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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
import tarfile
import wget
import gradio as gr
from huggingface_hub import snapshot_download
import os
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
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)
# download_dir = os.path.join(download_dir, "saved_model")
saved_model_dir = os.path.join(download_dir, "saved_model")
detection_model = tf.saved_model.load(saved_model_dir)
return detection_model
def load_model2():
wget.download("https://nyp-aicourse.s3-ap-southeast-1.amazonaws.com/pretrained-models/balloon_model.tar.gz")
tarfile.open("balloon_model.tar.gz").extractall()
model_dir = 'saved_model'
detection_model = tf.saved_model.load(str(model_dir))
return detection_model
def predict(pilimg):
image_np = pil_image_as_numpy_array(pilimg)
return predict2(image_np)
def predict2(image_np):
results = detection_model(image_np)
# different object detection models have additional results
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=0.60,
agnostic_mode=False,
line_thickness=3)
result_pil_img = tf.keras.utils.array_to_img(image_np_with_detections[0])
return result_pil_img
REPO_ID = "A23066X/A23066X_model"
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')
title = "Cauliflower and Beetroot Detection"
description = "Using ssd_resnet50_v1_fpn_640x640_coco17_tpu-8"
gr.Interface(fn=predict,
title = title,
description = description,
inputs=gr.Image(type="pil", height=250),
outputs=gr.Image(type="pil", height=250)
).launch(share=True) |