[Example] YOLOv8-OpenCV-ONNX-Python (#1007)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: ayush chaurasia <ayush.chaurarsia@gmail.com>

examples/README.md

@@ -3,8 +3,9 @@ This is a list of real-world applications and walkthroughs. These can be folders
 ## Ultralytics YOLO example applications
 
 | Title                                                           | Format             | Contributor                                         |
-| --------------------------------------------------------------- | -------- | ------------------------------------------------- |
+| --------------------------------------------------------------- | ------------------ | --------------------------------------------------- |
 | [Yolov8/yolov5 ONNX Inference with C++](./Yolov8_CPP_Inference) | C++/ONNX           | [Justas Bartnykas](https://github.com/JustasBart)   |
+| [YOLOv8-OpenCV-ONNX-Python](./YOLOv8-OpenCV-ONNX-Python)        | OpenCV/Python/ONNX | [Farid Inawan](https://github.com/frdteknikelektro) |
 
 ## How can you contribute ?
 

examples/YOLOv8-OpenCV-ONNX-Python/README.md

@@ -0,0 +1,19 @@
+# YOLOv8 - OpenCV
+
+Implementation YOLOv8 on OpenCV using ONNX Format.
+
+Just simply clone and run
+
+```bash
+pip install -r requirements.txt
+python main.py
+```
+
+If you start from scratch:
+
+```bash
+pip install ultralytics
+yolo export model=yolov8n.pt imgsz=640 format=onnx opset=12
+```
+
+_\*Make sure to include "opset=12"_

examples/YOLOv8-OpenCV-ONNX-Python/main.py

@@ -0,0 +1,74 @@
+import cv2.dnn
+import numpy as np
+
+from ultralytics.yolo.utils import ROOT, yaml_load
+from ultralytics.yolo.utils.checks import check_yaml
+
+CLASSES = yaml_load(check_yaml('coco128.yaml'))['names']
+
+colors = np.random.uniform(0, 255, size=(len(CLASSES), 3))
+
+
+def draw_bounding_box(img, class_id, confidence, x, y, x_plus_w, y_plus_h):
+    label = f'{CLASSES[class_id]} ({confidence:.2f})'
+    color = colors[class_id]
+    cv2.rectangle(img, (x, y), (x_plus_w, y_plus_h), color, 2)
+    cv2.putText(img, label, (x - 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
+
+
+def main():
+    model: cv2.dnn.Net = cv2.dnn.readNetFromONNX('yolov8n.onnx')
+    original_image: np.ndarray = cv2.imread(str(ROOT / 'assets/bus.jpg'))
+    [height, width, _] = original_image.shape
+    length = max((height, width))
+    image = np.zeros((length, length, 3), np.uint8)
+    image[0:height, 0:width] = original_image
+    scale = length / 640
+
+    blob = cv2.dnn.blobFromImage(image, scalefactor=1 / 255, size=(640, 640))
+    model.setInput(blob)
+    outputs = model.forward()
+
+    outputs = np.array([cv2.transpose(outputs[0])])
+    rows = outputs.shape[1]
+
+    boxes = []
+    scores = []
+    class_ids = []
+
+    for i in range(rows):
+        classes_scores = outputs[0][i][4:]
+        (minScore, maxScore, minClassLoc, (x, maxClassIndex)) = cv2.minMaxLoc(classes_scores)
+        if maxScore >= 0.25:
+            box = [
+                outputs[0][i][0] - (0.5 * outputs[0][i][2]), outputs[0][i][1] - (0.5 * outputs[0][i][3]),
+                outputs[0][i][2], outputs[0][i][3]]
+            boxes.append(box)
+            scores.append(maxScore)
+            class_ids.append(maxClassIndex)
+
+    result_boxes = cv2.dnn.NMSBoxes(boxes, scores, 0.25, 0.45, 0.5)
+
+    detections = []
+    for i in range(len(result_boxes)):
+        index = result_boxes[i]
+        box = boxes[index]
+        detection = {
+            'class_id': class_ids[index],
+            'class_name': CLASSES[class_ids[index]],
+            'confidence': scores[index],
+            'box': box,
+            'scale': scale}
+        detections.append(detection)
+        draw_bounding_box(original_image, class_ids[index], scores[index], round(box[0] * scale), round(box[1] * scale),
+                          round((box[0] + box[2]) * scale), round((box[1] + box[3]) * scale))
+
+    cv2.imshow('image', original_image)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+
+    return detections
+
+
+if __name__ == '__main__':
+    main()