Synced repo using 'sync_with_huggingface' Github Action

app.py

@@ -2,22 +2,14 @@ import streamlit as st
 
 import pandas as pd
 
+import dataset_wrangler
+
 
 st.write(
     "Scrambled Images  from [https://www.slv.vic.gov.au/images](https://www.slv.vic.gov.au/images)"
 )
 
-try:
-
-    df = pd.read_csv(
-        "https://raw.githubusercontent.com/StateLibraryVictoria/public-domain-hack-2024/refs/heads/main/datasets/challenge-3-Image-Pool-2024-11-27.csv"
-    )
-
-except:
-
-    df = pd.read_csv(
-        "https://raw.githubusercontent.com/StateLibraryVictoria/public-domain-hack-2024/refs/heads/main/datasets/challenge-3-Image-Pool-2024-11-27.csv"
-    )
+df = dataset_wrangler.clean_df()
 
 
 st.dataframe(df.head(10))

dataset_wrangler.py

@@ -0,0 +1,23 @@
+import pandas as pd
+
+dataset = "https://raw.githubusercontent.com/StateLibraryVictoria/public-domain-hack-2024/refs/heads/main/datasets/challenge-3-Image-Pool-2024-11-27.csv"
+
+columns = [
+    "IE PID",
+    "Title (DC)",
+    "ALMA _ MMS (Object Identifier - IE)",
+    "HANDLE (Object Identifier - IE)",
+    "Creator (DC)",
+    "Genre (DCTERMS)",
+    "Created (DCTERMS)",
+]
+
+
+def clean_df(columns=columns, dataset=dataset):
+
+    df = pd.read_csv(dataset)
+
+    df = df[columns]
+    df = df.dropna()
+
+    return df

image_analysis.py

@@ -0,0 +1,77 @@
+import cv2 as cv
+import numpy as np
+import requests
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+
+from sklearn.cluster import KMeans
+
+
+def get_iiif_image_urls(ie_pid: str):
+
+    manifest_url = f"https://rosetta.slv.vic.gov.au/delivery/iiif/presentation/2.1/{ie_pid}/manifest"
+    print(manifest_url)
+    session = requests.Session()
+
+    response = session.get(manifest_url)
+
+    manifest = response.json()
+
+    image_ids = [
+        canvas["images"][0]["resource"]["service"]["@id"]
+        for canvas in manifest["sequences"][0]["canvases"]
+    ]
+
+    image_urls = [f"{image_id}/full/600,/0/default.jpg" for image_id in image_ids]
+
+    return image_urls
+
+
+def show_img_compare(img_1, img_2):
+    f, ax = plt.subplots(1, 2, figsize=(10, 10))
+    ax[0].imshow(img_1)
+    ax[1].imshow(img_2)
+    ax[0].axis("off")  # hide the axis
+    ax[1].axis("off")
+    f.tight_layout()
+    plt.show()
+
+
+def palette(clusters):
+    width = 300
+    palette = np.zeros((50, width, 3), np.uint8)
+    steps = width / clusters.cluster_centers_.shape[0]
+    for idx, centers in enumerate(clusters.cluster_centers_):
+        palette[:, int(idx * steps) : (int((idx + 1) * steps)), :] = centers
+    return palette
+
+
+def get_palette_clusters(img, no_of_clusters=5):
+
+    cluster = KMeans(n_clusters=no_of_clusters)
+    cluster.fit(img.reshape(-1, 3))
+
+    clusters = cluster.fit(img.reshape(-1, 3))
+
+    return clusters
+
+
+image_urls = get_iiif_image_urls("IE1267294")
+
+response = requests.get(image_urls[0])
+
+img = cv.imdecode(np.frombuffer(response.content, np.uint8), -1)
+img = cv.cvtColor(img, cv.COLOR_BGR2RGB)
+
+dim = (500, 300)
+img = cv.resize(img, dim, interpolation=cv.INTER_AREA)
+
+clt_1 = get_palette_clusters(img)
+
+
+img_palette = palette(clt_1)
+
+print(img_palette)
+
+show_img_compare(img, img_palette)