import numpy as np import cv2 import sys import matplotlib.pyplot as plt

full_image = cv2.cvtColor(cv2.imread("/work/images/Wheres_Waldo_Full.png"), cv2.COLOR_BGR2RGB) waldo_image = cv2.cvtColor(cv2.imread("/work/images/Wheres_Waldo_Waldo.png"), cv2.COLOR_BGR2RGB) wilma_image = cv2.cvtColor(cv2.imread("/work/images/Wheres_Waldo_Wilma.png"), cv2.COLOR_BGR2RGB) plt.figure(figsize=(20,10)) plt.imshow(full_image) fig, ax = plt.subplots(1,2,figsize=(12, 6)) ax[0].imshow(waldo_image) ax[1].imshow(wilma_image)

image = full_image.mean(-1).astype(np.float32)/255 kernel = waldo_image.mean(-1).astype(np.float32)/255 fig, ax = plt.subplots(1,2,figsize=(12, 6)) ax[0].imshow(image) ax[1].imshow(kernel) # waldo_filter = cv2.filter2D(image, -1, kernel, borderType=cv2.BORDER_CONSTANT) # See here: https://docs.opencv.org/3.4/de/da9/tutorial_template_matching.html waldo_filter = cv2.matchTemplate(image, kernel, cv2.TM_CCOEFF_NORMED) # waldo_position = np.linalg.norm(waldo_filter, axis=-1) waldo_position = np.abs(waldo_filter) print(waldo_position) plt.figure(figsize=(20,10)) viz = np.pad(waldo_position / np.max(waldo_position), [[20, 19], [20, 19]]) plt.imshow(0.7*viz + 0.3 * image) y, x = np.unravel_index(waldo_position.argmax(), waldo_position.shape) print(x, y) plt.scatter(x+20, y+20, s=100, color='r', marker='x')