image loader

app.py

@@ -1,117 +1,19 @@
 import gradio as gr
-from transformers import DPTFeatureExtractor, DPTForDepthEstimation
-import torch
-import numpy as np
-from PIL import Image
-import open3d as o3d
-from pathlib import Path
+from fastai.vision.all import *
+import skimage
 
-feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-large")
-model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large")
+learn = load_learner('export.pkl')
 
-def process_image(image_path):
-    image_path = Path(image_path)
-    image_raw = Image.open(image_path)
-    image = image_raw.resize(
-        (800, int(800 * image_raw.size[1] / image_raw.size[0])),
-        Image.Resampling.LANCZOS)
+labels = learn.dls.vocab
+def predict(img):
+    img = PILImage.create(img)
+    pred,pred_idx,probs = learn.predict(img)
+    return {labels[i]: float(probs[i]) for i in range(len(labels))}
 
-    # prepare image for the model
-    encoding = feature_extractor(image, return_tensors="pt")
+title = "Breast Cancer classification"
+description = "Demo for breast cancer classification using histopathology images."
+article="<p style='text-align: center'><a href='https://www.kaggle.com/code/josemauriciodelgado/breast-cancer-detection/edit' target='_blank'>Notebook</a></p>"
+interpretation='default'
+enable_queue=True
 
-    # forward pass
-    with torch.no_grad():
-        outputs = model(**encoding)
-        predicted_depth = outputs.predicted_depth
-
-    # interpolate to original size
-    prediction = torch.nn.functional.interpolate(
-        predicted_depth.unsqueeze(1),
-        size=image.size[::-1],
-        mode="bicubic",
-        align_corners=False,
-    ).squeeze()
-    output = prediction.cpu().numpy()
-    depth_image = (output * 255 / np.max(output)).astype('uint8')
-    try:
-        gltf_path = create_3d_obj(np.array(image), depth_image, image_path)
-        img = Image.fromarray(depth_image)
-        return [img, gltf_path, gltf_path]
-    except Exception:
-        gltf_path = create_3d_obj(
-            np.array(image), depth_image, image_path, depth=8)
-        img = Image.fromarray(depth_image)
-        return [img, gltf_path, gltf_path]
-    except:
-        print("Error reconstructing 3D model")
-        raise Exception("Error reconstructing 3D model")
-
-
-def create_3d_obj(rgb_image, depth_image, image_path, depth=10):
-    depth_o3d = o3d.geometry.Image(depth_image)
-    image_o3d = o3d.geometry.Image(rgb_image)
-    rgbd_image = o3d.geometry.RGBDImage.create_from_color_and_depth(
-        image_o3d, depth_o3d, convert_rgb_to_intensity=False)
-    w = int(depth_image.shape[1])
-    h = int(depth_image.shape[0])
-
-    camera_intrinsic = o3d.camera.PinholeCameraIntrinsic()
-    camera_intrinsic.set_intrinsics(w, h, 500, 500, w/2, h/2)
-
-    pcd = o3d.geometry.PointCloud.create_from_rgbd_image(
-        rgbd_image, camera_intrinsic)
-
-    print('normals')
-    pcd.normals = o3d.utility.Vector3dVector(
-        np.zeros((1, 3)))  # invalidate existing normals
-    pcd.estimate_normals(
-        search_param=o3d.geometry.KDTreeSearchParamHybrid(radius=0.01, max_nn=30))
-    pcd.orient_normals_towards_camera_location(
-        camera_location=np.array([0., 0., 1000.]))
-    pcd.transform([[1, 0, 0, 0],
-                   [0, -1, 0, 0],
-                   [0, 0, -1, 0],
-                   [0, 0, 0, 1]])
-    pcd.transform([[-1, 0, 0, 0],
-                   [0, 1, 0, 0],
-                   [0, 0, 1, 0],
-                   [0, 0, 0, 1]])
-
-    print('run Poisson surface reconstruction')
-    with o3d.utility.VerbosityContextManager(o3d.utility.VerbosityLevel.Debug):
-        mesh_raw, densities = o3d.geometry.TriangleMesh.create_from_point_cloud_poisson(
-            pcd, depth=depth, width=0, scale=1.1, linear_fit=True)
-
-    voxel_size = max(mesh_raw.get_max_bound() - mesh_raw.get_min_bound()) / 256
-    print(f'voxel_size = {voxel_size:e}')
-    mesh = mesh_raw.simplify_vertex_clustering(
-        voxel_size=voxel_size,
-        contraction=o3d.geometry.SimplificationContraction.Average)
-
-    # vertices_to_remove = densities < np.quantile(densities, 0.001)
-    # mesh.remove_vertices_by_mask(vertices_to_remove)
-    bbox = pcd.get_axis_aligned_bounding_box()
-    mesh_crop = mesh.crop(bbox)
-    gltf_path = f'./{image_path.stem}.gltf'
-    o3d.io.write_triangle_mesh(
-        gltf_path, mesh_crop, write_triangle_uvs=True)
-    return gltf_path
-
-title = "Demo: zero-shot depth estimation with DPT + 3D Point Cloud"
-description = "This demo is a variation from the original <a href='https://huggingface.co/spaces/nielsr/dpt-depth-estimation' target='_blank'>DPT Demo</a>. It uses the DPT model to predict the depth of an image and then uses 3D Point Cloud to create a 3D object."
-examples = [["examples/1-jonathan-borba-CgWTqYxHEkg-unsplash.jpg"]]
-
-iface = gr.Interface(fn=process_image,
-                     inputs=[gr.Image(
-                         type="filepath", label="Input Image")],
-                     outputs=[gr.Image(label="predicted depth", type="pil"),
-                              gr.Model3D(label="3d mesh reconstruction", clear_color=[
-                                                 1.0, 1.0, 1.0, 1.0]),
-                              gr.File(label="3d gLTF")],
-                     title=title,
-                     description=description,
-                     examples=examples,
-                     allow_flagging="never",
-                     cache_examples=False)
-
-iface.launch(debug=True, enable_queue=False)
+gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(512, 512)),outputs=gr.outputs.Label(num_top_classes=3),title=title,description=description,article=article,interpretation=interpretation,enable_queue=enable_queue).launch()

requirements.txt

@@ -0,0 +1,2 @@
+fastai
+scikit-image