docs: Update README and compilation guides for clarity and consistency, including path corrections and improved formatting. Add copyright notices to source files and adjust file permissions for several scripts and directories.
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dian.yuan
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136 changed files with 14413 additions and 9399 deletions
266
examples/blazepose_detect/py/balzepose_detect.py
Executable file
266
examples/blazepose_detect/py/balzepose_detect.py
Executable file
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#
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# Copyright (C) 2026 Amlogic, Inc. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import numpy as np
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import os
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import glob
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import argparse
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import cv2
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from pathlib import Path
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from amlnnlite.api import AMLNNLite
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def letterbox(img, new_shape=(224, 224), color=(0, 0, 0)):
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shape = img.shape[:2] # [height, width]
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scale = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
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new_unpad = (int(round(shape[1] * scale)), int(round(shape[0] * scale)))
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pad_w = (new_shape[1] - new_unpad[0]) / 2
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pad_h = (new_shape[0] - new_unpad[1]) / 2
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if shape[::-1] != new_unpad:
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img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
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top, bottom = int(round(pad_h - 0.1)), int(round(pad_h + 0.1))
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left, right = int(round(pad_w - 0.1)), int(round(pad_w + 0.1))
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img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)
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scale = 1. / scale
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ori_left = left * scale
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ori_top = top * scale
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return img, scale, (ori_left, ori_top)
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def preprocess(img_path, new_shape=(224, 224), data_format='NCHW', s=0.003921568859368563, zp=-128):
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original_img = cv2.imread(str(img_path))
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if original_img is None:
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raise ValueError(f"can't read image: {img_path}")
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processed_img, scale, pad = letterbox(original_img, new_shape)
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rgb_img = cv2.cvtColor(processed_img, cv2.COLOR_BGR2RGB)
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normalized_img = rgb_img.astype(np.float32) / 127.5 - 1.
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if data_format == 'NCHW':
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# HWC -> CHW -> BCHW (ONNX default format)
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input_tensor = np.transpose(normalized_img, (2, 0, 1))
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input_tensor = np.expand_dims(input_tensor, axis=0)
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elif data_format == 'NHWC':
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# HWC -> BHWC (TFLITE default format)
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input_tensor = np.expand_dims(normalized_img, axis=0)
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else:
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raise ValueError(f"Unsupported data format: {data_format}. Only 'NCHW' and 'NHWC' are supported.")
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# Quantize to int8
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input_tensor = np.round(input_tensor / s + zp).astype(np.int8)
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return input_tensor, original_img, scale, pad
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def postprocess(outputs, scale, pad, data_format='NCHW', anchor_path='anchors.npy', score_threshold=0.5, nms_threshold=0.3):
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all_boxes = []
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all_scores = []
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raw_box = outputs[0] # (1, 2254, 12)
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raw_score = outputs[1] # (1, 2254, 1)
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anchors = np.load(anchor_path).astype("float32")
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# all_boxes = decode_boxes(raw_box, anchors)
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# anchors: [N, 4] -> x, y, w, h
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anc_x, anc_y, anc_w, anc_h = anchors.T
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# raw_box shape: [..., K]
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all_boxes = np.zeros_like(raw_box)
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# box center & size
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x_center = raw_box[..., 0] / 224.0 * anc_w + anc_x
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y_center = raw_box[..., 1] / 224.0 * anc_h + anc_y
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w = raw_box[..., 2] / 224.0 * anc_w
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h = raw_box[..., 3] / 224.0 * anc_h
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# bbox: ymin, xmin, ymax, xmax
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all_boxes[..., 0] = y_center - 0.5 * h
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all_boxes[..., 1] = x_center - 0.5 * w
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all_boxes[..., 2] = y_center + 0.5 * h
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all_boxes[..., 3] = x_center + 0.5 * w
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# keypoints (4 points, each has x/y)
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for k in range(4):
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idx = 4 + k * 2
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all_boxes[..., idx] = raw_box[..., idx] / 224.0 * anc_w + anc_x
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all_boxes[..., idx + 1] = raw_box[..., idx + 1] / 224.0 * anc_h + anc_y
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thresh = 100.0
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raw_score = raw_score.clip(-thresh, thresh)
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# Apply sigmoid activation to class scores
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all_scores = 1.0 / (1.0 + np.exp(-raw_score)).squeeze(axis=-1)
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print(f"all_scores {all_scores}")
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print(f"max(all_scores) {max(all_scores[0])}")
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mask = all_scores >= score_threshold
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# Merge all scales
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final_boxes = np.concatenate(all_boxes, axis=0)
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final_scores = np.concatenate(all_scores, axis=0)
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# Filter by confidence threshold
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valid_mask = final_scores > score_threshold
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if not np.any(valid_mask):
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return []
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valid_boxes = final_boxes[valid_mask]
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valid_scores = final_scores[valid_mask]
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# Map coordinates back to original image
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pad_x, pad_y = pad
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s = scale * 224
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valid_boxes[:, [0, 2]] = valid_boxes[:, [0, 2]] * s - pad_x
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valid_boxes[:, [1, 3]] = valid_boxes[:, [1, 3]] * s - pad_y
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valid_boxes[:, 4::2] = valid_boxes[:, 4::2] * s - pad_y
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valid_boxes[:, 5::2] = valid_boxes[:, 5::2] * s - pad_x
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valid_boxes = np.maximum(valid_boxes, 0)
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# NMS
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if len(valid_boxes) > 0:
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nms_indices = cv2.dnn.NMSBoxes(
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valid_boxes.tolist(), valid_scores.tolist(), score_threshold, nms_threshold
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)
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if len(nms_indices) > 0:
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nms_indices = nms_indices.flatten()
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detections = []
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for idx in nms_indices:
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x1, y1, x2, y2 = valid_boxes[idx, :4]
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confidence = valid_scores[idx]
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# x_center = (valid_boxes[:,1] + valid_boxes[:,3]) / 2
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# y_center = (valid_boxes[:,0] + valid_boxes[:,2]) / 2
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# scale = (valid_boxes[:,3] - valid_boxes[:,1]) # assumes square boxes
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detections.append({
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'bbox': [float(x1), float(y1), float(x2), float(y2)],
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'confidence': float(confidence)
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})
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return detections
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return []
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def get_class_color(class_id):
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import colorsys
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hue = (class_id * 137.508) % 360
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rgb = colorsys.hsv_to_rgb(hue/360.0, 0.8, 0.9)
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bgr = (int(rgb[2]*255), int(rgb[1]*255), int(rgb[0]*255))
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return bgr
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def draw_detections(img, detections, save_path):
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result_img = img.copy()
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for det in detections:
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x1, y1, x2, y2 = [int(coord) for coord in det['bbox']]
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confidence = det['confidence']
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class_name = det['class_name']
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class_id = det['class_id']
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color = get_class_color(class_id)
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cv2.rectangle(result_img, (x1, y1), (x2, y2), color, 2)
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label = f"{class_name}: {confidence:.2f}"
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(label_w, label_h), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.6, 1)
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cv2.rectangle(result_img, (x1, y1 - label_h - 10), (x1 + label_w, y1), color, -1)
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text_color = (255, 255, 255) if sum(color) < 400 else (0, 0, 0)
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cv2.putText(result_img, label, (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.6, text_color, 1)
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cv2.imwrite(save_path, result_img)
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return result_img
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument('--model-path', default='./blazepose_detect_int8_A311D2.adla')
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parser.add_argument('--run-cycles', default= 1, type=int)
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args = parser.parse_args()
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# Initialize AMLNNLite
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amlnn = AMLNNLite()
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amlnn.config(
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model_path=args.model_path, # Model file path, Support ADLA and quantized TFlite models
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run_cycles=args.run_cycles
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)
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amlnn.init()
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# Find all image files in the 01_export_model directory
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image_dir = "./"
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image_extensions = ["*.jpg", "*.jpeg", "*.png", "*.bmp"]
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image_files = []
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for ext in image_extensions:
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image_files.extend(glob.glob(os.path.join(image_dir, ext)))
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image_files.extend(glob.glob(os.path.join(image_dir, ext.upper())))
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if not image_files:
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print("No image files found in", image_dir)
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amlnn.uninit()
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return
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print(f"Found {len(image_files)} image files to process:")
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for img_file in image_files:
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print(f" - {os.path.basename(img_file)}")
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print()
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# Process each image
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for i, image_path in enumerate(image_files, 1):
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print(f"=" * 60)
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print(f"Processing image {i}/{len(image_files)}: {os.path.basename(image_path)}")
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print(f"=" * 60)
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try:
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# Preprocess input
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input_tensor, original_img, scale, pad = preprocess(image_path, new_shape=(224, 224), data_format='NHWC', s=0.007843137718737125, zp=-1)
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# Run inference
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outputs = amlnn.inference(inputs=[input_tensor])
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# Postprocess results
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detections = postprocess(outputs, scale, pad, data_format='NHWC', score_threshold=0.5, nms_threshold=0.3)
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# Print detection results
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if detections:
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print(f" Detected {len(detections)} objects:")
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for i, det in enumerate(detections, 1):
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print(f" {i}. {det['class_name']} ({det['confidence']:.2f})")
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else:
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print(" No objects detected")
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# Save result image
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model_name = Path(args.model_path).stem
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result_dir = f"{model_name}_result"
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os.makedirs(result_dir, exist_ok=True)
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img_name = Path(image_path).stem
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save_path = os.path.join(result_dir, f"{img_name}_result.jpg")
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draw_detections(original_img, detections, str(save_path))
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print(f" Result saved to: {save_path}")
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except Exception as e:
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print(f"Error processing {os.path.basename(image_path)}: {e}")
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print()
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# Optional visualization
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amlnn.visualize()
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# Release resources
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amlnn.uninit()
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if __name__ == "__main__":
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main()
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