Short answer: 1) All the original images are just transformed (i.e. rotation, zooming, etc.) every epoch and then used for training, and 2) [Therefore] the number of images in each epoch is equal to the number of original images you have.
Long answer: In each epoch, the ImageDataGenerator applies a transformation on the images you have and use the transformed images for training. The set of transformations includes rotation, zooming, etc. By doing this you're somehow creating new data (i.e. also called data augmentation), but obviously the generated images are not totally different from the original ones. This way the learned model may be more robust and accurate as it is trained on different variations of the same image.
You need to set the steps_per_epoch argument of fit method to n_samples / batch_size, where n_samples is the total number of training data you have (i.e. 1000 in your case). This way in each epoch, each training sample is augmented only one time and therefore 1000 transformed images will be generated in each epoch.
Further, I think it's worth clarifying the meaning of "augmentation" in this context: basically we are augmenting the images when we use ImageDataGenerator and enabling its augmentation capabilities. But the word "augmentation" here does not mean, say, if we have 100 original training images we end up having 1000 images per epoch after augmentation (i.e. the number of training images does not increase per epoch). Instead, it means we use a different transformation of each image in each epoch; hence, if we train our model for, say, 5 epochs, we have used 5 different versions of each original image in training (or 100 * 5 = 500 different images in the whole training, instead of using just the 100 original images in the whole training). To put it differently, the total number of unique images increases in the whole training from start to finish, and not per epoch.
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