203 lines
6.4 KiB
Python
203 lines
6.4 KiB
Python
import cv2
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import time
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import os
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import numpy as np
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from PIL import Image
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from skimage.metrics import structural_similarity as ssim
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import shutil
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from rknnlite.api import RKNNLite
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# ================== 配置参数 ==================
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RTSP_URL = "rtsp://admin:XJ123456@192.168.250.60:554/streaming/channels/101"
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SAVE_INTERVAL = 15
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SSIM_THRESHOLD = 0.9
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OUTPUT_DIR = "camera_test"
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RKNN_MODEL = "bag3588.rknn"
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SHOW_WINDOW = False
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# 灰度判断参数
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GRAY_LOWER = 70
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GRAY_UPPER = 230
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GRAY_RATIO_THRESHOLD = 0.7
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IMG_SIZE = (640, 640)
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OBJ_THRESH = 0.001
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NMS_THRESH = 0.45
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CLASS_NAME = ["bag"]
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os.makedirs(OUTPUT_DIR, exist_ok=True)
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# ================== 灰度判断 ==================
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def is_large_gray(image):
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img = np.array(image)
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if img.ndim != 3 or img.shape[2] != 3:
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return True
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h, w, _ = img.shape
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gray_mask = (
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(img[:, :, 0] >= GRAY_LOWER) & (img[:, :, 0] <= GRAY_UPPER) &
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(img[:, :, 1] >= GRAY_LOWER) & (img[:, :, 1] <= GRAY_UPPER) &
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(img[:, :, 2] >= GRAY_LOWER) & (img[:, :, 2] <= GRAY_UPPER)
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)
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return gray_mask.sum() / (h * w) > GRAY_RATIO_THRESHOLD
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# ================== RKNN 工具函数 ==================
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def letterbox_resize(image, size, bg_color=114):
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target_w, target_h = size
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h, w = image.shape[:2]
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scale = min(target_w / w, target_h / h)
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new_w, new_h = int(w * scale), int(h * scale)
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resized = cv2.resize(image, (new_w, new_h))
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canvas = np.full((target_h, target_w, 3), bg_color, dtype=np.uint8)
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dx, dy = (target_w - new_w) // 2, (target_h - new_h) // 2
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canvas[dy:dy + new_h, dx:dx + new_w] = resized
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return canvas, scale, dx, dy
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def dfl_numpy(position):
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n, c, h, w = position.shape
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p_num = 4
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mc = c // p_num
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y = position.reshape(n, p_num, mc, h, w)
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y = np.exp(y) / np.sum(np.exp(y), axis=2, keepdims=True)
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acc = np.arange(mc).reshape(1,1,mc,1,1)
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return np.sum(y * acc, axis=2)
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def box_process(position):
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grid_h, grid_w = position.shape[2:4]
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col, row = np.meshgrid(np.arange(grid_w), np.arange(grid_h))
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col = col.reshape(1,1,grid_h,grid_w)
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row = row.reshape(1,1,grid_h,grid_w)
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grid = np.concatenate((col,row), axis=1)
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stride = np.array([IMG_SIZE[1]//grid_h, IMG_SIZE[0]//grid_w]).reshape(1,2,1,1)
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position = dfl_numpy(position)
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box_xy = grid + 0.5 - position[:,0:2,:,:]
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box_xy2 = grid + 0.5 + position[:,2:4,:,:]
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return np.concatenate((box_xy*stride, box_xy2*stride), axis=1)
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def filter_boxes(boxes, box_confidences, box_class_probs):
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boxes = boxes.reshape(-1,4)
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box_confidences = box_confidences.reshape(-1)
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box_class_probs = np.array(box_class_probs)
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class_ids = np.argmax(box_class_probs, axis=-1)
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class_scores = box_class_probs[np.arange(len(class_ids)), class_ids]
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scores = box_confidences * class_scores
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mask = scores >= OBJ_THRESH
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if np.sum(mask) == 0:
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return None
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return True # 只需要判断是否有目标
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def post_process(outputs, scale, dx, dy):
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boxes_list, conf_list, class_list = [], [], []
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for i in range(3):
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boxes_list.append(box_process(outputs[i*3]))
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conf_list.append(outputs[i*3+2])
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class_list.append(outputs[i*3+1])
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def flatten(x):
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x = x.transpose(0,2,3,1)
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return x.reshape(-1,x.shape[3])
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boxes = np.concatenate([flatten(b) for b in boxes_list])
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box_conf = np.concatenate([flatten(c) for c in conf_list])
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class_probs = np.concatenate([flatten(c) for c in class_list])
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return filter_boxes(boxes, box_conf, class_probs)
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# ================== RKNN 初始化 ==================
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rknn = RKNNLite()
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if rknn.load_rknn(RKNN_MODEL) != 0:
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raise RuntimeError("❌ RKNN 模型加载失败")
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if rknn.init_runtime(core_mask=RKNNLite.NPU_CORE_AUTO) != 0:
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raise RuntimeError("❌ RKNN Runtime 初始化失败")
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print("✅ RKNN 初始化完成")
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# ================== 视频流处理 ==================
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max_retry_seconds = 10
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retry_interval_seconds = 1
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last_gray = None
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frame_count = 0
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while True:
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cap = cv2.VideoCapture(RTSP_URL)
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start_time = time.time()
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while not cap.isOpened():
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if time.time() - start_time >= max_retry_seconds:
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print("❌ 无法连接 RTSP")
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exit(1)
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time.sleep(retry_interval_seconds)
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cap = cv2.VideoCapture(RTSP_URL)
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print("✅ 开始读取视频流")
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try:
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while True:
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ret, frame = cap.read()
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if not ret:
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print("❌ 读取失败")
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break
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frame_count += 1
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if SHOW_WINDOW:
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cv2.imshow("Camera", frame)
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if cv2.waitKey(1) == ord('q'):
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raise KeyboardInterrupt
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if frame_count % SAVE_INTERVAL != 0:
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continue
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print(f"处理帧 {frame_count}")
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# STEP1: 灰度过滤
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pil_image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
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if is_large_gray(pil_image):
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print("跳过：大面积灰色")
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continue
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# STEP2: SSIM 去重
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gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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if last_gray is not None:
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sim = ssim(gray, last_gray)
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if sim > SSIM_THRESHOLD:
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print(f"跳过：SSIM={sim:.3f}")
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continue
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last_gray = gray.copy()
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# STEP3: RKNN 推理，只判断是否有 bag
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img_resized, scale, dx, dy = letterbox_resize(frame, IMG_SIZE)
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input_data = np.expand_dims(img_resized, 0)
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outputs = rknn.inference(inputs=[input_data])
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has_bag = post_process(outputs, scale, dx, dy)
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if not has_bag:
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print("跳过：未检测到 bag")
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continue
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# STEP4: 磁盘检查
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_, _, free = shutil.disk_usage(OUTPUT_DIR)
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if free < 5*1024**3:
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print("❌ 磁盘空间不足")
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raise SystemExit(1)
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# STEP5: 保存原图
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ts = time.strftime("%Y%m%d_%H%M%S")
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ms = int((time.time()%1)*1000)
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filename = f"bag_{ts}_{ms:03d}.png"
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path = os.path.join(OUTPUT_DIR, filename)
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cv2.imwrite(path, frame) # 保存原图
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print(f"✅ 已保存: {path}")
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except KeyboardInterrupt:
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print("\n🛑 用户中断")
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break
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finally:
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cap.release()
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cv2.destroyAllWindows()
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print(f"视频流关闭，共处理 {frame_count} 帧")
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rknn.release()
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print("程序结束")
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