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import cv2
import numpy as np
import torch
from matplotlib import pyplot as plt
from ultralytics import YOLO
DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# Data
test_image = "data/DJI_20240905091530_0003_W.JPG"
LABELS = {0: "Boş", 1: "Çelik Direkler", 2: "Kafes Kule", 3: "Kablo", 4: "Ahşap Kule"}
colorMap = {"Boş":"#ffffff", "Çelik Direkler":"#0000ff", "Kafes Kule":"#ff0000", "Kablo":"#00ff00", "Ahşap Kule":"#ff0000"}
# Load a model
model = YOLO("Weight/yolov9c-cable-seg.pt") # load a custom model
model.fuse()
def ParseResults(results, threshold=0.5, scale_masks=True):
batches = []
SCORES = torch.Tensor([]).to(DEVICE)
CLASSES = torch.Tensor([]).to(DEVICE)
MASKS = torch.Tensor([]).to(DEVICE)
BOXES = torch.Tensor([]).to(DEVICE)
with torch.no_grad():
for result in results:
original_shape = result.orig_shape
_scores = result.boxes.conf # 7
_classes = result.boxes.cls # 7
_masks = result.masks.data # 7, 480, 640
_boxes = result.boxes.xyxy # 7, 4
# Threshold Filter
conditions = _scores > threshold
SCORES = torch.cat((SCORES, _scores[conditions]), dim=0)
CLASSES = torch.cat((CLASSES, _classes[conditions]), dim=0)
BOXES = torch.cat((BOXES, _boxes[conditions]), dim=0)
mask = _masks[conditions]
if scale_masks:
mask = ScaleMasks(mask, original_shape[:2])
MASKS = torch.cat((MASKS, mask), dim=0)
batches += [(SCORES, CLASSES, MASKS, BOXES)]
return batches
def ScaleMasks(masks: torch.Tensor, shape: tuple) -> torch.Tensor:
masks = masks.unsqueeze(0)
interpolatedMask:torch.Tensor = torch.nn.functional.interpolate(masks, shape, mode="nearest")
interpolatedMask = interpolatedMask.squeeze(0)
return interpolatedMask
def DrawResults(image, scores: torch.Tensor, classes: torch.Tensor, masks: torch.Tensor, boxes: torch.Tensor, labels:dict=LABELS, class_filter:list=None):
_image = np.array(image).copy()
_image = cv2.cvtColor(_image, cv2.COLOR_BGR2RGB)
maskCanvas = np.zeros_like(_image)
with torch.no_grad():
scores = scores.cpu().numpy()
classes = classes.cpu().numpy().astype(np.int32)
masks = masks.cpu().numpy()
boxes = boxes.cpu().numpy()
for score, cls, mask, box in zip(scores, classes, masks, boxes):
label = labels[cls]
if class_filter and cls not in class_filter:
continue
box = box.astype(np.int32)
mask = cv2.cvtColor(mask*255, cv2.COLOR_GRAY2BGR).astype(np.uint8)
maskCanvas = cv2.addWeighted(maskCanvas, 1.0, mask, 1.0, 0)
maskCanvas = cv2.rectangle(maskCanvas, (box[0], box[1]), (box[2], box[3]), color=(255, 0, 0), thickness=3) # Red color for bounding box
maskCanvas = cv2.putText(maskCanvas, f"{label} : {score:.2f}", (box[0], box[1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, color=(255, 0, 0), thickness=2)
canvas = cv2.addWeighted(_image, 1.0, maskCanvas.astype(np.uint8), 1.0, 0)
return canvas, maskCanvas
def RescaleTheMask(orijinal_image, masks):
_masks = []
for contour in masks:
b_mask = np.zeros(orijinal_image.shape[:2], np.uint8)
contour = contour.astype(np.int32)
# contour = contour.reshape(-1, 1, 2)
w = orijinal_image.shape[0]
h = orijinal_image.shape[1]
mask = cv2.drawContours(b_mask, [contour], -1, (1, 1, 1), cv2.FILLED)
_masks += [mask]
return _masks
image = cv2.imread(test_image)
with torch.no_grad():
results = model(
image,
save=False,
show_boxes=False,
project="./inference/",
conf=0.5,
iou=0.7,
retina_masks=False
)
batches = ParseResults(results, threshold=0.5, scale_masks=True)
scores, classes, masks, boxes = batches[0]
canvas, mask = DrawResults(image, scores, classes, masks, boxes, class_filter=[3])
# ALL Segmentation
# canvas = torch.any(result.masks.data, dim=0).int() * 255
# Instance Segmentation
# objIdx = torch.where(result.boxes.cls.data == 3)
# objMasks = result.masks.data[objIdx]
# obj_mask = torch.any(objMasks, dim=0).int() * 255
#! Plot
fig, axs = plt.subplots(2, 2, figsize=(27, 15))
axs[0][0].imshow(image)
axs[0][0].set_title("Orijinal Görüntü")
axs[0][1].imshow(mask)
axs[0][1].set_title("Segmentasyon Maskesi")
# axs[1][0].imshow(obj_mask.cpu().numpy())
# axs[1][0].set_title("Seçilen")
axs[1][1].imshow(canvas)
axs[1][1].set_title("Sonuç")
# mask = np.array(obj_mask.cpu().numpy())*255
# cv2.imwrite("cable_mask.png", mask)
plt.tight_layout()
plt.show()
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