Commit a4f18277 authored by Pyone Thant Win's avatar Pyone Thant Win
Browse files

Added scripts

parent f3c66703
CREATE DATABASE calorie_estimation;
\ No newline at end of file
CREATE TABLE credentials(
user_id serial PRIMARY KEY,
password VARCHAR(50) NOT NULL,
email VARCHAR(100) NOT NULL,
first_name CHAR(50) NOT NULL,
last_name CHAR(50) NOT NULL);
\ No newline at end of file
from imageai.Prediction.Custom import ModelTraining
model_trainer = ModelTraining()
model_trainer.trainModel(num_objects=10, num_experiments=200, enhance_data=True, batch_size=32, show_network_summary=True)
import cv2
def getFrames(path_to_vid, frameCount):
cap = cv2.VideoCapture(path_to_vid)
i = 0
while cap.isOpened():
ret, frame =
if ret == False:
if i != frameCount:
i+= 1
\ No newline at end of file
# import the necessary packages
from imutils import paths
import numpy as np
import argparse
import imutils
import cv2
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--images", type=str, required=True,
help="path to input directory of images to stitch")
ap.add_argument("-o", "--output", type=str, required=True,
help="path to the output image")
ap.add_argument("-c", "--crop", type=int, default=0,
help="whether to crop out largest rectangular region")
args = vars(ap.parse_args())
# grab the paths to the input images and initialize our images list
print("[INFO] loading images...")
imagePaths = sorted(list(paths.list_images(args["images"])))
images = []
# loop over the image paths, load each one, and add them to our
# images to stich list
for imagePath in imagePaths:
image = cv2.imread(imagePath)
# initialize OpenCV's image sticher object and then perform the image
# stitching
print("[INFO] stitching images...")
stitcher = cv2.createStitcher() if imutils.is_cv3() else cv2.Stitcher_create()
(status, stitched) = stitcher.stitch(images)
# if the status is '0', then OpenCV successfully performed image
# stitching
if status == 0:
# check to see if we supposed to crop out the largest rectangular
# region from the stitched image
if args["crop"] > 0:
# create a 10 pixel border surrounding the stitched image
print("[INFO] cropping...")
stitched = cv2.copyMakeBorder(stitched, 10, 10, 10, 10,
cv2.BORDER_CONSTANT, (0, 0, 0))
# convert the stitched image to grayscale and threshold it
# such that all pixels greater than zero are set to 255
# (foreground) while all others remain 0 (background)
gray = cv2.cvtColor(stitched, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY)[1]
# find all external contours in the threshold image then find
# the *largest* contour which will be the contour/outline of
# the stitched image
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cnts = imutils.grab_contours(cnts)
c = max(cnts, key=cv2.contourArea)
# allocate memory for the mask which will contain the
# rectangular bounding box of the stitched image region
mask = np.zeros(thresh.shape, dtype="uint8")
(x, y, w, h) = cv2.boundingRect(c)
cv2.rectangle(mask, (x, y), (x + w, y + h), 255, -1)
# create two copies of the mask: one to serve as our actual
# minimum rectangular region and another to serve as a counter
# for how many pixels need to be removed to form the minimum
# rectangular region
minRect = mask.copy()
sub = mask.copy()
# keep looping until there are no non-zero pixels left in the
# subtracted image
while cv2.countNonZero(sub) > 0:
# erode the minimum rectangular mask and then subtract
# the thresholded image from the minimum rectangular mask
# so we can count if there are any non-zero pixels left
minRect = cv2.erode(minRect, None)
sub = cv2.subtract(minRect, thresh)
# find contours in the minimum rectangular mask and then
# extract the bounding box (x, y)-coordinates
cnts = cv2.findContours(minRect.copy(), cv2.RETR_EXTERNAL,
cnts = imutils.grab_contours(cnts)
c = max(cnts, key=cv2.contourArea)
(x, y, w, h) = cv2.boundingRect(c)
# use the bounding box coordinates to extract the our final
# stitched image
stitched = stitched[y:y + h, x:x + w]
# write the output stitched image to disk
cv2.imwrite(args["output"], stitched)
# display the output stitched image to our screen
cv2.imshow("Stitched", stitched)
# otherwise the stitching failed, likely due to not enough keypoints)
# being detected
print("[INFO] image stitching failed ({})".format(status))
\ No newline at end of file
CREATE TABLE meal_log(
meal_id serial,
user_id integer NOT NULL,
food CHAR(50) NOT NULL,
calorie int NOT NULL,
time_eaten TIME,
date_eat DATE
\ No newline at end of file
CREATE TABLE overview(
user_id int NOT NULL,
date_eaten DATE,
daily_calories int
\ No newline at end of file
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment