feat: add User Interface
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alban
parent
6d83f0ed93
commit
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199
main.py
199
main.py
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import math
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from dataclasses import dataclass
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import tkinter as tk
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from tkinter import ttk
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from process import process_frame
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from PIL import Image, ImageTk # Required for displaying images
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import cv2 # OpenCV for numpy array to image conversion
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import numpy as np
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import cv2
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import tkinter
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import os
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import sys
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from pathlib import Path
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class OpenCVInterface:
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def __init__(self, root):
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self.root = root
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self.root.title("Yiking")
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dir_path = Path(".").absolute()
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TYPE_1 = "_________"
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TYPE_2 = "___ ___"
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# Variables for sliders with min, max, and default values
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self.variables_config = {
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"minDist": (0, 500, 100),
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"param1": (0, 500, 30),
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"param2": (0, 400, 25),
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"minRadius": (0, 100, 5),
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"maxRadius": (0, 1000, 1000),
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"color1_R": (0, 64, 5),
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"color1_V": (0, 64, 5),
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"color1_B": (0, 64, 5),
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"color2_R": (0, 64, 5),
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"color2_V": (0, 64, 5),
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"color2_B": (0, 64, 5),
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}
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self.variables = {
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name: tk.IntVar(value=config[2]) for name, config in self.variables_config.items()
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}
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@dataclass
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class Object:
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x: int
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y: int
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diametre: int
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# GUI Layout
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self.setup_gui()
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def setup_gui(self):
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# Root grid layout
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self.root.rowconfigure(1, weight=1)
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self.root.columnconfigure(0, weight=1)
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self.root.columnconfigure(1, weight=1)
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# 1. Acquisition de l'image
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src = dir_path.joinpath('tests/images/balls-full-small.jpg')
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raw_image = cv2.imread(str(src))
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# Left Column: Sliders
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left_frame = ttk.Frame(self.root)
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left_frame.grid(row=0, column=0, sticky="nswe")
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# 2. Boxing des objets via opencv
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gray = cv2.cvtColor(raw_image, cv2.COLOR_BGR2GRAY)
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blurred = cv2.medianBlur(gray, 25)
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minDist = 100
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param1 = 30 # 500
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param2 = 25 # 200 #smaller value-> more false circles
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minRadius = 5
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maxRadius = 1000 # 10
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circles = cv2.HoughCircles(blurred, cv2.HOUGH_GRADIENT, 1, minDist, param1=param1, param2=param2, minRadius=minRadius,
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maxRadius=maxRadius)
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for var_name, var in self.variables.items():
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min_val, max_val, _ = self.variables_config[var_name]
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self.create_slider(left_frame, var_name, var, min_val, max_val)
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min_diameter = 9999
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cochonnet = None
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boules = []
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if circles is not None:
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circles = np.uint16(np.around(circles))
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for i in circles[0, :]:
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boule = Object(x=int(i[0]), y=int(i[1]), diametre=int(i[2]))
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# cv2.circle(img, (boule.x, boule.y), boule.diametre, (0, 255, 0), 2)
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# Right Column: Image Placeholder
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self.image_canvas = tk.Canvas(self.root, bg="gray", width=1024, height=768)
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self.image_canvas.grid(row=0, column=1, sticky="nswe")
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# 3. Détection de la box la plus petite : cochonnet
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if boule.diametre < min_diameter:
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min_diameter = boule.diametre
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if cochonnet != None:
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boules.append(cochonnet)
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cochonnet = boule
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else:
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boules.append(boule)
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# Bottom Row: Run Button and Result
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run_button = ttk.Button(self.root, text="Run", command=self.run_process)
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run_button.grid(row=1, column=0, sticky="we")
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img_check_shapes = raw_image.copy()
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for boule in boules:
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cv2.circle(img_check_shapes, (boule.x, boule.y), boule.diametre, (0, 255, 0), 2)
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cv2.circle(img_check_shapes, (cochonnet.x, cochonnet.y), cochonnet.diametre, (255, 255, 0), -1)
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# cv2.imshow('img_check_shapes', img_check_shapes)
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# cv2.waitKey(0)
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# cv2.destroyAllWindows()
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self.result_text = tk.Text(self.root, height=10, width=40)
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self.result_text.grid(row=1, column=1, sticky="nswe")
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# 4. Regroupement en liste de boules 1 ou 2 selon la couleur principale de chaque box restante
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def create_slider(self, parent, name, variable, min_val, max_val):
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frame = ttk.Frame(parent)
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frame.pack(fill="x", padx=5, pady=2)
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hsv = cv2.cvtColor(raw_image, cv2.COLOR_BGR2HSV)
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(h, s, v) = cv2.split(hsv)
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s = s * 2
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s = np.clip(s, 0, 255)
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imghsv = cv2.merge([h, s, v])
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# cv2.imshow('imghsv', imghsv)
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# cv2.waitKey(0)
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# cv2.destroyAllWindows()
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# Label
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label = ttk.Label(frame, text=name)
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label.pack(side="left")
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boules_couleurs = []
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for boule in boules:
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half_diametre = int(boule.diametre / 2)
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crop = imghsv[
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boule.y - half_diametre:boule.y + half_diametre,
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boule.x - half_diametre:boule.x + half_diametre,
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].copy()
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pixels = np.float32(crop.reshape(-1, 3))
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n_colors = 2
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criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 200, .1)
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_, labels, palette = cv2.kmeans(pixels, n_colors, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)
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_, counts = np.unique(labels, return_counts=True)
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(b, g, r) = palette[np.argmax(counts)] / 16
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def on_slide(value):
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# Round value to nearest multiple of 5
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rounded_value = round(float(value) / 5) * 5
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variable.set(int(rounded_value)) # Update the variable with the rounded value
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# A modulariser
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boules_couleurs.append(TYPE_1 if b > 4 else TYPE_2)
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# Slider
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slider = ttk.Scale(
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frame, from_=min_val, to=max_val,
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variable=variable, orient="horizontal", command=on_slide
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)
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slider.pack(side="left", fill="x", expand=True, padx=5)
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# cv2.imshow('crop', crop)
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# cv2.waitKey(0)
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# Entry box
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entry = ttk.Entry(frame, textvariable=variable, width=5)
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entry.pack(side="left")
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# 5. Calcul des distances entre chaque boule et le cochonnet selon le centre des boxs
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boules_distance = {}
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for i, boule in enumerate(boules):
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dist = int(math.sqrt(math.pow(cochonnet.x - boule.x, 2) + math.pow(cochonnet.y - boule.y, 2)))
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boules_distance[i] = dist
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boules_distance = dict(sorted(boules_distance.items(), key=lambda item: item[1]))
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def run_process(self):
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# Collect slider values
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parameters = {key: var.get() for key, var in self.variables.items()}
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# 6. Liste ordonnée des 6 distances les plus faibles
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try:
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# Call process function
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image, result_text = process_frame(parameters)
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boules_proches = [x for x in list(boules_distance)[0:6]]
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# Convert OpenCV image (numpy array) to PIL Image for Tkinter display
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if image is not None:
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image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # Convert BGR to RGB
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pil_image = Image.fromarray(image) # Convert numpy array to PIL Image
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# 7. Sortie des 6 couleurs en --- ou - -
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img_final = raw_image.copy()
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for i in boules_proches:
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boule = boules[i]
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print(boules_couleurs[i])
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cv2.circle(img_final, (boule.x, boule.y), boule.diametre, (0, 255, 0), 2)
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# Rescale image to fit within 1024x768 while preserving aspect ratio
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max_width, max_height = 1024, 768
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original_width, original_height = pil_image.size
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aspect_ratio = min(max_width / original_width, max_height / original_height)
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new_width = int(original_width * aspect_ratio)
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new_height = int(original_height * aspect_ratio)
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pil_image = pil_image.resize((new_width, new_height), Image.Resampling.LANCZOS)
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# Show result for testing:
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cv2.imshow('img_final', img_final)
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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tk_image = ImageTk.PhotoImage(pil_image) # Convert PIL Image to Tkinter Image
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# Clear canvas and display the image
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self.image_canvas.delete("all")
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self.image_canvas.create_image(512, 384, image=tk_image, anchor="center")
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self.image_canvas.image = tk_image # Keep a reference to prevent garbage collection
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# Update result text
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self.result_text.delete(1.0, tk.END)
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self.result_text.insert(tk.END, result_text)
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except Exception as exc:
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# Handle and display exceptions
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self.result_text.delete(1.0, tk.END)
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self.result_text.insert(tk.END, str(exc))
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self.image_canvas.delete("all")
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if __name__ == "__main__":
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root = tk.Tk()
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app = OpenCVInterface(root)
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root.mainloop()
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120
process.py
Normal file
120
process.py
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@ -0,0 +1,120 @@
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from operator import itemgetter
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from PIL import Image, ImageTk
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import math
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from dataclasses import dataclass
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import numpy as np
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import cv2
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from pathlib import Path
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dir_path = Path(".").absolute()
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TYPE_1 = "_________"
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TYPE_2 = "___ ___"
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@dataclass
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class Object:
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x: int
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y: int
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rayon: int
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def process_frame(params):
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"""
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Simulates OpenCV processing using parameters from the GUI.
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Args:
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params (dict): A dictionary of variable values passed from the GUI.
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Returns:
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ImageTk.PhotoImage: A Tkinter-compatible image.
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str: A result text description.
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"""
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# Simulate processing: for now, return a dummy image and text.
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# width, height = 400, 300
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# image = Image.new("RGB", (width, height),
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# color=(params["color1_R"] * 4, params["color1_V"] * 4, params["color1_B"] * 4))
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# image_tk = ImageTk.PhotoImage(image)
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#
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# result_text = f"Processed image with params: {params}"
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# return image_tk, result_text
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(minDist, param1, param2, minRadius, maxRadius,
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color1_R, color1_V, color1_B, color2_R, color2_V, color2_B) = itemgetter(
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'minDist', 'param1', 'param2', 'minRadius', 'maxRadius',
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'color1_R', 'color1_V', 'color1_B', 'color2_R', 'color2_V', 'color2_B'
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)(params)
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# 1. Acquisition de l'image
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src = dir_path.joinpath('tests/images/balls-full-small.jpg')
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raw_image = cv2.imread(str(src))
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# 2. Boxing des objets via opencv
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gray = cv2.cvtColor(raw_image, cv2.COLOR_BGR2GRAY)
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blurred = cv2.medianBlur(gray, 25)
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circles = cv2.HoughCircles(blurred, cv2.HOUGH_GRADIENT, 1, minDist, param1=param1, param2=param2,
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minRadius=minRadius,
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maxRadius=maxRadius)
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min_rayon = 9999
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cochonnet = None
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boules = []
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if circles is not None:
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circles = np.uint16(np.around(circles))
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for i in circles[0, :]:
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boule = Object(x=int(i[0]), y=int(i[1]), rayon=int(i[2]))
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# 3. Détection de la box la plus petite : cochonnet
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if boule.rayon < min_rayon:
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min_rayon = boule.rayon
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if cochonnet is not None:
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boules.append(cochonnet)
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cochonnet = boule
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else:
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boules.append(boule)
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# 4. Regroupement en liste de boules 1 ou 2 selon la couleur principale de chaque box restante
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hsv = cv2.cvtColor(raw_image, cv2.COLOR_BGR2HSV)
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(h, s, v) = cv2.split(hsv)
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s = s * 2
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s = np.clip(s, 0, 255)
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imghsv = cv2.merge([h, s, v])
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boules_couleurs = []
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for boule in boules:
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half_diametre = int(boule.rayon / 2)
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crop = imghsv[
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boule.y - half_diametre:boule.y + half_diametre,
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boule.x - half_diametre:boule.x + half_diametre,
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].copy()
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pixels = np.float32(crop.reshape(-1, 3))
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n_colors = 2
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criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 200, .1)
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_, labels, palette = cv2.kmeans(pixels, n_colors, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)
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_, counts = np.unique(labels, return_counts=True)
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(b, g, r) = palette[np.argmax(counts)] / 16
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# A modulariser
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boules_couleurs.append(TYPE_1 if b > 4 else TYPE_2)
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# 5. Calcul des distances entre chaque boule et le cochonnet selon le centre des boxs
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boules_distance = {}
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for i, boule in enumerate(boules):
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dist = int(math.sqrt(math.pow(cochonnet.x - boule.x, 2) + math.pow(cochonnet.y - boule.y, 2)))
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boules_distance[i] = dist
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boules_distance = dict(sorted(boules_distance.items(), key=lambda item: item[1]))
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# 6. Liste ordonnée des 6 distances les plus faibles
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boules_proches = [x for x in list(boules_distance)[0:6]]
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# 7. Sortie des 6 couleurs en --- ou - -
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return_text = ""
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img_final = raw_image.copy()
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for i in boules_proches:
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boule = boules[i]
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return_text += f"{boules_couleurs[i]}\n"
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cv2.circle(img_final, (boule.x, boule.y), boule.rayon, (0, 255, 0), 2)
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return img_final, return_text
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@ -1,2 +1,3 @@
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numpy~=2.1.3
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opencv-python
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opencv-python
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pillow
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