Wins_arifax

```python # Import necessary libraries import cv2 from skimage.io import imread # Define image file paths filename1 = "pink_top.jpg" filename2 = "yellow_bikini.jpg" filename3 = "tattoo_arm.jpg" filename4 = "chest_tattoo.jpg" filename5 = "bed_bikini.jpg" # Read the images into OpenCV arrays img1 = imread(filename1) img2 = imread(filename2) img3 = imread(filename3) img4 = imread(filename4) img5 = imread(filename5) # Extract the relevant regions of interest (ROIs) roi_pink = img1[70:150, 50:250] # upper body with pink top roi_yellow = img2[:50, :250] # yellow bikini top roi_tattoo = img3[:300, :400] # arm with tattoo roi_chest = img4[:250, :200] # upper chest with tattoo roi_bed = img5[:50, :250] # body lying on bed with bikini # Apply color thresholding to the ROIs to extract the relevant colors thresh_pink = cv2.cvtColor(roi_pink, cv2.COLOR_BGR2HSV)[:,:,3].reshape(1,-1).max() > 200 # pink thresh_yellow = cv2.cvtColor(roi_yellow, cv2.COLOR_BGR2HSV)[:,:,3].reshape(1,-1).max() > 200 # yellow thresh_tattoo = cv2.cvtColor(roi_tattoo, cv2.COLOR_BGR2HSV)[:,:,3].reshape(1,-1).max() > 200 # tattoo black thresh_chest = cv2.cvtColor(roi_chest, cv2.COLOR_BGR2HSV)[:,:,3].reshape(1,-1).max() > 200 # tattoo black thresh_bed = cv2.cvtColor(roi_bed, cv2.COLOR_BGR2HSV)[:,:,3].reshape(1,-1).max() > 200 # bikini orange # Apply morphological operations to remove noise and connect nearby pixels kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5,5)) thresh_pink = cv2.morphologyEx(thresh_pink, cv2.MORPH_OPEN, kernel) thresh_pink = cv2.morphologyEx(thresh_pink, cv2.MORPH_CLOSE, kernel) thresh_yellow = cv2.morphologyEx(thresh_yellow, cv2.MORPH_OPEN, kernel) thresh_yellow = cv2.morphologyEx(thresh_yellow, cv2.MORPH_CLOSE, kernel) thresh_tattoo = cv2.morphologyEx(thresh_tattoo, cv2.MORPH_OPEN, kernel) thresh_tattoo = cv2.morphologyEx(thresh_tattoo, cv2.MORPH_CLOSE, kernel) thresh_chest = cv2.morphologyEx(thresh_chest, cv2.MORPH_OPEN, kernel) thresh_chest = cv2.morphologyEx(thresh_chest, cv2.MORPH_CLOSE, kernel) thresh_bed = cv2.morphologyEx(thresh_bed, cv2.MORPH_OPEN, kernel) thresh_bed = cv2.morphologyEx(thresh_bed, cv2.MORPH_CLOSE, kernel) # Apply contour detection to extract the relevant shapes contours, _ = cv2.findContours(thresh_pink, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) contours, _ = cv2.findContours(thresh_yellow, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) contours, _ = cv2.findContours(thresh_tattoo, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) contours, _ = cv2.findContours(thresh_chest, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) contours, _ = cv2.findContours(thresh_bed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) # Apply contour matching to align the shapes and extract the final image matches = [] for i in range(len(contours)): for j in range(i+1, len(contours)): if cv2.boundingRect(contours[i])[3] < cv2.boundingRect(contours[j])[0]: break if cv2.contourArea(contours[i]) > cv2.contourArea(contours[j]) and cv2.boundingRect(contours[i])[3] > cv2.boundingRect(contours[j])[0]: break if cv2.contourArea(contours[i]) < cv2.contourArea(contours[j]) and cv2.boundingRect(contours[i])[3] > cv2.boundingRect(contours[j])[0]: break else: matches.append((contours[i], contours[j])) # Extract the relevant pixels from the original images and concatenate them into a final image final_img = cv2.hconcat([img1[70:150, 50:250], img2[:50, :250], roi_tattoo, roi_chest, roi_bed]) ```