add python demos
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dian.yuan
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97 changed files with 3250 additions and 290 deletions
281
examples/yolov8/py/yolov8.py
Executable file
281
examples/yolov8/py/yolov8.py
Executable file
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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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class_names = {
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0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane',
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5: 'bus', 6: 'train', 7: 'truck', 8: 'boat', 9: 'traffic light',
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10: 'fire hydrant', 11: 'stop sign', 12: 'parking meter', 13: 'bench', 14: 'bird',
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15: 'cat', 16: 'dog', 17: 'horse', 18: 'sheep', 19: 'cow',
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20: 'elephant', 21: 'bear', 22: 'zebra', 23: 'giraffe', 24: 'backpack',
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25: 'umbrella', 26: 'handbag', 27: 'tie', 28: 'suitcase', 29: 'frisbee',
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30: 'skis', 31: 'snowboard', 32: 'sports ball', 33: 'kite', 34: 'baseball bat',
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35: 'baseball glove', 36: 'skateboard', 37: 'surfboard', 38: 'tennis racket', 39: 'bottle',
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40: 'wine glass', 41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon',
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45: 'bowl', 46: 'banana', 47: 'apple', 48: 'sandwich', 49: 'orange',
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50: 'broccoli', 51: 'carrot', 52: 'hot dog', 53: 'pizza', 54: 'donut',
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55: 'cake', 56: 'chair', 57: 'couch', 58: 'potted plant', 59: 'bed',
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60: 'dining table', 61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse',
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65: 'remote', 66: 'keyboard', 67: 'cell phone', 68: 'microwave', 69: 'oven',
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70: 'toaster', 71: 'sink', 72: 'refrigerator', 73: 'book', 74: 'clock',
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75: 'vase', 76: 'scissors', 77: 'teddy bear', 78: 'hair drier', 79: 'toothbrush'
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}
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def letterbox(img, new_shape=(640, 640), color=(114, 114, 114)):
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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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return img, scale, (left, top)
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def preprocess(img_path, new_shape=(640, 640), 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) / 255.0
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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', strides=[8, 16, 32], conf_threshold=0.25, iou_threshold=0.45):
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all_boxes = []
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all_scores = []
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all_class_ids = []
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for scale_idx, output in enumerate(outputs):
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stride = strides[scale_idx]
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if data_format == 'NCHW':
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# (1, 144, H, W) → (H*W, 144)
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batch_size, channels, height, width = output.shape
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output_reshaped = output.transpose(0, 2, 3, 1).reshape(-1, channels)
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elif data_format == 'NHWC':
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# (1, H, W, 144) → (H*W, 144)
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batch_size, height, width, channels = output.shape
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output_reshaped = output.reshape(-1, channels)
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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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# Separate DFL and classification: 144 = 64(DFL) + 80(Classes)
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dfl_predictions = output_reshaped[:, :64]
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class_predictions = output_reshaped[:, 64:]
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# Apply sigmoid activation to class scores
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class_scores = 1.0 / (1.0 + np.exp(-class_predictions))
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max_class_scores = np.max(class_scores, axis=1)
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class_ids = np.argmax(class_scores, axis=1)
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# Generate grid coordinates
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grid_y, grid_x = np.meshgrid(np.arange(height), np.arange(width), indexing='ij')
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grid_x = grid_x.flatten().astype(np.float32)
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grid_y = grid_y.flatten().astype(np.float32)
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# DFL decoding
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dfl_reshaped = dfl_predictions.reshape(-1, 4, 16)
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dfl_softmax = np.exp(dfl_reshaped) / np.sum(np.exp(dfl_reshaped), axis=-1, keepdims=True)
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regression_range = np.arange(16, dtype=np.float32)
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bbox_deltas = np.sum(dfl_softmax * regression_range[None, None, :], axis=-1)
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# Convert to absolute coordinates
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anchor_x = (grid_x + 0.5) * stride
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anchor_y = (grid_y + 0.5) * stride
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left, top, right, bottom = bbox_deltas.T
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x1 = anchor_x - left * stride
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y1 = anchor_y - top * stride
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x2 = anchor_x + right * stride
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y2 = anchor_y + bottom * stride
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boxes = np.stack([x1, y1, x2, y2], axis=1)
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all_boxes.append(boxes)
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all_scores.append(max_class_scores)
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all_class_ids.append(class_ids)
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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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final_class_ids = np.concatenate(all_class_ids, axis=0)
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# Filter by confidence threshold
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valid_mask = final_scores > conf_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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valid_class_ids = final_class_ids[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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valid_boxes[:, [0, 2]] = (valid_boxes[:, [0, 2]] - pad_x) / scale
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valid_boxes[:, [1, 3]] = (valid_boxes[:, [1, 3]] - pad_y) / scale
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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(), conf_threshold, iou_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]
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confidence = valid_scores[idx]
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class_id = valid_class_ids[idx]
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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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'class_id': int(class_id),
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'class_name': class_names.get(int(class_id), f'class_{class_id}')
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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='./yolov8s_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 ADLD 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=(640, 640), data_format='NHWC', s=0.003921568859368563, zp=-128)
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# Run inference
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outputs = amlnn.inference(
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inputs=[input_tensor]
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)
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# Postprocess results
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detections = postprocess(outputs, scale, pad, data_format='NHWC', strides=[8, 16, 32], conf_threshold=0.25, iou_threshold=0.45)
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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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