643bd0823c1f09fcb621207f3345879abf694ab0,demo.py,,main,#,85

Before Change

            print("prediction done!")

            red_mask = np.zeros((img_shape+(3,)))
            red_mask[:, :] = (0, 0, 255)

            // print(image.shape)
            // image = (image * (1 - display_mask) + red_mask * display_mask).astype(np.uint8)
            image = prediction

After Change

            dismap = np.expand_dims(dismap, axis=-1)

            image = image[:, :, ::-1] // change to rgb
            merge_input = np.concatenate((image, dismap), axis=2).astype(np.float32)
            inputs = torch.from_numpy(merge_input.transpose((2, 0, 1))[np.newaxis, ...])

            // Run a forward pass
            inputs = inputs.to(device)
            outputs = net.forward(inputs)
            outputs = upsample(outputs, size=(450, 450), mode="bilinear", align_corners=True)
            outputs = outputs.to(torch.device("cpu"))

            prediction = np.transpose(outputs.data.numpy()[0, ...], (1, 2, 0))
            prediction = 1 / (1 + np.exp(-prediction))
            prediction = np.squeeze(prediction)
            prediction[prediction>0.8] = 255
            prediction[prediction<=0.8] = 0
            print("prediction done!")
            prediction = np.expand_dims(prediction, axis=-1).astype(np.uint8)
            image = image[:, :, ::-1] // change to bgr
            display_mask = np.concatenate([prediction, prediction, prediction], axis=-1)
            image = cv2.addWeighted(image, 0.9, display_mask, 0.5, 0.1)

        if k == 99:
            break

Instances