yiking/process.py

from operator import itemgetter

from PIL import Image, ImageTk

import math
from dataclasses import dataclass

import numpy as np
import cv2
from pathlib import Path

dir_path = Path(".").absolute()
TYPE_1 = "_________"
TYPE_2 = "___   ___"

import cv2


def capture_frame_from_webcam(cam_id):
    """
    Captures a single frame from the webcam.

    Returns:
        frame (numpy.ndarray): The captured frame as a NumPy array.
    """
    # Open a connection to the second webcam (index 0)
    cap = cv2.VideoCapture(cam_id)

    if not cap.isOpened():
        raise Exception("Could not open webcam. Please check your webcam connection.")

    try:
        # Capture a single frame
        ret, frame = cap.read()

        if not ret:
            raise Exception("Failed to capture frame from webcam.")

        return frame
    finally:
        # Release the webcam resource
        cap.release()



@dataclass
class Object:
    x: int
    y: int
    rayon: int


def process_frame(params, cam_id):
    """
    Simulates OpenCV processing using parameters from the GUI.

    Args:
        params (dict): A dictionary of variable values passed from the GUI.

    Returns:
        ImageTk.PhotoImage: A Tkinter-compatible image.
        str: A result text description.
    """
    # Simulate processing: for now, return a dummy image and text.
    # width, height = 400, 300
    # image = Image.new("RGB", (width, height),
    #                   color=(params["color1_R_min"] * 4, params["color1_V_min"] * 4, params["color1_B_min"] * 4))
    # image_tk = ImageTk.PhotoImage(image)
    #
    # result_text = f"Processed image with params: {params}"
    # return image_tk, result_text

    (minDist, param1, param2, minRadius, maxRadius,
     color1_R_min, color1_V_min, color1_B_min, color1_R_max, color1_V_max, color1_B_max) = itemgetter(
        'minDist', 'param1', 'param2', 'minRadius', 'maxRadius',
        'color1_R_min', 'color1_V_min', 'color1_B_min', 'color1_R_max', 'color1_V_max', 'color1_B_max'
    )(params)

    # 1. Acquisition de l'image
    # src = dir_path.joinpath('tests/images/balls-full-small.jpg')
    # raw_image = cv2.imread(str(src))

    raw_image = capture_frame_from_webcam(cam_id)

    # 2. Boxing des objets via opencv
    gray = cv2.cvtColor(raw_image, cv2.COLOR_BGR2GRAY)
    blurred = cv2.medianBlur(gray, 25)

    circles = cv2.HoughCircles(blurred, cv2.HOUGH_GRADIENT, 1, minDist, param1=param1, param2=param2,
                               minRadius=minRadius,
                               maxRadius=maxRadius)

    min_rayon = 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]), rayon=int(i[2]))

            # 3. Détection de la box la plus petite : cochonnet
            if boule.rayon < min_rayon:
                min_rayon = boule.rayon
                if cochonnet is not None:
                    boules.append(cochonnet)
                cochonnet = boule
            else:
                boules.append(boule)

    # 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])
    boules_couleurs = []
    boules_bgr = []
    for boule in boules:
        half_diametre = int(boule.rayon / 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, v, r) = palette[np.argmax(counts)]
        boules_bgr.append(f"R:{int(r)} V:{int(v)} B:{int(b)}")

        # On récupère les valeurs de R G et B qui sont à analyser
        if int(
                color1_R_min <= math.floor(r) <= color1_R_max
                and color1_V_min <= math.floor(v) <= color1_V_max
                and color1_B_min <= math.floor(b) <= color1_B_max
        ):
            boules_couleurs.append(TYPE_1)
        else :
            boules_couleurs.append(TYPE_2)

    # 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 - -
    return_text = ""
    img_final = raw_image.copy()
    for i in boules_proches:
        boule = boules[i]
        return_text += f"{boules_couleurs[i]}\n"
        cv2.circle(img_final, (boule.x, boule.y), boule.rayon, (0, 255, 0), 2)
        cv2.putText(img_final, boules_bgr[i], (boule.x, boule.y), cv2.FONT_HERSHEY_SIMPLEX,
                    fontScale=0.75, color=(255, 255, 255), thickness=1, lineType=cv2.LINE_AA)

    return img_final, return_text