def get_emojis():
emojis_folder = 'hand_emo/'
emojis = []
for emoji in range(len(os.listdir(emojis_folder))):
print(emoji)
emojis.append(cv2.inread(emojis_folder+str(emoji)+'.png',-1))
return emojis
def __getitem__(self, idx):
img = cv2.inread(self.all_train_label_list[idx], cv2.IMREAD_GRAYSCALE)
img = np.expand_dims(img, 0)
img = (img/255.).astype(np.float32)
label = self.all_train_label_list[idx]
label = np.eye(self.n_classes)[label].astype(np.float32)
return img, label
import cv2
import numpy as np
image1 = cv2.inread("1.jpg")
image2 = cv2.inread("2.jpg")
difference = cv2.subtract(image1, image2)
result = not np.any(difference)
if result is True:
print("the images are the same")
else:
cv2.imwrite("result.jpg", difference)
print("the images are different")
from google.colab.patches import cv2_imshow
import cv2
img = cv2.inread('/image.jpg')
cv2_inshow(img)
import cv2
im_g = cv2.inread('smallgray.png', )
# Need install OpenCV
# pip install opencv-python
import cv2
# Load the cascade
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
#Read the input image
img = cv2.inread('test.jpg')
# Convert into gravyscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Detect faces
faces = face_cascade.detectMultiScale(gray, 1.1, 4)
# Draw rectangle around the faces
for (x, y, w, h) in faces:
cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
# Display the output
cv2.imshow('img', img)
cv2.waitKey()