Updated to MONO REPO

ios/.gitignore

@@ -26,7 +26,7 @@ Flutter/flutter_assets/
 Flutter/flutter_export_environment.sh
 ServiceDefinitions.json
 Runner/GeneratedPluginRegistrant.*
-
+server/crackapp-23e59-firebase-adminsdk-o18mf-aafa26780f.json
 # Exceptions to above rules.
 !default.mode1v3
 !default.mode2v3

lib/pages/scan.dart

@@ -66,8 +66,8 @@ class _scanState extends State<scan> {
 
 
 
-  // final String apiUrl = "https://a48e-203-192-251-182.ngrok.io/imageapi";
-  final String apiUrl = "http://10.0.2.2:3000/imageapi";
+  final String apiUrl = "https://5078-119-161-98-148.ngrok-free.app/imageapi";
+  // final String apiUrl = "http://10.0.2.2:3000/imageapi";
   String? b64;
 
   Future sendImage(File imageFile, BuildContext context) async {

server/requirements.txt

@@ -0,0 +1,11 @@
+firebase_admin==6.1.0
+Flask==2.3.2
+flask_ngrok==0.0.25
+matplotlib==3.5.3
+numpy==1.23.2
+opencv_contrib_python==4.7.0.72
+opencv_python==4.7.0.72
+opencv_python_headless==4.6.0.66
+Pillow==9.3.0
+Pillow==10.0.0
+roboflow==1.0.8

server/server.py

@@ -0,0 +1,102 @@
+from flask import *
+from PIL import Image
+import io
+import base64
+import cv2 as cv
+from roboflow import Roboflow
+from flask_ngrok import run_with_ngrok
+import matplotlib.pyplot as plt
+from firebase_admin import credentials, initialize_app, storage
+import datetime
+import string
+
+cred = credentials.Certificate(
+    "crackapp-23e59-firebase-adminsdk-o18mf-aafa26780f.json")
+initialize_app(
+    cred, {'storageBucket': 'crackapp-23e59.appspot.com'})
+app = Flask(__name__)
+# run_with_ngrok(app)
+
+
+
+@app.route('/')
+def home():
+    return jsonify({'message': 'hi'})
+
+
+
+
+
+
+@app.route('/imageapi', methods=['POST'])
+def crack_finder():
+    image_str = request.json.get('image')
+    image_bytes = base64.b64decode(image_str)
+    image_file = io.BytesIO(image_bytes)
+
+    image = Image.open(image_file)
+    image = image.convert("RGB")
+    image.save("K:/MistralHack/crackapp/server/test.jpg")
+
+    rf = Roboflow(api_key="DzJXlBY4OY5a7ohy6WQ2")
+    #########additional app keys##############
+    # DzJXlBY4OY5a7ohy6WQ2
+    # ao0gkNxhFLHpuGLDxLya
+    # x9uX7YwqY57VddkIXY5L
+    # ########crack###############
+    project = rf.workspace().project("crack-shadyar-zhir")
+    model = project.version(1).model
+    model.predict("test.jpg").save("crack.jpg")
+
+    # ##########dent##############
+    # project = rf.workspace().project("dentsegmentation")
+    # model = project.version(2).model
+    # model.predict("test.jpg").save("dent.jpg")
+
+    project = rf.workspace().project("car_dent_scratch_detection-1")
+    model = project.version(9).model
+    model.predict("test.jpg").save("dent.jpg")
+
+    # project = rf.workspace().project("dent-7ltse")
+    # model = project.version(2).model
+    # model.predict("test.jpg").save("dent.jpg")
+
+
+    Pred = Image.open('K:/MistralHack/crackapp/server/crack.jpg')
+    pred2 = Image.open('K:/MistralHack/crackapp/server/dent.jpg')
+    t_res = image.resize((250,200))
+    pred_res = Pred.resize((250,200))
+    pred_res_2 = pred2.resize((250,200))
+    final = Image.new("RGB", (790, 200), "white")
+    final.paste(t_res,(0,0))
+    final.paste(pred_res,(270,0))
+    final.paste(pred_res_2, (540, 0))
+    final.save("K:/MistralHack/crackapp/server/final.jpg")
+
+    with open('K:/MistralHack/crackapp/server/final.jpg', 'rb') as f:
+        image_bytes = f.read()
+        image_b64 = base64.b64encode(image_bytes).decode('utf-8')
+        res = Response(image_b64, mimetype='text/plain')
+        @res.call_on_close
+        def handle_post_request_completion():
+            # Code to be executed after the POST request is completed
+            file_extension = 'jpg'
+            destination_filename = generate_destination_filename(file_extension)
+            fileName = "final.jpg"
+            bucket = storage.bucket()
+            blob = bucket.blob(destination_filename)
+            blob.upload_from_filename(fileName)
+            # Opt : if you want to make public access from the URL
+            blob.make_public() 
+        return res
+
+
+def generate_destination_filename(file_extension):
+    timestamp = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
+    return f"images/{timestamp}.{file_extension}"
+
+
+
+if __name__ == '__main__':
+    # app.run()  # for global deployment
+    app.run(port=3001) # for local development

server/testing/img.py

@@ -0,0 +1,17 @@
+from flask import *
+from PIL import Image
+import io
+import base64
+import cv2 as cv
+from roboflow import Roboflow
+from flask_ngrok import run_with_ngrok
+import matplotlib.pyplot as plt
+from firebase_admin import credentials, initialize_app, storage
+import datetime
+import string
+
+rf = Roboflow(api_key="DzJXlBY4OY5a7ohy6WQ2")
+
+project = rf.workspace().project("car_dent_scratch_detection-1")
+model = project.version(9).model
+model.predict("car_dent.jpg").save("dent.jpg")

server/testing/t2.py

@@ -0,0 +1,101 @@
+import cv2
+import numpy as np
+
+
+def detect_cracks(image_path):
+    # Load the image
+    image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
+
+    # Apply image preprocessing (e.g., thresholding, blurring) to enhance crack visibility
+    blurred = cv2.GaussianBlur(image, (5, 5), 0)
+    _, thresholded = cv2.threshold(
+        blurred, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
+
+    # Perform morphological operations to fill gaps and reduce noise
+    kernel = np.ones((3, 3), np.uint8)
+    dilated = cv2.dilate(thresholded, kernel, iterations=2)
+    closing = cv2.morphologyEx(dilated, cv2.MORPH_CLOSE, kernel)
+
+    # Find contours in the image
+    contours, _ = cv2.findContours(
+        closing, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+    # Iterate through the contours and filter out smaller ones (adjust the area threshold as needed)
+    min_area_threshold = 100
+    detected_cracks = []
+    for contour in contours:
+        area = cv2.contourArea(contour)
+        if area > min_area_threshold:
+            # Calculate the bounding rectangle for the contour
+            x, y, w, h = cv2.boundingRect(contour)
+            detected_cracks.append((x, y, x + w, y + h))
+
+    return detected_cracks
+
+
+# Example usage
+image_path = 'car.jpeg'
+image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
+
+cracks = detect_cracks(image_path)
+for crack in cracks:
+    x1, y1, x2, y2 = crack
+    cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
+
+# Display the image with detected cracks
+cv2.imshow('Detected Cracks', image)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
+
+
+
+
+
+###################################   DENT  #####################################
+# import cv2
+# import numpy as np
+
+
+# def detect_dents(image_path):
+#     # Load the image
+#     image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
+
+#     # Apply image preprocessing (e.g., thresholding, blurring) to enhance dent visibility
+#     blurred = cv2.GaussianBlur(image, (5, 5), 0)
+#     _, thresholded = cv2.threshold(
+#         blurred, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
+
+#     # Perform morphological operations to fill gaps and reduce noise
+#     kernel = np.ones((3, 3), np.uint8)
+#     dilated = cv2.dilate(thresholded, kernel, iterations=2)
+#     closing = cv2.morphologyEx(dilated, cv2.MORPH_CLOSE, kernel)
+
+#     # Find contours in the image
+#     contours, _ = cv2.findContours(
+#         closing, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+#     # Iterate through the contours and filter out smaller ones (adjust the area threshold as needed)
+#     min_area_threshold = 100
+#     detected_dents = []
+#     for contour in contours:
+#         area = cv2.contourArea(contour)
+#         if area > min_area_threshold:
+#             # Calculate the bounding rectangle for the contour
+#             x, y, w, h = cv2.boundingRect(contour)
+#             detected_dents.append((x, y, x + w, y + h))
+
+#     return detected_dents
+
+
+# # Example usage
+# image_path = 'car_dent.jpg'
+# image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
+# dents = detect_dents(image_path)
+# for dent in dents:
+#     x1, y1, x2, y2 = dent
+#     cv2.rectangle(image, (x1, y1), (x2, y2), (0, 0, 255), 2)
+
+# # Display the image with detected dents
+# cv2.imshow('Detected Dents', image)
+# cv2.waitKey(0)
+# cv2.destroyAllWindows()