def pre_process(image_path):
"""
Pre-process the input image to return a json to pass to the tf model
Args:
image_path (str): Path to the jpeg image
Returns:
formatted_json_input (str)
"""
image = Image.open(image_path).convert("RGB")
image_np = plot_util.load_image_into_numpy_array(image)
#print("image shape", image_np.shape)
# Expand dims to create bach of size 1
image_tensor = np.expand_dims(image_np, 0)
np.save("image_tensor", image)
#print(image_np)
formatted_json_input = json.dumps({
"signature_name":
"serving_default",
"instances":
image_tensor.tolist(),
"model_name":
"v1/models/faster_rcnn_resnet:predict"
})
return formatted_json_input
def pre_process(image_path):
image = Image.open(image_path).convert("RGB")
image_np = plot_util.load_image_into_numpy_array(image)
# Expand dims to create bach of size 1
image_tensor = np.expand_dims(image_np, 0)
formatted_json_input = json.dumps({
"signature_name": "serving_default",
"instances": image_tensor.tolist()
})
return formatted_json_input
# Post process output
print(f'\n\nPost-processing server response...\n')
post_processed_data = post_process(server_response)
print(f'Post-processing done!\n')
# Save output on disk
print(f'\n\nSaving output to {output_image}\n\n')
with open(output_image, 'w+') as outfile:
json.dump(post_processed_data, outfile)
print(f'Output saved!\n')
if save_output_image:
# Save output on disk
print('\n\nBuilding output image\n\n')
image = Image.open(image_path).convert("RGB")
image_np = plot_util.load_image_into_numpy_array(image)
category_index = plot_util.load_category_index(
path_to_labels,
99999) # FIXME: Magic number to replace by meaningful var
# Visualization of the results of a detection.
vis_util.visualize_boxes_and_labels_on_image_array(
image_np,
np.array(post_processed_data['detection_boxes']
), # NOTE: np.array needed for .shape property
post_processed_data['detection_classes'],
post_processed_data['detection_scores'],
category_index,
instance_masks=post_processed_data.get('detection_masks'),
use_normalized_coordinates=True,
