feat: v0.0.1

.gitignore

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+#.idea/

README.md

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+# Ursula Le Yiking
+
+## Todo 
+
+- [ ] Utiliser une webcam en entrée 
+- [ ] Créer une interface avec tkinter
+- [ ] Variabliser les tailles et couleurs via l'interface  
+
+## Crash course 
+
+Lancer, avec une version Python >= 3.10 :
+
+```shell
+git clone https://git.interhacker.space/protonphoton/yiking
+python3 -m venv .venv
+source .venv/bin/activate 
+pip3 install -r requirements.txt
+python3 main.py 
+```
+
+... devrait afficher ...
+
+```shell
+_________
+_________
+_________
+___   ___
+___   ___
+___   ___
+```
+
+... qui signifie :
+
+> 11. LA PAIX (TAI 泰)  
+> La rencontre entre le Ciel et la Terre – une combinaison rare de grâce et de force -annonce la paix, l’harmonie, la stabilité et la prospérité. Un moment de consensus promettant des résultats constructifs et prometteurs. Ces circonstances augurent une coexistence pacifique entre tout le monde. 
+
+
+## Fonctionnement
+
+### Source 
+![](./tests/images/balls-full-small.jpg)
+
+### Post traitement 
+![](./tests/images/balls-full-small-final.jpg)
+
+

main.py

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+import math
+from dataclasses import dataclass
+
+import numpy as np
+import cv2
+import tkinter
+import os
+import sys
+from pathlib import Path
+
+dir_path = Path(".").absolute()
+TYPE_1 = "_________"
+TYPE_2 = "___   ___"
+
+
+@dataclass
+class Object:
+    x: int
+    y: int
+    diametre: int
+
+
+# 1. Acquisition de l'image
+src = dir_path.joinpath('tests/images/balls-full-small.jpg')
+raw_image = cv2.imread(str(src))
+
+# 2. Boxing des objets via opencv
+gray = cv2.cvtColor(raw_image, cv2.COLOR_BGR2GRAY)
+blurred = cv2.medianBlur(gray, 25)
+minDist = 100
+param1 = 30  # 500
+param2 = 25  # 200 #smaller value-> more false circles
+minRadius = 5
+maxRadius = 1000  # 10
+circles = cv2.HoughCircles(blurred, cv2.HOUGH_GRADIENT, 1, minDist, param1=param1, param2=param2, minRadius=minRadius,
+                           maxRadius=maxRadius)
+
+min_diameter = 9999
+cochonnet = None
+boules = []
+if circles is not None:
+    circles = np.uint16(np.around(circles))
+    for i in circles[0, :]:
+        boule = Object(x=int(i[0]), y=int(i[1]), diametre=int(i[2]))
+        # cv2.circle(img, (boule.x, boule.y), boule.diametre, (0, 255, 0), 2)
+
+        # 3. Détection de la box la plus petite : cochonnet
+        if boule.diametre < min_diameter:
+            min_diameter = boule.diametre
+            if cochonnet != None:
+                boules.append(cochonnet)
+            cochonnet = boule
+        else:
+            boules.append(boule)
+
+img_check_shapes = raw_image.copy()
+for boule in boules:
+    cv2.circle(img_check_shapes, (boule.x, boule.y), boule.diametre, (0, 255, 0), 2)
+cv2.circle(img_check_shapes, (cochonnet.x, cochonnet.y), cochonnet.diametre, (255, 255, 0), -1)
+# cv2.imshow('img_check_shapes', img_check_shapes)
+# cv2.waitKey(0)
+# cv2.destroyAllWindows()
+
+# 4. Regroupement en liste de boules 1 ou 2 selon la couleur principale de chaque box restante
+
+hsv = cv2.cvtColor(raw_image, cv2.COLOR_BGR2HSV)
+(h, s, v) = cv2.split(hsv)
+s = s * 2
+s = np.clip(s, 0, 255)
+imghsv = cv2.merge([h, s, v])
+# cv2.imshow('imghsv', imghsv)
+# cv2.waitKey(0)
+# cv2.destroyAllWindows()
+
+boules_couleurs = []
+for boule in boules:
+    half_diametre = int(boule.diametre / 2)
+    crop = imghsv[
+           boule.y - half_diametre:boule.y + half_diametre,
+           boule.x - half_diametre:boule.x + half_diametre,
+           ].copy()
+    pixels = np.float32(crop.reshape(-1, 3))
+    n_colors = 2
+    criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 200, .1)
+    _, labels, palette = cv2.kmeans(pixels, n_colors, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)
+    _, counts = np.unique(labels, return_counts=True)
+    (b, g, r) = palette[np.argmax(counts)] / 16
+
+    # A modulariser
+    boules_couleurs.append(TYPE_1 if b > 4 else TYPE_2)
+
+    # cv2.imshow('crop', crop)
+    # cv2.waitKey(0)
+
+# 5. Calcul des distances entre chaque boule et le cochonnet selon le centre des boxs
+boules_distance = {}
+for i, boule in enumerate(boules):
+    dist = int(math.sqrt(math.pow(cochonnet.x - boule.x, 2) + math.pow(cochonnet.y - boule.y, 2)))
+    boules_distance[i] = dist
+boules_distance = dict(sorted(boules_distance.items(), key=lambda item: item[1]))
+
+# 6. Liste ordonnée des 6 distances les plus faibles
+
+boules_proches = [x for x in list(boules_distance)[0:6]]
+
+# 7. Sortie des 6 couleurs en  --- ou - -
+img_final = raw_image.copy()
+for i in boules_proches:
+    boule = boules[i]
+    print(boules_couleurs[i])
+    cv2.circle(img_final, (boule.x, boule.y), boule.diametre, (0, 255, 0), 2)
+
+# Show result for testing:
+cv2.imshow('img_final', img_final)
+cv2.waitKey(0)
+cv2.destroyAllWindows()

requirements.txt

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+numpy~=2.1.3
+opencv-python