test

app.py

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+import matplotlib.pyplot as plt
+import numpy as np
+from PIL import Image, ImageFilter
+import io
+import time
+import os
+import copy
+import pickle
+import datetime
+import urllib.request
+import gradio as gr
+# from mim import install
+
+
+
+# install('mmcv-full')
+# install('mmengine')
+# install('mmdet')
+from mmocr.apis import MMOCRInferencer
+ocr = MMOCRInferencer(det='TextSnake', rec='ABINet_Vision')
+
+# url = (
+#     "https://upload.wikimedia.org/wikipedia/commons/3/38/Adorable-animal-cat-20787.jpg"
+# )
+# path_input = "./cat.jpg"
+# urllib.request.urlretrieve(url, filename=path_input)
+
+# url = "https://upload.wikimedia.org/wikipedia/commons/4/43/Cute_dog.jpg"
+# path_input = "./dog.jpg"
+# urllib.request.urlretrieve(url, filename=path_input)
+
+# model = keras_model(weights="imagenet")
+
+# n_steps = 50
+# method = "gausslegendre"
+# internal_batch_size = 50
+# ig = IntegratedGradients(
+#     model, n_steps=n_steps, method=method, internal_batch_size=internal_batch_size
+# )
+
+
+# def do_process(img, baseline):
+#     instance = image.img_to_array(img)
+#     instance = np.expand_dims(instance, axis=0)
+#     instance = preprocess_input(instance)
+#     preds = model.predict(instance)
+#     lstPreds = decode_predictions(preds, top=3)[0]
+#     dctPreds = {
+#         lstPreds[i][1]: round(float(lstPreds[i][2]), 2) for i in range(len(lstPreds))
+#     }
+#     predictions = preds.argmax(axis=1)
+#     if baseline == "white":
+#         baselines = bls = np.ones(instance.shape).astype(instance.dtype)
+#         img_flt = Image.fromarray(np.uint8(np.squeeze(baselines) * 255))
+#     elif baseline == "black":
+#         baselines = bls = np.zeros(instance.shape).astype(instance.dtype)
+#         img_flt = Image.fromarray(np.uint8(np.squeeze(baselines) * 255))
+#     elif baseline == "blur":
+#         img_flt = img.filter(ImageFilter.GaussianBlur(5))
+#         baselines = image.img_to_array(img_flt)
+#         baselines = np.expand_dims(baselines, axis=0)
+#         baselines = preprocess_input(baselines)
+#     else:
+#         baselines = np.random.random_sample(instance.shape).astype(instance.dtype)
+#         img_flt = Image.fromarray(np.uint8(np.squeeze(baselines) * 255))
+#     explanation = ig.explain(instance, baselines=baselines, target=predictions)
+#     attrs = explanation.attributions[0]
+#     fig, ax = visualize_image_attr(
+#         attr=attrs.squeeze(),
+#         original_image=img,
+#         method="blended_heat_map",
+#         sign="all",
+#         show_colorbar=True,
+#         title=baseline,
+#         plt_fig_axis=None,
+#         use_pyplot=False,
+#     )
+#     fig.tight_layout()
+#     buf = io.BytesIO()
+#     fig.savefig(buf)
+#     buf.seek(0)
+#     img_res = Image.open(buf)
+#     return img_res, img_flt, dctPreds
+
+
+# input_im = gr.inputs.Image(
+#     shape=(224, 224), image_mode="RGB", invert_colors=False, source="upload", type="pil"
+# )
+# input_drop = gr.inputs.Dropdown(
+#     label="Baseline (default: random)",
+#     choices=["random", "black", "white", "blur"],
+#     default="random",
+#     type="value",
+# )
+
+# output_img = gr.outputs.Image(label="Output of Integrated Gradients", type="pil")
+# output_base = gr.outputs.Image(label="Baseline image", type="pil")
+# output_label = gr.outputs.Label(label="Classification results", num_top_classes=3)
+
+# title = "XAI - Integrated gradients"
+# description = "Playground: Integrated gradients for a ResNet model trained on Imagenet dataset. Tools: Alibi, TF, Gradio."
+# examples = [["./cat.jpg", "blur"], ["./dog.jpg", "random"]]
+# article = "<p style='text-align: center'><a href='https://github.com/mawady' target='_blank'>By Dr. Mohamed Elawady</a></p>"
+# iface = gr.Interface(
+#     fn=do_process,
+#     inputs=[input_im, input_drop],
+#     outputs=[output_img, output_base, output_label],
+#     live=False,
+#     interpretation=None,
+#     title=title,
+#     description=description,
+#     article=article,
+#     examples=examples,
+# )
+
+# iface.launch(debug=True)

requirements.txt

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+pillow
+mmcv>=2.0.0rc1
+mmdet
+mmocr