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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 = '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(): | |
download_dir = snapshot_download() | |
saved_model_dir = os.path.join(download_dir, "saved_model.pb") | |
detection_model = tf.saved_model.load(saved_model_dir) | |
return detection_model | |
def predict(pilimg): | |
image_np = pil_image_as_numpy_array(pilimg) | |
return predict2(image_np) | |
detection_model = load_model() | |
# pil_image = Image.open(image_path) | |
# image_arr = pil_image_as_numpy_array(pil_image) | |
# predicted_img = predict(image_arr) | |
# predicted_img.save('predicted.jpg') | |
gr.Interface(fn=predict, | |
inputs=gr.Image(type="pil"), | |
outputs=gr.Image(type="pil") | |
).launch(share=True) | |