MrDavidYu

sometimes = lambda aug: iaa.Sometimes(0.5, aug)
# Define our sequence of augmentation steps that will be applied to every image
# All augmenters with per_channel=0.5 will sample one value _per image_
# in 50% of all cases. In all other cases they will sample new values
# _per channel_.
seq = iaa.Sequential(
    [
        # apply the following augmenters to most images
        iaa.Fliplr(0.5), # horizontally flip 50% of all images
        iaa.Flipud(0.2), # vertically flip 50% of all images

MrDavidYu

with detection_graph.as_default():
    with tf.Session(graph=detection_graph) as sess:
        image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
        detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
        detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
        detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
        num_detections = detection_graph.get_tensor_by_name('num_detections:0')
          for image_path in TEST_IMAGE_PATHS:
            image = Image.open(image_path)
            image_np = load_image_into_numpy_array(image)

MrDavidYu

model {
  faster_rcnn {
    num_classes: 4
    image_resizer {
      keep_aspect_ratio_resizer {
        height: 600
        width: 600
      }
    }
    feature_extractor {

MrDavidYu

import matplotlib.pyplot as plt
import matplotlib.patches as patches
from PIL import Image
import numpy as np

input_test_img = '/your/path/Utah_AGRC-HRO_15.0cm_12TVK220980-CROP.0.0.jpg'
im = np.array(Image.open(input_test_img), dtype=np.uint8)

# Create figure and axes
fig,ax = plt.subplots(figsize=(10, 10))