upload_app

app.py

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+import numpy as np
+import cv2
+import torch
+import sys
+import pathlib
+temp = pathlib.PosixPath
+pathlib.PosixPath = pathlib.WindowsPath
+
+yolov5_path = pathlib.PosixPath('yolov5')
+sys.path.append(str(yolov5_path))
+
+from models.experimental import attempt_load
+from utils.general import non_max_suppression, scale_boxes
+
+model = attempt_load('best.pt')
+model.eval()
+pathlib.PosixPath = temp
+
+cap = cv2.VideoCapture(0)
+
+while cap.isOpened():
+    ret, frame = cap.read()
+    if not ret:
+        print("Failed to grab frame")
+        break
+
+    # Resize the frame to 640x640
+    img = cv2.resize(frame, (640, 640))
+    img = img[:, :, ::-1]  # Convert BGR to RGB
+    img = np.ascontiguousarray(img)  # Make sure the array is contiguous
+    img = torch.from_numpy(img).float()  # Convert to Tensor
+    img /= 255.0  # Normalize to [0, 1]
+
+    # Change the shape to [batch_size, channels, height, width]
+    img = img.permute(2, 0, 1).unsqueeze(0)  # Rearrange dimensions and add batch dimension
+
+    # Perform inference
+    with torch.no_grad():
+        pred = model(img)[0]  # Get predictions
+
+    # Apply Non-Maximum Suppression
+    pred = non_max_suppression(pred, conf_thres=0.25, iou_thres=0.45)
+
+    # Process predictions
+    for det in pred:  # detections per image
+        if det is not None and len(det):
+            # Rescale boxes from 640 to original frame size
+            det[:, :4] = scale_boxes(img.shape[2:], det[:, :4], frame.shape).round()
+
+            # Draw bounding boxes on the frame
+            for *xyxy, conf, cls in reversed(det):
+                label = f'{model.names[int(cls)]}: {conf:.2f}'  # Create label
+                cv2.rectangle(frame, (int(xyxy[0]), int(xyxy[1])), (int(xyxy[2]), int(xyxy[3])), (0, 255, 0), 2)  # Draw box
+                cv2.putText(frame, label, (int(xyxy[0]), int(xyxy[1] - 10)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)  # Draw label
+
+    # Display the frame
+    cv2.imshow('Deteksi Cadar Masker', frame)
+
+    # Break the loop on 'q' key press
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break
+
+# Release the webcam and close windows
+cap.release()
+cv2.destroyAllWindows()