Spaces:
Sleeping
Sleeping
File size: 1,670 Bytes
f83ceb5 bf0820e f83ceb5 6c3df1f d73cc9d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 |
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)
|