Create onnx_guide/app.py

onnx_guide/app.py

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+import json
+import math
+
+import cv2
+import gradio as gr
+import matplotlib.pyplot as plt
+import numpy as np
+import onnxruntime as rt
+from huggingface_hub import hf_hub_download
+
+modele = hf_hub_download(repo_id="onnx/EfficientNet-Lite4", filename="efficientnet-lite4-11.onnx")
+# load the labels text file
+labels = json.load(open("onnx_guide/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(100.0 * out_height / scale)
+    new_width = int(100.0 * 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
+
+
+sess = rt.InferenceSession(modele)
+
+
+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)]] = float(results[0][r])
+    return resultdic
+
+
+title = "EfficientNet-Lite4"
+description = "EfficientNet-Lite 4 is the largest variant and most accurate of the set of EfficientNet-Lite model. It is an integer-only quantized model that produces the highest accuracy of all of the EfficientNet models. It achieves 80.4% ImageNet top-1 accuracy, while still running in real-time (e.g. 30ms/image) on a Pixel 4 CPU."
+examples = [[hf_hub_download('nateraw/gradio-guides-files', 'catonnx.jpg', repo_type='dataset', force_filename='catonnx.jpg')]]
+
+interface = gr.Interface(
+    inference, gr.inputs.Image(type="filepath"), "label", title=title, description=description, examples=examples
+)
+
+if __name__ == '__main__':
+    interface.launch(debug=True)