Instructions to use PredictiveManish/wall-crack-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use PredictiveManish/wall-crack-detection with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://PredictiveManish/wall-crack-detection") - Notebooks
- Google Colab
- Kaggle
| import cv2 | |
| import tensorflow as tf | |
| import numpy as np | |
| model = tf.keras.models.load_model("crack_detector.h5") | |
| # Use HTTP instead of HTTPS | |
| url = "<Embedd-your-URL>" #Iinstall droidcam on phone and insert the URL | |
| cap = cv2.VideoCapture(url) | |
| while True: | |
| ret, frame = cap.read() | |
| if not ret: | |
| print("Failed to grab frame. Check IP/Port and make sure phone/laptop are on same WiFi.") | |
| break | |
| img = cv2.resize(frame, (224, 224)) | |
| img = img.astype("float32")/255.0 | |
| img = np.expand_dims(img, axis=0) | |
| pred = model.predict(img, verbose=0)[0][0] | |
| label = "No Crack" if pred<0.9 else "Crack Detected" | |
| color = (0,255,0) if label=="No Crack" else (0,0,255) | |
| cv2.putText(frame, label, (20,40), cv2.FONT_HERSHEY_SIMPLEX,1, color, 2) | |
| cv2.imshow("Wall crack detection", frame) | |
| if cv2.waitKey(1) & 0xFF == ord('q'): | |
| break | |
| cap.release() | |
| cv2.destroyAllWindows() | |