1st draft

app.py

@@ -16,87 +16,44 @@ 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"
+    "https://upload.wikimedia.org/wikipedia/commons/thumb/5/5b/Draft_Marks_on_the_Bow_of_Kruzenshtern_Port_of_Tallinn_16_July_2011.jpg/1600px-Draft_Marks_on_the_Bow_of_Kruzenshtern_Port_of_Tallinn_16_July_2011.jpg"
 )
-path_input = "./cat.jpg"
+path_input = "./example1.jpg"
 urllib.request.urlretrieve(url, filename=path_input)
 
-url = "https://upload.wikimedia.org/wikipedia/commons/4/43/Cute_dog.jpg"
-path_input = "./dog.jpg"
+url = "https://upload.wikimedia.org/wikipedia/commons/3/3e/733_how-deep.jpg"
+path_input = "./example2.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
-# )
+path_img_output_folder = "./demo-out"
+if not os.path.exists(path_img_output_folder):
+    os.makedirs(path_img_output_folder)
 
+path_img_input_folder = "./demo-input"
+if not os.path.exists(path_img_input_folder):
+    os.makedirs(path_img_input_folder)
 
 def do_process(img):
-    return img
-#     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
-
+    img_name = 'tmp.jpg'
+    img.save(path_input)
+    path_input = os.path.join(path_img_input_folder, img_name)
+    path_output = os.path.join(path_img_output_folder, 'vis',img_name)
+    result = ocr(path_input, out_dir=path_img_output_folder, save_vis=True)
+    img_res = Image(filename=path_output)
+    return img_res
 
 input_im = gr.inputs.Image(
-    shape=(224, 224), image_mode="RGB", invert_colors=False, source="upload", type="pil"
+    shape=None, 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"], ["./dog.jpg"]]
+title = "Reading draught marks"
+description = "Playground: Reading draught marks using pre-trained models. Tools: MMOCR, Gradio."
+examples = [["./example1.jpg"], ["./example2.jpg"]]
 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,