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import math
import cv2
import imutils
import numpy as np
from sklearn.preprocessing import MinMaxScaler
from ultralytics import YOLO
from models.birefnet import BiRefNet
from util.utils import check_state_dict
from PIL import Image
import torch
from torchvision import transforms
from openvino.runtime import Core
device = "cuda" if torch.cuda.is_available() else "cpu"
model = AutoModelForImageSegmentation.from_pretrained('/'.join(('zhengpeng7', usage_to_weights_file['General'])), trust_remote_code=True)
model.to(device)
model.eval()
# Input Data
transform_image = transforms.Compose([
transforms.Resize((1024, 1024)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
import torch
from PIL import Image
import torchvision.transforms as transforms
def pred_segmentation(imagepath='../DIS-VD-11#Furniture#17#Table#4317824734_63b46ff6e6_o.jpg', box=[-1, -1, -1, -1]):
print('predicting segmentation...')
# box: left, top, right, bottom
image = Image.open(imagepath)
w, h = image.size[:2]
for idx_coord_value, coord_value in enumerate(box):
if coord_value == -1:
box[idx_coord_value] = [0, 0, w, h][idx_coord_value]
image_crop = image.crop(box)
input_images = transform_image(image_crop).unsqueeze(0)
model.eval()
# Prediction
with torch.no_grad():
preds = model(input_images)[-1].sigmoid()
pred = preds[0].squeeze()
canvas = torch.zeros_like(pred)
box_to_canvas = [int(round(coord_value * (canvas.shape[-1] / w, canvas.shape[-2] / h)[idx_coord_value % 2])) for idx_coord_value, coord_value in enumerate(box)]
pred = torch.nn.functional.interpolate(
pred.unsqueeze(0).unsqueeze(0),
size=(box_to_canvas[3] - box_to_canvas[1], box_to_canvas[2] - box_to_canvas[0]),
mode='bilinear',
align_corners=True
).squeeze()
canvas[box_to_canvas[1]:box_to_canvas[3], box_to_canvas[0]:box_to_canvas[2]] = pred
# Show Results
pred_pil = transforms.ToPILImage()(canvas)
return pred_pil
def pred_bbox(image_path):
print('predicting bounding box...')
image = cv2.imread(image_path)
model = YOLO('models/weights/yolo_finetuned.pt')
# Perform prediction
results = model(image)
boxes = results[0].boxes.xyxy.cpu().numpy()[0]
# Extract the bounding box coordinates
x1, y1, x2, y2 = map(int, list(boxes))
return [x1, y1, x2, y2]
def get_kps_from_pil(pil_image):
print('converting keypoints...')
image_array = np.array(pil_image)
# Find contours using OpenCV
contours, _ = cv2.findContours(image_array, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# Find the largest contour by area
largest_contour = max(contours, key=cv2.contourArea)
largest_contour = np.array(largest_contour)
contour = []
for i in range(len(largest_contour)):
contour.append(largest_contour[i][0])
scaler = MinMaxScaler()
kps = scaler.fit_transform(contour)
kps = np.array(kps)
kps = kps * 299
kps = np.int32(kps)
return kps
def get_features_up(contour):
feature = []
for i in range(0, 300):
position = 0
unsorted_features = []
for j in range(len(contour)):
point = contour[j]
prev_point = point
if j != 0:
prev_point = contour[j - 1]
if point[0] > i and position == 0:
position = 1
elif point[0] < i and position == 0:
position = -1
elif point[0] > i and position == -1:
unsorted_features.append((point[1] + prev_point[1]) // 2)
position = 1
elif point[0] < i and position == 1:
position = -1
unsorted_features.append((point[1] + prev_point[1]) // 2)
elif point[0] == i and position == 1:
unsorted_features.append(point[1])
position = -1
elif point[0] == i and position == -1:
unsorted_features.append(point[1])
position = 1
elif point[0] == i and position == 0:
position = 1
if len(unsorted_features) != 0:
if len(unsorted_features) == 1:
unsorted_features.append((contour[0][1] + contour[-1][1]) // 2)
unsorted_features.sort()
feature.append(max(unsorted_features))
else:
feature.append(-1)
return feature
def get_features_down(contour):
feature = []
for i in range(0, 300):
position = 0
unsorted_features = []
for j in range(len(contour)):
point = contour[j]
prev_point = point
if j != 0:
prev_point = contour[j - 1]
if point[0] > i and position == 0:
position = 1
elif point[0] < i and position == 0:
position = -1
elif point[0] > i and position == -1:
unsorted_features.append((point[1] + prev_point[1]) // 2)
position = 1
elif point[0] < i and position == 1:
position = -1
unsorted_features.append((point[1] + prev_point[1]) // 2)
elif point[0] == i and position == 1:
unsorted_features.append(point[1])
position = -1
elif point[0] == i and position == -1:
unsorted_features.append(point[1])
position = 1
elif point[0] == i and position == 0:
position = 1
if len(unsorted_features) != 0:
if len(unsorted_features) == 1:
unsorted_features.append((contour[0][1] + contour[-1][1]) // 2)
unsorted_features.sort()
feature.append(min(unsorted_features))
else:
feature.append(-1)
return feature
def get_features_right(contour):
feature = []
for i in range(0, 300):
position = 0
unsorted_features = []
for j in range(len(contour)):
point = contour[j]
prev_point = point
if j != 0:
prev_point = contour[j - 1]
if point[1] > i and position == 0:
position = 1
elif point[1] < i and position == 0:
position = -1
elif point[1] > i and position == -1:
unsorted_features.append((point[0] + prev_point[0]) // 2)
position = 1
elif point[1] < i and position == 1:
position = -1
unsorted_features.append((point[0] + prev_point[0]) // 2)
elif point[1] == i and position == 1:
unsorted_features.append(point[0])
position = -1
elif point[1] == i and position == -1:
unsorted_features.append(point[0])
position = 1
elif point[1] == i and position == 0:
position = 1
if len(unsorted_features) != 0:
if len(unsorted_features) == 1:
unsorted_features.append((contour[0][0] + contour[-1][0]) // 2)
unsorted_features.sort()
feature.append(min(unsorted_features))
else:
feature.append(-1)
return feature
def get_features_left(contour):
feature = []
for i in range(0, 300):
position = 0
unsorted_features = []
for j in range(len(contour)):
point = contour[j]
prev_point = point
if j != 0:
prev_point = contour[j - 1]
if point[1] > i and position == 0:
position = 1
elif point[1] < i and position == 0:
position = -1
elif point[1] > i and position == -1:
unsorted_features.append((point[0] + prev_point[0]) // 2)
position = 1
elif point[1] < i and position == 1:
position = -1
unsorted_features.append((point[0] + prev_point[0]) // 2)
elif point[1] == i and position == 1:
unsorted_features.append(point[0])
position = -1
elif point[1] == i and position == -1:
unsorted_features.append(point[0])
position = 1
elif point[1] == i and position == 0:
position = 1
if len(unsorted_features) != 0:
if len(unsorted_features) == 1:
unsorted_features.append((contour[0][0] + contour[-1][0]) // 2)
unsorted_features.sort()
feature.append(max(unsorted_features))
else:
feature.append(-1)
return feature
def extract_features(contour):
print('extracting features...')
return get_features_down(contour) + get_features_up(contour) + get_features_right(contour) + get_features_left(contour)
def final_features(image_path):
image = Image.open(image_path)
image = rotate_image(image)
pil_image = pred_segmentation(image, pred_bbox(image_path))
contour = get_kps_from_pil(pil_image)
return extract_features(contour)
def predict_kps(image):
model = YOLO('models/weights/yolo_finetuned.pt')
# Perform prediction
results = model(image)
kps = results[0].masks.xy[0]
return kps
def calculate_angle(p1, p2):
delta_y = p2[1] - p1[1]
delta_x = p2[0] - p1[0]
return math.degrees(np.arctan2(delta_y, delta_x))
# Function to rotate points by a given angle
def calculate_square(img):
np_image = np.array(img)
# Convert RGB (PIL) to BGR (OpenCV)
if np_image.ndim == 3: # Check if the image is colored
cv_image = cv2.cvtColor(np_image, cv2.COLOR_RGB2BGR)
else:
# For grayscale images, no conversion is needed
cv_image = np_image
rect = cv2.minAreaRect(predict_kps(cv_image))
box = cv2.boxPoints(rect)
box = np.int32(box)
return box
def rotate_image(image):
square = calculate_square(image)
# Calculate the lengths of the sides
side_lengths = [np.linalg.norm(square[i] - square[i + 1]) for i in range(len(square) - 1)]
# Find the indices of the larger side
max_index = np.argmax(side_lengths)
# Find the two points that form the largest side
p1, p2 = square[max_index], square[max_index + 1]
# Calculate the angle between this side and the horizontal axis
angle = calculate_angle(p1, p2)
# Rotate the square to align the largest side with the horizontal axis
rotated_image = image.rotate(angle)
return rotated_image
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