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import numpy as np
import math
import matplotlib.pyplot as plt
import onnxruntime as rt
import cv2
import json
import gradio as gr
# load the labels text file
labels = json.load(open("labels_map.txt", "r"))
# set image file dimensions to 224x224 by resizing and cropping image from center
def pre_process_edgetpu(img, dims):
output_height, output_width, _ = dims
img = resize_with_aspectratio(img, output_height, output_width, inter_pol=cv2.INTER_LINEAR)
img = center_crop(img, output_height, output_width)
img = np.asarray(img, dtype='float32')
# converts jpg pixel value from [0 - 255] to float array [-1.0 - 1.0]
img -= [127.0, 127.0, 127.0]
img /= [128.0, 128.0, 128.0]
return img
# resize the image with a proportional scale
def resize_with_aspectratio(img, out_height, out_width, scale=87.5, inter_pol=cv2.INTER_LINEAR):
height, width, _ = img.shape
new_height = int(100. * out_height / scale)
new_width = int(100. * out_width / scale)
if height > width:
w = new_width
h = int(new_height * height / width)
else:
h = new_height
w = int(new_width * width / height)
img = cv2.resize(img, (w, h), interpolation=inter_pol)
return img
# crop the image around the center based on given height and width
def center_crop(img, out_height, out_width):
height, width, _ = img.shape
left = int((width - out_width) / 2)
right = int((width + out_width) / 2)
top = int((height - out_height) / 2)
bottom = int((height + out_height) / 2)
img = img[top:bottom, left:right]
return img
import onnxruntime as rt
sess = rt.InferenceSession('efficientnet-lite4-11.onnx')
def inference(img):
img = cv2.imread(img)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = pre_process_edgetpu(img, (224, 224, 3))
img_batch = np.expand_dims(img, axis=0)
results = sess.run(["Softmax:0"], {"images:0": img_batch})[0]
result = reversed(results[0].argsort()[-5:])
resultdic = {}
for r in result:
resultdic[labels[str(r)]] = results[0][r]
return resultdic
gr.Interface(inference,gr.inputs.Image(type="filepath"),"label").launch() |