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()