q-viper

import dstack.controls as ctrl
import dstack as ds
import pandas as pd
# import plotly.express as px
import plotly.graph_objs as go
# from plotly import figure_factory as FF 


@ds.cache()
def get_data():

q-viper

...................................
...................................
                    cv2.imshow("Drawing", dc)
        if chr(key) == "s":
            show(drawn)
            d = drawn.copy().astype(np.uint8) 
            r = recognition(cv2.cvtColor(d, cv2.COLOR_BGR2GRAY), 'show')
        cv2.rectangle(clone, (left, top), (right, bottom), (0, 255, 0), 2) # draw a ROI
        cv2.imshow("Feed", clone) # show live feed 

q-viper

.......................
.......................
def prediction(img):
    curr_dir = os.getcwd()+"/dcr/"
    json_file = open(curr_dir+'cnn2/cnn2.json', 'r')
    loaded_model_json = json_file.read()     # load json and create model
    json_file.close()
    loaded_model = model_from_json(loaded_model_json)  
    loaded_model.load_weights(curr_dir+"cnn2/cnn2.h5") # load weights into new model
    ..................................

q-viper

 .....................................................
                .....................................................
                if erase==True:
                    cv2.circle(canvas, (m[0], m[1]), 5, draw_color, -3)
                    erimg=cv2.circle(canvas.copy(), (m[0], m[1]), 5, (0, 0, 0), -3)            
                    cv2.circle(c, (m[0], m[1]), 5, (0, 0, 255), -3)
                    e = cv2.erode(erimg, (3, 3), iterations=5)
                    drawn = cv2.resize(e, (clone.shape[1], clone.shape[0]))
                    c = cv2.resize(c, (clone.shape[1], clone.shape[0]))
                    drawn_new = drawn+c

q-viper

                ..........................................
                ...........................................
                c = np.zeros(canvas.shape, dtype=np.uint8)
                cv2.circle(c, (m[0], m[1]), 5, pointer_color, -3)
                cv2.circle(clone, (right+m[0], top+m[1]), 5, pointer_color, -3)
                if erase==True:
                    cv2.circle(canvas, (m[0], m[1]), 5, draw_color, -3)
                    erimg=cv2.circle(canvas.copy(), (m[0], m[1]), 5, (0, 0, 0), -3)            
                    cv2.circle(c, (m[0], m[1]), 5, (0, 0, 255), -3)

q-viper

 ...........................
            ..........................
            if hand is not None:
                if chr(key) == "x": # if pressed x, erase
                    draw_color = (255, 255, 255)
                    pointer_color = (0, 0, 255)
                    erase = True
                if chr(key) == "c":
                    draw_color = (0, 255, 0)
                    pointer_color = (255, 0, 0)

q-viper

                ####################################################
                ####################################################
                cv2.circle(c, (m[0], m[1]), 5, pointer_color, -3)
                cv2.circle(clone, (right+m[0], top+m[1]), 5, pointer_color, -3)   
                cv2.imshow("mask", thresholded)
                cv2.imshow("c", c)                
                cv2.circle(canvas, (m[0], m[1]), 5, draw_color, -3)
                e = cv2.erode(canvas, (3, 3), iterations=5)
                drawn = cv2.resize(e, (clone.shape[1], clone.shape[0]))

q-viper

def running_average(bg_img, image, aweight):
    if bg_img is None:
        bg_img = image.copy().astype("float")
    else:
        cv2.accumulateWeighted(image, bg_img, aweight)    
    return bg_img
def get_contours(bg_img, image, threshold=10):    
    diff = cv2.absdiff(bg_img.astype("uint8"), image)    # abs diff betn img and bg    
    _, th = cv2.threshold(diff, threshold, 255, cv2.THRESH_BINARY)
    (cnts, _) = cv2.findContours(th.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)    

q-viper

###########################
    ########################
    else: # if not to take average
        num_frames = 0                        
        hand = get_contours(bg_img, gray) # take our segmented hand
        take_average=False
        if hand is not None:
            thresholded, segmented = hand
            cv2.drawContours(clone, [segmented+(right,top)], -1, (0, 0, 255))    
            cv2.imshow("mask", thresholded)

q-viper

........................
    ........................    
    else: # if not to take average
        num_frames = 0                               
        hand = get_contours(bg_img, gray) # take our segmented hand
        take_average=False
        if hand is not None:
            thresholded, segmented = hand
            cv2.drawContours(clone, [segmented+(right,top)], -1, (0, 0, 255))    
            cv2.imshow("mask", thresholded)