used for the object detection.
self.path_to_ckpt = self.cwd + '/content/datalab/fine_tuned_model' + '/frozen_inference_graph.pb'
# List of the strings that is used to add correct label for each box.
self.path_to_labels = self.cwd + '/content/datalab' + '/label_map.pbtxt'
self.test_image_path = self.cwd + '/content/datalab/test_image/image1.png'
# Size, in inches, of the output images.
self.output_img_size = (12, 8)
self.label_map = label_map_util.load_labelmap(self.path_to_labels)
self.categories = label_map_util.convert_label_map_to_categories(self.label_map, \
max_num_classes=self.num_classes,
use_display_name=True)
self.category_index = label_map_util.create_category_index(self.categories)
self.detection_graph = tf.Graph()
self.load_detection_graph()
self.tf_session = tf.Session(graph=self.detection_graph)
def load_image_into_numpy_array(self, image):
(im_width, im_height) = image.size
return np.array(image.getdata()).reshape(
(im_height, im_width, 3)).astype(np.uint8)
def run_inference_for_single_image(self, image):
with self.detection_graph.as_default():
# 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)
if 'detection_masks' in tensor_dict: # find out what his is for
# 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')
# print(time.time() - self.now)
# Run inference
with self.tf_session.as_default():
output_dict = self.tf_session.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]
# print(time.time() - self.now)
return output_dict
def load_detection_graph(self):
with self.detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(self.path_to_ckpt, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
def visualize_detection(self, output_dict, image_np):
vis_util.visualize_boxes_and_labels_on_image_array(
image_np,
output_dict['detection_boxes'],
output_dict['detection_classes'],
output_dict['detection_scores'],
self.category_index,
instance_masks=output_dict.get('detection_masks'),
use_normalized_coordinates=True,
line_thickness=8)
# cv2.imshow('image', image_np)
# cv2.waitKey(0)
im = Image.fromarray(image_np)
im.save(self.cwd + '/content/datalab/test_image/image_det.jpg')
# cv2.destroyAllWindows()
# print(time.time() - self.now)
def set_up_object_detection_api(self):
# print("starting object detection...")
# self.now = time.time()
test_image = Image.open(self.test_image_path)
# the array based representation of the image will be used later in order to prepare the
# result image with boxes and labels on it.
image_np = self.load_image_into_numpy_array(test_image)
# Actual detection.
output_dict = self.run_inference_for_single_image(image_np)
# get actual dimensions of the test image
width, height = test_image.size
# find the center of the object detection box
box_norm_coords = output_dict['detection_boxes'][0]
# coord normalized output [y_min, x_min, y_max, x_max]
# y = (y1+y2)/2
# x = (x1+x2)/2
# Visualization of the results of a detection.
# self.visualize_detection(output_dict, image_np)
# print(box_norm_coords)
return [int((box_norm_coords[1] * width) + (box_norm_coords[3] * width)) / 2, \
int((box_norm_coords[0] * height) + (box_norm_coords[2] * height)) / 2]
# print(box_norm_coords[0] * height) # y1 = ymin * height
# print(box_norm_coords[1] * width) # x1 = xmin * width
# print(box_norm_coords[2] * height) # y2 = ymax * height
# print(box_norm_coords[3] * width) # x2 = xmax * width
if __name__ == '__main__':
run = BallDetection()
while True:
input("Press enter to continue")
print(run.set_up_object_detection_api())