initial commit

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
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+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
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+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
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.github/workflows/deploy-hf.yml

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+name: Deploy to Hugging Face spaces
+
+on:
+  push:
+    branches:
+      - main
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout Dev Repo
+        uses: actions/checkout@v3
+        with:
+          fetch-depth: 0
+          lfs: true
+
+      - name: Push to HF
+        env:
+          HFTOKEN: ${{ secrets.HFTOKEN }}
+
+        run: |
+          git remote add hf https://thiagohersan:[email protected]/spaces/thiagohersan/predominant-color
+          git push hf main

.gitignore

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+.DS_S*
+__pycache__/
+gradio_cached_examples/
+.ipynb_checkpoints/

README.md

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+---
+title: Predominant Color Calculator
+emoji: 🤨🧮
+colorFrom: blue
+colorTo: gray
+sdk: gradio
+sdk_version: 4.42.0
+app_file: app.py
+pinned: false
+---

app.py

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+import gradio as gr
+from PIL import Image as PImage
+
+from dominant_color import get_dominant_colors, rgb255_to_hex_str
+
+NUM_OUTS = 4
+all_outputs = [gr.ColorPicker(visible=False) for _ in range(NUM_OUTS)]
+
+def dom_col(img_in):
+  rgb_by_cnt, rgb_by_hls = get_dominant_colors(img_in)
+  palette_cnt = [[int(v) for v in c] for c in rgb_by_cnt[:NUM_OUTS]]
+  palette_hls = [[int(v) for v in c] for c in rgb_by_hls[:NUM_OUTS]]
+  return palette_cnt, palette_hls
+
+def dom_col_hls(img_in):
+  _, palette_hls = dom_col(img_in)
+  palette_hex = [rgb255_to_hex_str(c) for c in palette_hls]
+  return [gr.ColorPicker(h, label=f"{h}", show_label=True, visible=True) for h in palette_hex]
+
+with gr.Blocks() as demo:
+  gr.Markdown("""
+              # Dominant color calculator
+              """)
+
+  gr.Interface(
+    dom_col_hls,
+    inputs=gr.Image(type="pil"),
+    outputs=all_outputs,
+    cache_examples=True,
+    examples=[["./imgs/03.webp"], ["./imgs/11.jpg"]],
+    allow_flagging="never",
+  )
+
+if __name__ == "__main__":
+   demo.launch()

dominant_color.py

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+import cv2
+
+import numpy as np
+
+from colorsys import hls_to_rgb, rgb_to_hls
+
+# CRITERIA: MAX_ITER, EPSILON_ACCURACY
+# cv2.TERM_CRITERIA_MAX_ITER + cv2.TERM_CRITERIA_EPS
+
+CV_KMEANS_PARAMS = {
+  "K": 8,
+  "bestLabels": None,
+  "criteria": (cv2.TERM_CRITERIA_EPS, -1, 0.02),
+  "attempts": 8,
+  "flags": cv2.KMEANS_RANDOM_CENTERS
+}
+
+def resize_PIL(pimg, max_dim=256):
+  iw, ih = pimg.size
+  resize_ratio = min(max_dim / iw, max_dim / ih)
+  new_size = (int(iw * resize_ratio), int(ih * resize_ratio))
+  return pimg.resize(new_size)
+
+def rgb01_to_rgb255(c):
+  return tuple(np.multiply(c, 255).astype(np.uint8))
+
+def rgb255_to_rgb01(c):
+  return tuple(np.multiply(c, 1.0 / 255.0).astype(np.float32))
+
+def hls_to_rgb255(c):
+  return rgb01_to_rgb255(hls_to_rgb(*c))
+
+def rgb255_to_hex_str(c):
+  r, g, b = c
+  return "#{:02x}{:02x}{:02x}".format(r, g, b)
+
+def hls_order(c):
+  _,l,s = c
+  l_term = 2 * abs(l - 0.5)
+  return l_term + (1.0 - s) if l_term < 0.5 else 1.5 * l_term
+
+def hls_order_from_rgb255(c):
+  return hls_order(rgb_to_hls(*rgb255_to_rgb01(c)))
+
+def get_dominant_colors(pimg, max_dim=256):
+  rpimg = resize_PIL(pimg, max_dim)
+  np_img = np.array(rpimg).astype(np.float32)
+  _, labels, centers = cv2.kmeans(np_img.reshape(-1, 3), **CV_KMEANS_PARAMS)
+  centers = centers.astype(np.uint8)
+
+  _, counts = np.unique(labels, return_counts=True)
+  by_count = np.argsort(-counts)
+
+  centers_hls = [rgb_to_hls(*rgb255_to_rgb01(c)) for c in centers]
+  by_hls = sorted(centers_hls, key=hls_order)
+
+  rgb_by_count = [tuple(centers[c]) for c in by_count]
+  rgb_by_hls = [hls_to_rgb255(c) for c in by_hls]
+
+  return rgb_by_count, rgb_by_hls

requirements.txt

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+fastapi==0.112.2
+opencv-python