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initial commit

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  1. .env +1 -0
  2. .gitignore +2 -0
  3. app.py +15 -0
  4. data/frame_422.jpg +0 -0
  5. requirements.txt +0 -0
  6. runs/segment/train/BoxF1_curve.png +0 -0
  7. runs/segment/train/BoxPR_curve.png +0 -0
  8. runs/segment/train/BoxP_curve.png +0 -0
  9. runs/segment/train/BoxR_curve.png +0 -0
  10. runs/segment/train/MaskF1_curve.png +0 -0
  11. runs/segment/train/MaskPR_curve.png +0 -0
  12. runs/segment/train/MaskP_curve.png +0 -0
  13. runs/segment/train/MaskR_curve.png +0 -0
  14. runs/segment/train/args.yaml +106 -0
  15. runs/segment/train/confusion_matrix.png +0 -0
  16. runs/segment/train/confusion_matrix_normalized.png +0 -0
  17. runs/segment/train/events.out.tfevents.1717071496.d5f1cd638bb0.15173.0 +3 -0
  18. runs/segment/train/labels.jpg +0 -0
  19. runs/segment/train/labels_correlogram.jpg +0 -0
  20. runs/segment/train/results.csv +101 -0
  21. runs/segment/train/results.png +0 -0
  22. runs/segment/train/train_batch0.jpg +0 -0
  23. runs/segment/train/train_batch1.jpg +0 -0
  24. runs/segment/train/train_batch2.jpg +0 -0
  25. runs/segment/train/train_batch90.jpg +0 -0
  26. runs/segment/train/train_batch91.jpg +0 -0
  27. runs/segment/train/train_batch92.jpg +0 -0
  28. runs/segment/train/val_batch0_labels.jpg +0 -0
  29. runs/segment/train/val_batch0_pred.jpg +0 -0
  30. runs/segment/train/weights/best.pt +3 -0
  31. runs/segment/train/weights/best.zip +3 -0
  32. runs/segment/train/weights/last.pt +3 -0
  33. runs/segment/val/BoxF1_curve.png +0 -0
  34. runs/segment/val/BoxPR_curve.png +0 -0
  35. runs/segment/val/BoxP_curve.png +0 -0
  36. runs/segment/val/BoxR_curve.png +0 -0
  37. runs/segment/val/MaskF1_curve.png +0 -0
  38. runs/segment/val/MaskPR_curve.png +0 -0
  39. runs/segment/val/MaskP_curve.png +0 -0
  40. runs/segment/val/MaskR_curve.png +0 -0
  41. runs/segment/val/confusion_matrix.png +0 -0
  42. runs/segment/val/confusion_matrix_normalized.png +0 -0
  43. runs/segment/val/val_batch0_labels.jpg +0 -0
  44. runs/segment/val/val_batch0_pred.jpg +0 -0
  45. src/controllers/__pycache__/sack_detector.cpython-310.pyc +0 -0
  46. src/controllers/sack_detector.py +27 -0
  47. src/handlers/__pycache__/image_processing.cpython-310.pyc +0 -0
  48. src/handlers/image_processing.py +48 -0
.env ADDED
@@ -0,0 +1 @@
 
 
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+ YOLO_MODEL_PATH = "runs/segment/train/weights/last.pt"
.gitignore ADDED
@@ -0,0 +1,2 @@
 
 
 
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+ .conda/*
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+ .conda
app.py ADDED
@@ -0,0 +1,15 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ import cv2
2
+ import numpy as np
3
+ import streamlit as st
4
+
5
+ from src.controllers.sack_detector import SackDetector
6
+
7
+ uploaded_file = st.file_uploader("Choose a image file", type="jpg")
8
+
9
+ if uploaded_file is not None:
10
+ # Convert the file to an opencv image.
11
+ file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
12
+ opencv_image = cv2.imdecode(file_bytes, 1)
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+ detected_image, result = SackDetector().detect_objects(image_path=opencv_image)
14
+ # Now do something with the image! For example, let's display it:
15
+ st.image(detected_image, channels="BGR")
data/frame_422.jpg ADDED
requirements.txt ADDED
Binary file (2.42 kB). View file
 
runs/segment/train/BoxF1_curve.png ADDED
runs/segment/train/BoxPR_curve.png ADDED
runs/segment/train/BoxP_curve.png ADDED
runs/segment/train/BoxR_curve.png ADDED
runs/segment/train/MaskF1_curve.png ADDED
runs/segment/train/MaskPR_curve.png ADDED
runs/segment/train/MaskP_curve.png ADDED
runs/segment/train/MaskR_curve.png ADDED
runs/segment/train/args.yaml ADDED
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+ task: segment
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+ mode: train
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+ model: yolov8n-seg.yaml
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+ data: /content/Surf_bag_segmentation-1/data.yaml
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+ epochs: 100
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+ time: null
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+ patience: 100
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+ batch: 16
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+ imgsz: 640
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+ save: true
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+ save_period: -1
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+ cache: false
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+ device: null
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+ workers: 8
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+ project: null
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+ name: train
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+ exist_ok: false
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+ pretrained: yolov8n-seg.pt
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+ optimizer: auto
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+ verbose: true
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+ seed: 0
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+ deterministic: true
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+ single_cls: false
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+ rect: false
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+ cos_lr: false
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+ close_mosaic: 10
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+ resume: false
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+ amp: true
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+ fraction: 1.0
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+ profile: false
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+ freeze: null
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+ multi_scale: false
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+ overlap_mask: true
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+ mask_ratio: 4
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+ dropout: 0.0
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+ val: true
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+ split: val
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+ save_json: false
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+ save_hybrid: false
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+ conf: null
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+ iou: 0.7
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+ max_det: 300
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+ half: false
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+ dnn: false
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+ plots: true
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+ source: null
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+ vid_stride: 1
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+ stream_buffer: false
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+ visualize: false
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+ augment: false
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+ agnostic_nms: false
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+ classes: null
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+ retina_masks: false
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+ embed: null
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+ show: false
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+ save_frames: false
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+ save_txt: false
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+ save_conf: false
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+ save_crop: false
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+ show_labels: true
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+ show_conf: true
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+ show_boxes: true
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+ line_width: null
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+ format: torchscript
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+ keras: false
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+ optimize: false
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+ int8: false
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+ dynamic: false
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+ simplify: false
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+ opset: null
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+ workspace: 4
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+ nms: false
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+ lr0: 0.01
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+ lrf: 0.01
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+ momentum: 0.937
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+ weight_decay: 0.0005
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+ warmup_epochs: 3.0
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+ warmup_momentum: 0.8
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+ warmup_bias_lr: 0.1
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+ box: 7.5
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+ cls: 0.5
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+ dfl: 1.5
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+ pose: 12.0
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+ kobj: 1.0
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+ label_smoothing: 0.0
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+ nbs: 64
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+ hsv_h: 0.015
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+ hsv_s: 0.7
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+ hsv_v: 0.4
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+ degrees: 0.0
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+ translate: 0.1
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+ scale: 0.5
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+ shear: 0.0
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+ perspective: 0.0
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+ flipud: 0.0
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+ fliplr: 0.5
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+ bgr: 0.0
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+ mosaic: 1.0
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+ mixup: 0.0
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+ copy_paste: 0.0
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+ auto_augment: randaugment
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+ erasing: 0.4
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+ crop_fraction: 1.0
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+ cfg: null
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+ tracker: botsort.yaml
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+ save_dir: runs/segment/train
runs/segment/train/confusion_matrix.png ADDED
runs/segment/train/confusion_matrix_normalized.png ADDED
runs/segment/train/events.out.tfevents.1717071496.d5f1cd638bb0.15173.0 ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:5565a2348302f0a12a73fa61fb5d589995e0accc2071a04e5a33afc7fd4fd5a6
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+ size 332723
runs/segment/train/labels.jpg ADDED
runs/segment/train/labels_correlogram.jpg ADDED
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+ epoch, train/box_loss, train/seg_loss, train/cls_loss, train/dfl_loss, metrics/precision(B), metrics/recall(B), metrics/mAP50(B), metrics/mAP50-95(B), metrics/precision(M), metrics/recall(M), metrics/mAP50(M), metrics/mAP50-95(M), val/box_loss, val/seg_loss, val/cls_loss, val/dfl_loss, lr/pg0, lr/pg1, lr/pg2
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runs/segment/val/MaskR_curve.png ADDED
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src/controllers/__pycache__/sack_detector.cpython-310.pyc ADDED
Binary file (1.3 kB). View file
 
src/controllers/sack_detector.py ADDED
@@ -0,0 +1,27 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import cv2
2
+ from ultralytics import YOLO
3
+
4
+ from src.handlers.image_processing import draw_box, resize_image
5
+
6
+ import os
7
+ from dotenv import load_dotenv
8
+ load_dotenv()
9
+
10
+ class SackDetector:
11
+ def __init__(self):
12
+ self.model_path = os.getenv("YOLO_MODEL_PATH", None) # Default path
13
+ if self.model_path is None:
14
+ raise ValueError("YOLO_MODEL_PATH environment variable is not set.")
15
+ self.model = YOLO(self.model_path)
16
+ self.class_list = self.model.model.names
17
+
18
+ def detect_objects(self, image_path):
19
+ results = self.model(image_path)
20
+ result = results[0]
21
+ labeled_img = draw_box(result.orig_img, result, self.class_list)
22
+ # display_img = resize_image(labeled_img, 100)
23
+ return labeled_img, result
24
+
25
+ def display_image(self, image):
26
+ cv2.imshow('outputimage', image)
27
+ cv2.waitKey(0) # Wait for user input to close the window
src/handlers/__pycache__/image_processing.cpython-310.pyc ADDED
Binary file (1.58 kB). View file
 
src/handlers/image_processing.py ADDED
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1
+ import cv2
2
+
3
+ def resize_image(img, scale_percent) :
4
+ # Calculate new size
5
+ width = int(img.shape[1] * scale_percent / 100)
6
+ height = int(img.shape[0] * scale_percent / 100)
7
+ dim = (width, height)
8
+ # Resize image
9
+ resized = cv2.resize(img, dim, interpolation = cv2.INTER_AREA)
10
+ return resized
11
+
12
+ def draw_box(img, result, class_list) :
13
+ # Get information from result
14
+ xyxy= result.boxes.xyxy.numpy()
15
+ confidence= result.boxes.conf.numpy()
16
+ class_id= result.boxes.cls.numpy().astype(int)
17
+ # Get Class name
18
+ class_name = [class_list[x] for x in class_id]
19
+ # Pack together for easy use
20
+ sum_output = list(zip(class_name, confidence,xyxy))
21
+ # Copy image, in case that we need original image for something
22
+ out_image = img.copy()
23
+ for run_output in sum_output :
24
+ # Unpack
25
+ label, con, box = run_output
26
+ # Choose color
27
+ box_color = (0, 0, 255)
28
+ text_color = (255,255,255)
29
+ # Draw object box
30
+ first_half_box = (int(box[0]),int(box[1]))
31
+ second_half_box = (int(box[2]),int(box[3]))
32
+ cv2.rectangle(out_image, first_half_box, second_half_box, box_color, 2)
33
+ # Create text
34
+ text_print = '{label} {con:.2f}'.format(label = label, con = con)
35
+ # Locate text position
36
+ text_location = (int(box[0]), int(box[1] - 10 ))
37
+ # Get size and baseline
38
+ labelSize, baseLine = cv2.getTextSize(text_print, cv2.FONT_HERSHEY_SIMPLEX, 1, 2)
39
+ # Draw text's background
40
+ cv2.rectangle(out_image
41
+ , (int(box[0]), int(box[1] - labelSize[1] - 10 ))
42
+ , (int(box[0])+labelSize[0], int(box[1] + baseLine-10))
43
+ , box_color , cv2.FILLED)
44
+ # Put text
45
+ cv2.putText(out_image, text_print ,text_location
46
+ , cv2.FONT_HERSHEY_SIMPLEX , 1
47
+ , text_color, 2 ,cv2.LINE_AA)
48
+ return out_image