predict_from_camera.py

import os
import sys
import cv2
import numpy as np
import cPickle
import imutils
import traceback
#import cPickle as pickle
#import cloudpickle as pickle
#from sklearn.externals import joblib
#import joblib
from common.config import get_config
from common.image_transformation import apply_image_transformation
from common.image_transformation import resize_image


# global variables
bg = None


### Function - To find the running average over the background
def run_avg(image, aWeight):
    global bg
    # initialize the background
    if bg is None:
        bg = image.copy().astype("float")
        return
    # compute weighted average, accumulate it and update the background
    cv2.accumulateWeighted(image, bg, aWeight)


### Function - To segment the region of hand in the image
def segment(image, threshold=25):
    global bg
    # find the absolute difference between background and current frame
    diff = cv2.absdiff(bg.astype("uint8"), image)
    # threshold the diff image so that we get the foreground
    thresholded = cv2.threshold(diff,
                                threshold,
                                255,
                                cv2.THRESH_BINARY)[1]
    # get the contours in the thresholded image
    (_, cnts, _) = cv2.findContours(thresholded.copy(),
                                    cv2.RETR_EXTERNAL,
                                    cv2.CHAIN_APPROX_SIMPLE)
    # return None, if no contours detected
    if len(cnts) == 0:
        return
    else:
        # based on contour area, get the maximum contour which is the hand
        segmented = max(cnts, key=cv2.contourArea)
        return (thresholded, segmented)


### Main function
if __name__ == "__main__":
    # initialize weight for running average
    aWeight = 0.5
    # get the reference to the webcam
    camera = cv2.VideoCapture(1)
    # region of interest (ROI) coordinates
    top, right, bottom, left = 20, 380, 320, 680
    # initialize num of frames
    num_frames = 0
    # keep looping, until interrupted
    while(True):
        # get the current frame
        (grabbed, frame) = camera.read()
        # resize the frame
        frame = imutils.resize(frame, width=700)
        # flip the frame so that it is not the mirror view
        frame = cv2.flip(frame, 1)
        # clone the frame
        clone = frame.copy()
        # get the height and width of the frame
        (height, width) = frame.shape[:2]
        # get the ROI
        roi = frame[top:bottom, right:left]
        # convert the roi to grayscale and blur it
        gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
        gray = cv2.GaussianBlur(gray, (7, 7), 0)
        # to get the background, keep looking till a threshold is reached
        # so that our running average model gets calibrated
        if num_frames < 30:
            run_avg(gray, aWeight)
        else:
            # segment the hand region
            hand = segment(gray)
            # check whether hand region is segmented
            if hand is not None:
                # if yes, unpack the thresholded image and
                # segmented region
                (thresholded, segmented) = hand
                # draw the segmented region and display the frame
                cv2.drawContours(clone, [segmented + (right, top)], -1, (0, 0, 255))
                cv2.imshow("Thesholded", thresholded)

        # draw the segmented hand
        cv2.rectangle(clone, (left, top), (right, bottom), (0,255,0), 2)
        # increment the number of frames
        num_frames += 1
        # display the frame with segmented hand
        cv2.imshow("Video Feed", clone)
        # observe the keypress by the user
        keypress = cv2.waitKey(1) & 0xFF
        # if the user pressed "q", then stop looping
        if keypress == ord("q"):
            break

# free up memory
camera.release()
cv2.destroyAllWindows()