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import gradio as gr
from huggingface_hub import snapshot_download
import tensorflow as tf
from PIL import Image
import numpy as np
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as viz_utils
# Path to the label map
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_model(repo_id):
download_dir = snapshot_download(repo_id)
saved_model_dir = os.path.join(download_dir, "saved_model")
detection_model = tf.saved_model.load(saved_model_dir)
return detection_model
def predict(pilimg):
image_np = pil_image_as_numpy_array(pilimg)
return predict_combined_models(image_np, detection_model1, detection_model2)
def predict_combined_models(image_np, model1, model2):
# Process with first model
results1 = model1(image_np)
result1 = {key:value.numpy() for key,value in results1.items()}
# Process with second model
results2 = model2(image_np)
result2 = {key:value.numpy() for key,value in results2.items()}
# Visualization for model 1
image_np_with_detections = image_np.copy()
viz_utils.visualize_boxes_and_labels_on_image_array(
image_np_with_detections[0],
result1['detection_boxes'][0],
(result1['detection_classes'][0]).astype(int),
result1['detection_scores'][0],
category_index,
use_normalized_coordinates=True,
max_boxes_to_draw=200,
min_score_thresh=.60,
agnostic_mode=False,
line_thickness=2)
# Visualization for model 2 (can adjust styles to differentiate)
viz_utils.visualize_boxes_and_labels_on_image_array(
image_np_with_detections[0],
result2['detection_boxes'][0],
(result2['detection_classes'][0]).astype(int),
result2['detection_scores'][0],
category_index,
use_normalized_coordinates=True,
max_boxes_to_draw=200,
min_score_thresh=.60,
agnostic_mode=False,
line_thickness=2)
# Combine and return final image
result_pil_img = tf.keras.utils.array_to_img(image_np_with_detections[0])
return result_pil_img
# Load your models
REPO_ID1 = "dtyago/23a052w-iti107-assn2_tfodmodel"
REPO_ID2 = "dtyago/23a052w-iti107-assn2_tfodmodel_run1"
detection_model1 = load_model(REPO_ID1)
detection_model2 = load_model(REPO_ID2)
# Gradio interface
gr.Interface(
fn=predict,
inputs=gr.Image(type="pil"),
outputs=gr.Image(type="pil")
).launch(share=True)
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