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import matplotlib.pyplot as plt | |
import numpy as np | |
from six import BytesIO | |
from PIL import Image | |
import tensorflow as tf | |
from object_detection.utils import label_map_util | |
from object_detection.utils import visualization_utils as viz_utils | |
from object_detection.utils import ops as utils_op | |
import tarfile | |
import wget | |
import gradio as gr | |
from huggingface_hub import snapshot_download | |
import os | |
PATH_TO_LABELS = 'data/label_map.pbtxt' | |
category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True) | |
def pil_image_as_numpy_array(pilimg): | |
img_array = tf.keras.utils.img_to_array(pilimg) | |
# img_array = np.expand_dims(img_array, axis=0) | |
return img_array | |
def load_image_into_numpy_array(path): | |
image = None | |
image_data = tf.io.gfile.GFile(path, 'rb').read() | |
image = Image.open(BytesIO(image_data)) | |
return pil_image_as_numpy_array(image) | |
def load_model(): | |
model_dir = 'saved_model' | |
detection_model = tf.saved_model.load(str(model_dir)) | |
return detection_model | |
def predict(image_np): | |
image_np = pil_image_as_numpy_array(image_np) | |
image_np = np.expand_dims(image_np, axis=0) | |
results = detection_model(image_np) | |
# different object detection models have additional results | |
result = {key:value.numpy() for key,value in results.items()} | |
label_id_offset = 0 | |
image_np_with_detections = image_np.copy() | |
viz_utils.visualize_boxes_and_labels_on_image_array( | |
image_np_with_detections[0], | |
result['detection_boxes'][0], | |
(result['detection_classes'][0] + label_id_offset).astype(int), | |
result['detection_scores'][0], | |
category_index, | |
use_normalized_coordinates=True, | |
max_boxes_to_draw=200, | |
min_score_thresh=.60, | |
agnostic_mode=False, | |
line_thickness=2) | |
result_pil_img = tf.keras.utils.array_to_img(image_np_with_detections[0]) | |
return result_pil_img | |
def predict2(pilimg): | |
image = load_image_into_numpy_array(pilimg) | |
return predict(image) | |
detection_model = load_model() | |
# Specify paths to example images | |
example_image_paths = ["test_1.jpg"] | |
# Create a list of example inputs and outputs using a for loop | |
example_inputs = [Image.open(path) for path in example_image_paths] | |
example_outputs = [predict2(input_image) for input_image in example_image_paths] | |
# Create the Gradio interface with examples using a for loop | |
examples = [[example_inputs[i], example_outputs[i]] for i in range(len(example_inputs))] | |
iface = gr.Interface(fn=predict, | |
inputs=gr.Image(label='Upload an expressway image', type="pil"), | |
outputs=gr.Image(type="pil"), | |
title='Blue and Yellow Taxi detection in live expressway traffic conditions (data.gov.sg)', | |
example=examples | |
).launch(share=True) | |