Website fingerprinting technique enables the local monitor to track which websites a user is visiting by capturing anonymous traffic between that user and the Tor （The onion router） entry nodes. Prior researches only extract part meta-data in the anonymous traffic to construct website fingerprints, and ignore much hidden fingerprint information inside the traffic. Therefore, a website fingerprinting technique named Image FingerPrinting (Image-FP) and based on deep convolutional neural network and image texture was proposed. Firstly, the anonymous communication traffic was mapped into Red-Green-Blue (RGB) images. Then, the Residual Network (ResNet) was used to construct the website fingerprinting model with automatic feature learning ability. In a closed-world scenario of 50 websites, Image-FP obtained classification accuracy of 97.2%, which is 0.4 percentage points higher than that of the state-of-the-art website fingerprinting attack technique. In the open-world scenario which is more realistic, Image-FP can identify the traffic of monitored websites with 100% accuracy, has the strongest accuracy and robustness among all fingerprinting techniques. The experimental results demonstrate that, the technique of converting anonymous traffic into images can preserve more features relevant to the website fingerprints, and further improve the classification accuracy while avoiding complex feature engineering