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#Loading the saved_model | |
import tensorflow as tf | |
import time | |
import numpy as np | |
import warnings | |
warnings.filterwarnings('ignore') | |
from PIL import Image | |
from google.colab.patches import cv2_imshow | |
from object_detection.utils import label_map_util | |
from object_detection.utils import visualization_utils as viz_utils | |
IMAGE_SIZE = (12, 8) # Output display size as you want | |
import matplotlib.pyplot as plt | |
PATH_TO_SAVED_MODEL="/mydrive/customTF2/data/inference_graph/saved_model" | |
print('Loading model...', end='') | |
# Load saved model and build the detection function | |
detect_fn=tf.saved_model.load(PATH_TO_SAVED_MODEL) | |
print('Done!') | |
#Loading the label_map | |
category_index=label_map_util.create_category_index_from_labelmap("/mydrive/customTF2/data/label_map.pbtxt",use_display_name=True) | |
#category_index=label_map_util.create_category_index_from_labelmap([path_to_label_map],use_display_name=True) | |
def load_image_into_numpy_array(path): | |
return np.array(Image.open(path)) | |
image_path = "/mydrive/mask_test_images/image1.jpg" | |
#print('Running inference for {}... '.format(image_path), end='') | |
image_np = load_image_into_numpy_array(image_path) | |
# The input needs to be a tensor, convert it using `tf.convert_to_tensor`. | |
input_tensor = tf.convert_to_tensor(image_np) | |
# The model expects a batch of images, so add an axis with `tf.newaxis`. | |
input_tensor = input_tensor[tf.newaxis, ...] | |
detections = detect_fn(input_tensor) | |
# All outputs are batches tensors. | |
# Convert to numpy arrays, and take index [0] to remove the batch dimension. | |
# We're only interested in the first num_detections. | |
num_detections = int(detections.pop('num_detections')) | |
detections = {key: value[0, :num_detections].numpy() | |
for key, value in detections.items()} | |
detections['num_detections'] = num_detections | |
# detection_classes should be ints. | |
detections['detection_classes'] = detections['detection_classes'].astype(np.int64) | |
image_np_with_detections = image_np.copy() | |
viz_utils.visualize_boxes_and_labels_on_image_array( | |
image_np_with_detections, | |
detections['detection_boxes'], | |
detections['detection_classes'], | |
detections['detection_scores'], | |
category_index, | |
use_normalized_coordinates=True, | |
max_boxes_to_draw=200, | |
min_score_thresh=.4, # Adjust this value to set the minimum probability boxes to be classified as True | |
agnostic_mode=False) | |
%matplotlib inline | |
plt.figure(figsize=IMAGE_SIZE, dpi=200) | |
plt.axis("off") | |
plt.imshow(image_np_with_detections) | |
plt.show() |
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Spectacular, thank you very much for the contribution. The key lines are 64 and 65.