quickDrawApp.py

import numpy as np
import os
import cv2
from keras.models import load_model
from collections import deque

model = load_model('WeightsOfQuickDraw.h5')

def main():
    Lower_blue = np.array([110, 50, 50])
    Upper_blue = np.array([130, 255, 255])
    blackboard = np.zeros((480, 640, 3), dtype=np.uint8)
    digit = np.zeros((200, 200, 3), dtype=np.uint8)
    emojis = get_emojis()
    cap = cv2.VideoCapture(0)
    pts = deque(maxlen=512)
    pred_class = 0

    while (cap.isOpened()):
        ret, img = cap.read()
        img = cv2.flip(img, 1)
        hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
        kernel = np.ones((5, 5), np.uint8)
        mask = cv2.inRange(hsv, Lower_blue, Upper_blue)
        mask = cv2.erode(mask, kernel, iterations=4)

        cv2.imshow("mask", mask)
        cnts, heir = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2:]
        center = None

        if len(cnts) >= 1:
            cnt = max(cnts, key=cv2.contourArea)
            if cv2.contourArea(cnt) > 200:
                print("Counters > 200")
                ((x, y), radius) = cv2.minEnclosingCircle(cnt)
                cv2.circle(img, (int(x), int(y)), int(radius), (0, 255, 0), 2)
                cv2.circle(img, center, 5, (0, 0, 255), -1)
                M = cv2.moments(cnt)
                center = (int(M['m10'] / M['m00']), int(M['m01'] / M['m00']))
                pts.appendleft(center)
                for i in range(1, len(pts)):
                    if pts[i - 1] is None or pts[i] is None:
                        continue
                    cv2.line(blackboard, pts[i - 1], pts[i], (255, 255, 255), 7)
                    cv2.line(img, pts[i - 1], pts[i], (0, 0, 255), 2)
        elif len(cnts) == 0:
            if len(pts) != []:
                print ("Counters < 200")
                blackboard_gray = cv2.cvtColor(blackboard, cv2.COLOR_BGR2GRAY)
                blur1 = cv2.medianBlur(blackboard_gray, 15)
                blur1 = cv2.GaussianBlur(blur1, (5, 5), 0)
                thresh1 = cv2.threshold(blur1, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
                blackboard_cnts = cv2.findContours(thresh1.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)[1]

                if len(blackboard_cnts) >= 1:
                    cnt = max(blackboard_cnts, key=cv2.contourArea)
                    #cv2.imshow("input image", cnt)
                    print(cv2.contourArea(cnt))
                    if cv2.contourArea(cnt) > 2000:
                        print ("GREATER THAN 2000")
                        x, y, w, h = cv2.boundingRect(cnt)
                        digit = blackboard_gray[y:y + h, x:x + w]
                        pred_probab, pred_class = predict_model(model, digit)
                        print(pred_class, pred_probab)

            pts = deque(maxlen=512)
            blackboard = np.zeros((480, 640, 3), dtype=np.uint8)
            img = overlay(img, emojis[pred_class], 400, 250, 100, 100)
        cv2.imshow("Frame", img)


def predict_model(model, image):
    image_x = 28
    image_y = 28
    img = cv2.resize(image, (image_x, image_y))
    img = np.array(img, dtype=np.float32)
    processed = np.reshape(img, (-1, image_x, image_y, 1))
    print("processed: " + str(processed.shape))
    pred_probab = model.predict(processed)[0]
    pred_class = list(pred_probab).index(max(pred_probab))
    return max(pred_probab), pred_class

def get_emojis():
    emojis_folder = 'qd_emo/'
    emojis = []
    for emoji in range(len(os.listdir(emojis_folder))):
        print(emoji)
        emojis.append(cv2.imread(emojis_folder + str(emoji) + '.png', -1))
    return emojis

def overlay(image, emoji, x, y, w, h):
    emoji = cv2.resize(emoji, (w, h))
    try:
        image[y:y + h, x:x + w] = blend_transparent(image[y:y + h, x:x + w], emoji)
    except:
        pass
    return image


def blend_transparent(face_img, overlay_t_img):
    overlay_img = overlay_t_img[:, :, :3]  # Grab the BRG planes
    overlay_mask = overlay_t_img[:, :, 3:]  # And the alpha plane

    background_mask = 255 - overlay_mask

    # Turn the masks into three channel, so we can use them as weights
    overlay_mask = cv2.cvtColor(overlay_mask, cv2.COLOR_GRAY2BGR)
    background_mask = cv2.cvtColor(background_mask, cv2.COLOR_GRAY2BGR)

    face_part = (face_img * (1 / 255.0)) * (background_mask * (1 / 255.0))
    overlay_part = (overlay_img * (1 / 255.0)) * (overlay_mask * (1 / 255.0))

    return np.uint8(cv2.addWeighted(face_part, 255.0, overlay_part, 255.0, 0.0))


predict_model(model, np.zeros((50, 50, 1), dtype=np.uint8))
if __name__ == '__main__':
    main()