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committing v5 and v6 ( final code used in demo)
Our prototype succeeded and got the top award from project invent, the "moonshot" award, for the most novel and impactful idea for the pacific region of the competition. We got 500 dollars of initial VC funding as our award.
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,16 +1,16 @@ | ||
| from PIL import Image | ||
| import os | ||
| import argparse | ||
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| def rescale_images (directory,size): | ||
| for img in os.listdir(directory): | ||
| im = Image.open(directory+img) | ||
| im_resized = im.resize(size, Image.ANTIALIAS) | ||
| im_resized.save(directory+img) | ||
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| if __name__ == "__main__": | ||
| parser = argparse.ArgumentParser(description="Rescale Images") | ||
| parser.add_argument('-d', '--directory', type=str, required=True, help='Dir') | ||
| parser.add_argument('-s', '--size', type=int,nargs=2, required=True, metavar=int) | ||
| args = parser.parse_args() | ||
| rescale_images(args.directory, args.size) | ||
| from PIL import Image | ||
| import os | ||
| import argparse | ||
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| def rescale_images (directory,size): | ||
| for img in os.listdir(directory): | ||
| im = Image.open(directory+img) | ||
| im_resized = im.resize(size, Image.ANTIALIAS) | ||
| im_resized.save(directory+img) | ||
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| if __name__ == "__main__": | ||
| parser = argparse.ArgumentParser(description="Rescale Images") | ||
| parser.add_argument('-d', '--directory', type=str, required=True, help='Dir') | ||
| parser.add_argument('-s', '--size', type=int,nargs=2, required=True, metavar=int) | ||
| args = parser.parse_args() | ||
| rescale_images(args.directory, args.size) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,38 +1,38 @@ | ||
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| import pyttsx3 | ||
| from gpiozero import Button | ||
| from gpiozero import Buzzer | ||
| from time import sleep | ||
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| button = Button(11) | ||
| motor1 = Buzzer(12) | ||
| motor2 = Buzzer(13) | ||
| motor3 = Buzzer(16) | ||
| text_speech = pyttsx3.init() | ||
| all = 0 | ||
| bathroom = 1 | ||
| chair = 2 | ||
| door = 3 | ||
| counter = 0 | ||
| mode = counter%4 | ||
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| #while True: | ||
| # if button.is_pressed: | ||
| # if (mode == all): | ||
| # text_speech.say("all") | ||
| # text_speech.runAndWait() | ||
| ## elif(mode == bathroom): | ||
| # text_speech.say("bathroom") | ||
| # text_speech.runAndWait() | ||
| # elif(mode == chair): | ||
| # text_speech.say("chair") | ||
| # text_speech.runAndWait() | ||
| # elif(mode == door): | ||
| # text_speech.say("door") | ||
| # text_speech.runAndWait() | ||
| # counter += 1 | ||
| # else: | ||
| # print(".") | ||
| # sleep(1) | ||
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| import pyttsx3 | ||
| from gpiozero import Button | ||
| from gpiozero import Buzzer | ||
| from time import sleep | ||
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| button = Button(11) | ||
| motor1 = Buzzer(12) | ||
| motor2 = Buzzer(13) | ||
| motor3 = Buzzer(16) | ||
| text_speech = pyttsx3.init() | ||
| all = 0 | ||
| bathroom = 1 | ||
| chair = 2 | ||
| door = 3 | ||
| counter = 0 | ||
| mode = counter%4 | ||
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| #while True: | ||
| # if button.is_pressed: | ||
| # if (mode == all): | ||
| # text_speech.say("all") | ||
| # text_speech.runAndWait() | ||
| ## elif(mode == bathroom): | ||
| # text_speech.say("bathroom") | ||
| # text_speech.runAndWait() | ||
| # elif(mode == chair): | ||
| # text_speech.say("chair") | ||
| # text_speech.runAndWait() | ||
| # elif(mode == door): | ||
| # text_speech.say("door") | ||
| # text_speech.runAndWait() | ||
| # counter += 1 | ||
| # else: | ||
| # print(".") | ||
| # sleep(1) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,36 +1,36 @@ | ||
| #from tensorflow.keras import Sequential | ||
| #from tensorflow.keras.layers import Flatten, Dense, Conv2D, MaxPooling2D, Dropout | ||
| import numpy as np | ||
| import pandas as pd | ||
| import cv2 | ||
| import random | ||
| from pathlib import Path | ||
| from tqdm import tqdm | ||
| #from tensorflow.keras.preprocessing.image import ImageDataGenerator, load_img, img_to_array | ||
| from keras.utils import to_categorical | ||
| import numpy as np | ||
| #from sklearn.mode_selection import train_test_split | ||
| import os | ||
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| labels = [] | ||
| ds_dir = "C:/Users/Soham Kulkarni/OneDrive/Documents/GitHub/vechtor/v4/doorimages/" | ||
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| image_size = 224 # Define the image size here | ||
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| def create_dataset(category, label, dataset): | ||
| for img in tqdm(category): | ||
| image_path = os.path.join(ds_dir, img) | ||
| try: | ||
| image = cv2.imread(image_path,cv2.IMREAD_COLOR) | ||
| image = cv2.resize(image,(image_size, image_size)) | ||
| except: | ||
| continue | ||
| dataset.append([np.array(image),np.array(label)]) | ||
| random.shuffle(dataset) | ||
| return dataset | ||
| trash = "C:/Users/Soham Kulkarni/OneDrive/Documents/GitHub/vechtor/v4/trash/" | ||
| door = "C:/Users/Soham Kulkarni/OneDrive/Documents/GitHub/vechtor/v4/doortestimages/" | ||
| dataset = [] | ||
| dataset = create_dataset(trash, 1, dataset) | ||
| #from tensorflow.keras import Sequential | ||
| #from tensorflow.keras.layers import Flatten, Dense, Conv2D, MaxPooling2D, Dropout | ||
| import numpy as np | ||
| import pandas as pd | ||
| import cv2 | ||
| import random | ||
| from pathlib import Path | ||
| from tqdm import tqdm | ||
| #from tensorflow.keras.preprocessing.image import ImageDataGenerator, load_img, img_to_array | ||
| from keras.utils import to_categorical | ||
| import numpy as np | ||
| #from sklearn.mode_selection import train_test_split | ||
| import os | ||
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| labels = [] | ||
| ds_dir = "C:/Users/Soham Kulkarni/OneDrive/Documents/GitHub/vechtor/v4/doorimages/" | ||
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| image_size = 224 # Define the image size here | ||
|
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| def create_dataset(category, label, dataset): | ||
| for img in tqdm(category): | ||
| image_path = os.path.join(ds_dir, img) | ||
| try: | ||
| image = cv2.imread(image_path,cv2.IMREAD_COLOR) | ||
| image = cv2.resize(image,(image_size, image_size)) | ||
| except: | ||
| continue | ||
| dataset.append([np.array(image),np.array(label)]) | ||
| random.shuffle(dataset) | ||
| return dataset | ||
| trash = "C:/Users/Soham Kulkarni/OneDrive/Documents/GitHub/vechtor/v4/trash/" | ||
| door = "C:/Users/Soham Kulkarni/OneDrive/Documents/GitHub/vechtor/v4/doortestimages/" | ||
| dataset = [] | ||
| dataset = create_dataset(trash, 1, dataset) | ||
| dataset = create_dataset(door, 2, dataset) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,37 +1,37 @@ | ||
| import matplotlib.pyplot as plt | ||
| import matplotlib.image as mpimg | ||
| import os | ||
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| def save_image(like_value, photo, img): | ||
| filename = "C:/Users/Soham Kulkarni/OneDrive/Documents/GitHub/vechtor/v4/" | ||
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| # body like | ||
| if like_value == 's': | ||
| print('doorimages') | ||
| filename += "doortestimages/" + photo | ||
| plt.imsave(filename, img) | ||
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| # body dislike | ||
| if like_value == 'd': | ||
| print('notdoor') | ||
| filename += 'trash/' + photo | ||
| plt.imsave(filename, img) | ||
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| def main(): | ||
| base_path = "C:/Users/Soham Kulkarni/OneDrive/Documents/GitHub/vechtor/v4/doorimages/" | ||
| for photo in os.listdir(base_path): | ||
| photo_path = base_path + photo | ||
| img = mpimg.imread(photo_path) | ||
| plt.imshow(img) | ||
| plt.show() | ||
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| like_value = input() | ||
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| # save pic to new folder and delete it from 'to_label' folder | ||
| save_image(like_value, photo, img) | ||
| os.remove(photo_path) | ||
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| if __name__ == "__main__": | ||
| import matplotlib.pyplot as plt | ||
| import matplotlib.image as mpimg | ||
| import os | ||
|
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| def save_image(like_value, photo, img): | ||
| filename = "C:/Users/Soham Kulkarni/OneDrive/Documents/GitHub/vechtor/v4/" | ||
|
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| # body like | ||
| if like_value == 's': | ||
| print('doorimages') | ||
| filename += "doortestimages/" + photo | ||
| plt.imsave(filename, img) | ||
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| # body dislike | ||
| if like_value == 'd': | ||
| print('notdoor') | ||
| filename += 'trash/' + photo | ||
| plt.imsave(filename, img) | ||
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| def main(): | ||
| base_path = "C:/Users/Soham Kulkarni/OneDrive/Documents/GitHub/vechtor/v4/doorimages/" | ||
| for photo in os.listdir(base_path): | ||
| photo_path = base_path + photo | ||
| img = mpimg.imread(photo_path) | ||
| plt.imshow(img) | ||
| plt.show() | ||
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| like_value = input() | ||
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| # save pic to new folder and delete it from 'to_label' folder | ||
| save_image(like_value, photo, img) | ||
| os.remove(photo_path) | ||
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| if __name__ == "__main__": | ||
| main() |
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