add python demos

examples/yolov11/cpp/build-linux.sh

@@ -1,65 +1,19 @@
-#TODO
 #!/bin/bash
 set -e
 
-usage() {
-    echo "Usage: $0 [-a <target_arch>]"
-    echo "  -a <target_arch> : Target architecture (default: aarch64)"
-    echo "  -h               : Show this help message"
-    exit 1
-}
-
-# Default values
-TARGET_ARCH=aarch64
-
-# Parse arguments
-while getopts 'a:h' opt; do
-  case "$opt" in
-    a)
-      TARGET_ARCH=$OPTARG
-      ;;
-    h)
-      usage
-      ;;
-    *)
-      usage
-      ;;
-  esac
-done
-
-# Default to aarch64-linux-gnu if GCC_COMPILER is not set
-GCC_COMPILER=${GCC_COMPILER:-aarch64-linux-gnu}
-
-# Set compilers
-export CC=${GCC_COMPILER}-gcc
-export CXX=${GCC_COMPILER}-g++
-
-# Validate compiler
-if ! command -v ${CC} &> /dev/null; then
-    echo "Error: Compiler ${CC} not found."
-    echo "Please set GCC_COMPILER environment variable to your cross-compiler path prefix."
-    echo "Example: export GCC_COMPILER=/path/to/toolchain/bin/aarch64-linux-gnu"
-    # Proceeding anyway as user might have custom env setup
-else
-    echo "Using compiler: ${CC}"
-fi
-
-ROOT_PWD=$(cd "$(dirname $0)" && pwd)
-BUILD_DIR=${ROOT_PWD}/build/linux
-
-echo "Building for Linux..."
-echo "COMPILER: ${CC}"
-echo "TARGET_ARCH: ${TARGET_ARCH}"
-echo "BUILD_DIR: ${BUILD_DIR}"
-
-mkdir -p ${BUILD_DIR}
-cd ${BUILD_DIR}
-
-cmake ../../src \
-    -DCMAKE_SYSTEM_NAME=Linux \
-    -DCMAKE_SYSTEM_PROCESSOR=${TARGET_ARCH} \
-    -DCMAKE_BUILD_TYPE=Release \
-    -DOpenCV_DIR=${ROOT_PWD}/../../../dependency/opencv/opencv-linux-aarch64/share/OpenCV
-make -j4
-
-echo "Build complete. Executable in ${BUILD_DIR}/yolo11_demo"
+#
+# Copyright (C) 2024–2025 Amlogic, Inc. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+### TO DO

examples/yolov11/cpp/src/main.cpp

@@ -1,3 +1,19 @@
+/*
+ * Copyright (C) 2024–2025 Amlogic, Inc. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
 #include <iostream>
 #include <vector>
 #include <filesystem>

examples/yolov11/cpp/src/postprocess.cpp

@@ -1,3 +1,19 @@
+/*
+ * Copyright (C) 2024–2025 Amlogic, Inc. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
 #include "postprocess.h"
 #include <cmath>
 #include <numeric>

examples/yolov11/cpp/src/postprocess.h

@@ -1,3 +1,19 @@
+/*
+ * Copyright (C) 2024–2025 Amlogic, Inc. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
 #ifndef YOLO11_POSTPROCESS_H
 #define YOLO11_POSTPROCESS_H
 

examples/yolov11/py/yolov11.py

@@ -0,0 +1,157 @@
+# -*- coding: utf-8 -*-
+"""
+Copyright (C) 2024–2025 Amlogic, Inc. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+"""
+
+import numpy as np
+import os
+import glob
+import argparse
+import cv2
+from pathlib import Path
+from amlnnlite.api import AMLNNLite
+
+class_names = {0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus', 6: 'train', 7: 'truck', 8: 'boat', 9: 'traffic light', 10: 'fire hydrant', 11: 'stop sign', 12: 'parking meter', 13: 'bench', 14: 'bird', 15: 'cat', 16: 'dog', 17: 'horse', 18: 'sheep', 19: 'cow', 20: 'elephant', 21: 'bear', 22: 'zebra', 23: 'giraffe', 24: 'backpack', 25: 'umbrella', 26: 'handbag', 27: 'tie', 28: 'suitcase', 29: 'frisbee', 30: 'skis', 31: 'snowboard', 32: 'sports ball', 33: 'kite', 34: 'baseball bat', 35: 'baseball glove', 36: 'skateboard', 37: 'surfboard', 38: 'tennis racket', 39: 'bottle', 40: 'wine glass', 41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon', 45: 'bowl', 46: 'banana', 47: 'apple', 48: 'sandwich', 49: 'orange', 50: 'broccoli', 51: 'carrot', 52: 'hot dog', 53: 'pizza', 54: 'donut', 55: 'cake', 56: 'chair', 57: 'couch', 58: 'potted plant', 59: 'bed', 60: 'dining table', 61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse', 65: 'remote', 66: 'keyboard', 67: 'cell phone', 68: 'microwave', 69: 'oven', 70: 'toaster', 71: 'sink', 72: 'refrigerator', 73: 'book', 74: 'clock', 75: 'vase', 76: 'scissors', 77: 'teddy bear', 78: 'hair drier', 79: 'toothbrush'}
+
+def letterbox(img, new_shape=(640, 640), color=(114, 114, 114)):
+    shape = img.shape[:2]
+    scale = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
+    new_unpad = (int(round(shape[1] * scale)), int(round(shape[0] * scale)))
+    dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]
+    dw /= 2; dh /= 2
+    img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
+    top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
+    left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
+    img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)
+    return img, scale, (left, top)
+
+def preprocess(img_path, new_shape=(640,640)):
+    original_img = cv2.imread(str(img_path))
+    if original_img is None: return None, None, None, None
+    processed_img, scale, pad = letterbox(original_img, new_shape)
+    rgb_img = cv2.cvtColor(processed_img, cv2.COLOR_BGR2RGB)
+    normalized_img = rgb_img.astype(np.float32) / 255.0
+    input_tensor = np.expand_dims(np.transpose(normalized_img, (2,0,1)), 0) # NCHW
+    return input_tensor, original_img, scale, pad
+
+def postprocess(outputs, scale, pad, strides=[32,16,8], conf_threshold=0.25, iou_threshold=0.45):
+    all_boxes, all_scores, all_class_ids = [], [], []
+    for scale_idx, output in enumerate(outputs):
+        stride = strides[scale_idx]
+        feat = output[0].transpose(1, 2, 0) # H, W, C
+        h, w, c = feat.shape
+        dfl = feat[:, :, :64].reshape(h, w, 4, 16)
+        cls_logits = feat[:, :, 64:]
+        cls_scores = 1.0 / (1.0 + np.exp(-cls_logits)) # sigmoid
+        
+        exp_x = np.exp(dfl - np.max(dfl, axis=-1, keepdims=True))
+        p = exp_x / np.sum(exp_x, axis=-1, keepdims=True)
+        bbox_deltas = np.sum(p * np.arange(16, dtype=np.float32), axis=-1)
+        
+        grid_y, grid_x = np.meshgrid(np.arange(h), np.arange(w), indexing='ij')
+        l, t, r, b = np.split(bbox_deltas, 4, axis=-1)
+        x1, y1 = (grid_x + 0.5 - l[..., 0]) * stride, (grid_y + 0.5 - t[..., 0]) * stride
+        x2, y2 = (grid_x + 0.5 + r[..., 0]) * stride, (grid_y + 0.5 + b[..., 0]) * stride
+        
+        all_boxes.append(np.stack([x1, y1, x2, y2], axis=-1).reshape(-1, 4))
+        all_scores.append(cls_scores.reshape(-1, cls_scores.shape[-1]))
+    
+    final_boxes = np.concatenate(all_boxes, axis=0)
+    final_scores_all = np.concatenate(all_scores, axis=0)
+    final_class_ids = np.argmax(final_scores_all, axis=1)
+    final_scores = np.max(final_scores_all, axis=1)
+
+    mask = final_scores > conf_threshold
+    if not np.any(mask): return []
+    
+    valid_boxes = final_boxes[mask]
+    valid_boxes[:, [0, 2]] = (valid_boxes[:, [0, 2]] - pad[0]) / scale
+    valid_boxes[:, [1, 3]] = (valid_boxes[:, [1, 3]] - pad[1]) / scale
+    
+    indices = cv2.dnn.NMSBoxes(valid_boxes.tolist(), final_scores[mask].tolist(), conf_threshold, iou_threshold)
+    detections = []
+    if len(indices) > 0:
+        for idx in indices.flatten():
+            detections.append({
+                'bbox': valid_boxes[idx].tolist(),
+                'confidence': float(final_scores[mask][idx]),
+                'class_name': class_names.get(int(final_class_ids[mask][idx]), 'unknown')
+            })
+    return detections
+
+def main():
+    parser = argparse.ArgumentParser(description="YOLOV11 AMLNNLite Demo")
+    parser.add_argument('--board-work-path', default='/data/nn', help='Work path on board')
+    parser.add_argument('-m', '--model-path', required=True, help='Path to .adla or .tflite model')
+    parser.add_argument('--image-dir', required=True, help='Directory containing test images')
+    parser.add_argument('--run-cycles', type=int, default=1, help='Inference cycles for profiling')
+    parser.add_argument('--loglevel', default='WARNING', choices=['DEBUG', 'INFO', 'WARNING', 'ERROR'], help='Log level')
+    args = parser.parse_args()
+
+    amlnn = AMLNNLite()
+    amlnn.config(
+        board_work_path=args.board_work_path,
+        model_path=args.model_path,
+        run_cycles=args.run_cycles,
+        loglevel=args.loglevel
+    )
+    amlnn.init()
+
+    image_files = []
+    for ext in ["*.jpg", "*.jpeg", "*.png"]:
+        image_files.extend(glob.glob(os.path.join(args.image_dir, ext)))
+        image_files.extend(glob.glob(os.path.join(args.image_dir, ext.upper())))
+    image_files.sort()
+    
+    if not image_files:
+        print(f"No images found in {args.image_dir}")
+        amlnn.uninit(); return
+
+    model_stem = Path(args.model_path).stem
+    res_dir = f"{model_stem}_result"
+    os.makedirs(res_dir, exist_ok=True)
+
+    for i, img_path in enumerate(image_files, 1):
+        print("=" * 60)
+        print(f"Processing image {i}/{len(image_files)}: {os.path.basename(img_path)}")
+        print("=" * 60)
+        
+        input_tensor, ori_img, scale, pad = preprocess(img_path)
+        if input_tensor is None: continue
+        
+        for _ in range(args.run_cycles):
+            outputs = amlnn.inference(input_tensor, inputs_data_format='NCHW', outputs_data_format='NCHW')
+        
+        detections = postprocess(outputs, scale, pad)
+        
+        print(f"    Detected {len(detections)} objects:")
+        for idx, det in enumerate(detections, 1):
+            print(f"      {idx}. {det['class_name']} ({det['confidence']:.2f})")
+            
+        for det in detections:
+            x1, y1, x2, y2 = map(int, det['bbox'])
+            cv2.rectangle(ori_img, (x1, y1), (x2, y2), (0, 255, 0), 2)
+            cv2.putText(ori_img, f"{det['class_name']} {det['confidence']:.2f}", (x1, y1-10), 
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
+        
+        save_path = os.path.join(res_dir, f"{Path(img_path).stem}_result.jpg")
+        cv2.imwrite(save_path, ori_img)
+        print(f"    Result saved to: {save_path}")
+
+    amlnn.visualize()
+    amlnn.uninit()
+
+if __name__ == "__main__":
+    main()