def run(self):
try:
if isinstance(self.source, int) or isinstance(self.source, str) or self.source.endswith(('.mp4', '.avi', '.mov')): # 视频或摄像头
cap = cv2.VideoCapture(self.source)
while self.running and cap.isOpened():
ret, frame = cap.read()
if not ret:
break
# 保存原始帧
original_frame = frame.copy()
# 检测
results = self.model(frame, conf=self.conf, iou=self.iou)
annotated_frame = results[0].plot()
# 提取检测结果
detections = []
for result in results:
for box in result.boxes:
class_id = int(box.cls)
class_name = self.model.names[class_id]
confidence = float(box.conf)
x, y, w, h = box.xywh[0].tolist()
detections.append((class_name, confidence, x, y))
# 发送信号
self.frame_received.emit(
cv2.cvtColor(original_frame, cv2.COLOR_BGR2RGB),
cv2.cvtColor(annotated_frame, cv2.COLOR_BGR2RGB),
detections
)
# 控制帧率
#time.sleep(0.03) # 约30fps
time.sleep(0.01) # 约30fps
cap.release()
else: # 图片
frame = cv2.imread(self.source)
if frame is not None:
original_frame = frame.copy()
results = self.model(frame, conf=self.conf, iou=self.iou)
annotated_frame = results[0].plot()
# 提取检测结果
detections = []
for result in results:
for box in result.boxes:
class_id = int(box.cls)
class_name = self.model.names[class_id]
confidence = float(box.conf)
x, y, w, h = box.xywh[0].tolist()
detections.append((class_name, confidence, x, y))
self.frame_received.emit(
cv2.cvtColor(original_frame, cv2.COLOR_BGR2RGB),
cv2.cvtColor(annotated_frame, cv2.COLOR_BGR2RGB),
detections
)
except Exception as e:
print(f"Detection error: {e}")
finally:
self.finished_signal.emit() 网络摄像头检测，存在严重的视频流延迟问题，如何处理该问题
最新发布
该博客围绕 cv2.VideoCapture 进行跳帧取图操作，虽未给出具体内容，但推测是关于利用此工具实现视频跳帧获取图像的信息技术相关内容。
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