Air_Draw_RGB.py

import cv2
import numpy as np
from PIL import Image
import mediapipe as mp
import time
import gradio as gr

DOMINANT_HAND = "Right"

width, height = 1280, 720
width_, height_, = 256, 144


def find_hands(brain, img):
    img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # opencv image is in BGR form but mp is trained with RGB
    results = brain.process(img_rgb) # process finds the hands and outputs classification and 21 landmarks for each hand
    all_hands = [] # initializing array to hold the dictionary for the hands
    h, w, _ = img.shape # get height and width of image for scaling
    if results.multi_hand_landmarks:
        for hand_type, hand_lms in zip(results.multi_handedness, results.multi_hand_landmarks): # elegant solution for mp list object traversal
            hand = {} # initializing dict for each hand
            lm_list = [] # landmarks array for all 21 point of the hand
            for lm in hand_lms.landmark:
                px, py, pz = int(lm.x * w), int(lm.y * h), int(lm.z * w) # scaling landmark points to image size for frame coordinates
                lm_list.append([px, py, pz])

            hand["lm_list"] = lm_list # add "lm_list" key for all landmark points of the hand
            hand["type"] = hand_type.classification[0].label # adds the label (left/right) for the hand
            all_hands.append(hand) # appends the dict
    return all_hands


def is_drawing(index, thumb): # proximity function with arbitrary threshold
    npindex = np.array((index[0], index[1]))
    npthumb = np.array((thumb[0], thumb[1]))
    if np.linalg.norm(npindex - npthumb) < 30:
        return True
    else:
        return False


def save(landmarks): # brute force finger orientation checking
    if landmarks[8][1] < landmarks[6][1]:
        if landmarks[12][1] < landmarks[10][1]:
            if landmarks[16][1] < landmarks[14][1]:
                if landmarks[20][1] < landmarks[18][1]:
                    return True
    else:
        return False

def clear(landmarks): # brute force finger orientation checking
    if landmarks[4][1] < landmarks[3][1] < landmarks[2][1] < landmarks[8][1]:
        return True
    else:
        return False

drawing_flag = False
sleepy_time = time.time()

if __name__ == '__main__':
    cam = cv2.VideoCapture(0)
    cam.set(3, width)
    cam.set(4, height)

    detector = mp.solutions.hands.Hands(min_detection_confidence=0.8) # initialize mp model
    # paper = np.zeros((width, height, 4), np.uint8)
    paper = np.zeros((height, width, 3), dtype=np.uint8) # create blank page
    paper.fill(255)

    past_holder = () # coordinates holder
    palette = cv2.imread('palette.jpg')

    output_frames = []
    page_num = 0
    # runny = 1
    color = (0, 0, 0)
    while True:
        # runny -= 1
        x, rgb_image = cam.read()
        rgb_image_f = cv2.flip(np.asanyarray(rgb_image), 1)

        hands = find_hands(detector, rgb_image_f)

        try:
            if hands:
                hand1 = hands[0] if hands[0]["type"] == DOMINANT_HAND else hands[1]
                lm_list1 = hand1["lm_list"]  # List of 21 Landmarks
                handedness = hand1["type"]

                if handedness == DOMINANT_HAND:
                    idx_coords = lm_list1[8][0], lm_list1[8][1]  # 0 is width (bigger)
                    # print(idx_coords)
                    cv2.circle(rgb_image_f, idx_coords, 5, color, cv2.FILLED)

                    if idx_coords[1] < 72: # brute force but should be extremely marginally faster lol
                        if idx_coords[0] < 142:  # red
                            color = (0, 0, 255)
                        if 142 < idx_coords[0] < 285:  # orange
                            color = (0, 115, 255)
                        if 285 < idx_coords[0] < 426:  # yellow
                            color = (0, 229, 255)
                        if 426 < idx_coords[0] < 569:  # green
                            color = (0, 195, 88)
                        if 569 < idx_coords[0] < 711:  # blue
                            color = (195, 85, 0)
                        if 711 < idx_coords[0] < 853:  # indigo
                            color = (195, 0, 68)
                        if 853 < idx_coords[0] < 996:  # violet
                            color = (195, 0, 143)
                        if 996 < idx_coords[0] < 1137:  # black
                            color = (0, 0, 0)
                        if 1137 < idx_coords[0]:  # white / eraser
                            color = (255, 255, 255)

                    if len(past_holder) and drawing_flag: # start drawing
                        cv2.line(paper, past_holder, idx_coords, color, 5)
                        cv2.line(rgb_image_f, past_holder, idx_coords, color, 5)
                        # paper[idx_coords[0]][idx_coords[1]][0] = 255
                        # paper[idx_coords[0]][idx_coords[1]][3] = 255
                        cv2.circle(rgb_image_f, idx_coords, 5, color, cv2.FILLED)

                    if save(lm_list1) and time.time() - sleepy_time > 3: # save page, 3 secs arbitrary, just to not iterate every loop iteration
                        paper[0:height_, w - width_: w] = 255
                        paper = cv2.cvtColor(paper, cv2.COLOR_BGR2RGB)
                        im = Image.fromarray(paper)
                        im.save("paper%s.png" % page_num)
                        print("saved")
                        sleepy_time = time.time()
                        paper = cv2.cvtColor(paper, cv2.COLOR_RGB2BGR)
                        page_num += 1

                    if clear(lm_list1) and time.time() - sleepy_time > 3: # clear page
                        paper = np.zeros((height, width, 3), dtype=np.uint8)
                        paper.fill(255)
                        print("page cleared")
                        sleepy_time = time.time()

                    past_holder = idx_coords

            if is_drawing(idx_coords, lm_list1[4]):  # 4 is thumb for intuitive "hold pen" to draw
                drawing_flag = True
            else:
                drawing_flag = False

        except:
            pass

        finally:
            rgb_image_f[0:72, ] = palette
            presenter = cv2.resize(rgb_image_f, (width_, height_))
            h, w, _ = rgb_image_f.shape
            paper[0:height_, w - width_: w] = presenter
            cv2.imshow("Image", rgb_image_f)
            cv2.imshow("paper", paper)
            key = cv2.waitKey(1)
            if key & 0xFF == ord('q') or key == 27:  # Press esc or 'q' to close the image window
                break
            output_frames.append(paper)


    print("output: ", type(output_frames))