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import gradio as gr | |
import matplotlib.pyplot as plt | |
import numpy as np | |
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 gradio as gr | |
import os | |
import cv2 | |
def greet(name): | |
return "Hello " + name + "!!" | |
iface = gr.Interface(fn=greet, inputs="text", outputs="text") | |
iface.launch() | |
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) | |
return img_array | |
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) | |
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=.6, | |
agnostic_mode=False, | |
line_thickness=2 | |
) | |
result_pil_img = tf.keras.utils.array_to_img(image_np_with_detections[0]) | |
return result_pil_img | |
detection_model = load_model() | |
# Specify paths to example images | |
sample_images = [["br_61.jpg"], ["br_61.jpg"], | |
] | |
tab1 = gr.Interface( | |
fn=predict, | |
inputs=gr.Image(label='Upload an expressway image', type="pil"), | |
outputs=gr.Image(type="pil"), | |
title='Image Processing', | |
examples=sample_images | |
) | |
# Create a Multi Interface with Tabs | |
iface = gr.TabbedInterface([tab1], title='Cauliflower and Beetroot Detection via ssd_resnet50_v1_fpn_640x640_coco17_tpu-8') | |
# Launch the interface | |
iface.launch(share=True) |