import math

import cv2
import numpy as np

def recvall(sock, n):
    data = b''
    while len(data) < n:
        packet = sock.recv(n - len(data))
        if not packet:
            return None
        data += packet
    return data

def distance(p1, p2):
    return math.sqrt((p2[0] - p1[0])**2 + (p2[1] - p1[1])**2)

import numpy as np

def normalize(move):
    left_hip = move[11]   # Left Hip
    right_hip = move[12]  # Right Hip
    nose = move[0]        # Nose (głowa)

    # Środek bioder
    center = (left_hip + right_hip) / 2

    # Przesunięcie względem środka
    normalized_keypoints = move - center

    # Zamiast max_dist używamy stałej miary "rozmiaru ciała"
    body_height = np.linalg.norm(nose[:2] - center[:2])  # np. odległość biodra-głowa

    if body_height > 0:
        normalized_keypoints[:, :2] /= body_height

    draw = normalized_keypoints[:, :2]
    return draw

def find_closest(moves, target):
    return min(moves, key=lambda t: abs(t[0] - target))

def resize_with_padding(image, target_size=(640, 640)):
    h, w = image.shape[:2]
    target_w, target_h = target_size

    # Oblicz współczynnik skalowania, zachowując proporcje
    scale = min(target_w / w, target_h / h)
    new_w, new_h = int(w * scale), int(h * scale)

    # Resize obrazu
    resized_image = cv2.resize(image, (new_w, new_h), interpolation=cv2.INTER_AREA)

    # Stwórz tło (czarne) o wymiarach docelowych
    output_image = np.zeros((target_h, target_w, 3), dtype=np.uint8)

    # Oblicz offsety do wyśrodkowania obrazu
    x_offset = (target_w - new_w) // 2
    y_offset = (target_h - new_h) // 2

    # Wklej resized image na tło
    output_image[y_offset:y_offset+new_h, x_offset:x_offset+new_w] = resized_image

    return output_image