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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
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(REPO_ID)
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')
REPO_ID = "23A066X"
gr.Interface(fn=predict,
inputs=[gr.Image(type="pil",label="Input Image"),gr.Textbox(placeholder="0.50",label="Set the confidence threshold (0.00-1.00)")],
outputs=gr.Image(type="pil",label="Output Image"),
title="Cauliflower and Beetroot Detection",
description="Model: ssd_resnet50_v1_fpn_640x640_coco17_tpu-8",
theme=gr.themes.Soft(primary_hue="blue", secondary_hue="sky")
).launch(share=True)
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