versionJan2024

app.py

@@ -1,21 +1,22 @@
 import streamlit as st
 import cv2
 import numpy as np
-from PIL import Image
-import imutils
-import easyocr
-import os
-import pathlib
-import platform
-from xyxy_converter import yolov5_to_image_coordinates
-import shutil
-
-system_platform = platform.system()
-if system_platform == 'Windows': pathlib.PosixPath = pathlib.WindowsPath
-
-CUR_DIR = os.getcwd()
-YOLO_PATH = f"{CUR_DIR}/yolov5"
-MODEL_PATH = "runs/train/exp/weights/best.pt"
+from PIL import Image, ImageDraw
+# import imutils
+# import easyocr
+# import os
+# import pathlib
+# import platform
+# from xyxy_converter import yolov5_to_image_coordinates
+# import shutil
+from models import get_odometer_xy
+
+# system_platform = platform.system()
+# if system_platform == 'Windows': pathlib.PosixPath = pathlib.WindowsPath
+
+# CUR_DIR = os.getcwd()
+# YOLO_PATH = f"{CUR_DIR}/yolov5"
+# MODEL_PATH = "runs/train/exp/weights/best.pt"
 
 def main():
     st.title("Odometer value extractor with Streamlit")
@@ -32,6 +33,37 @@ def main():
         resized_image = resized_image.astype(np.uint8)
         cv2.imwrite('odometer_image.jpg', resized_image)
 
+        # original_img = cv2.imread('odometer_image.jpg')
+        gray = cv2.cvtColor(resized_image, cv2.COLOR_BGR2GRAY)
+
+        x1, y1, x2, y2, odo_confidence = get_odometer_xy(
+            model_path='odo_detector.tflite',
+            image_path='odometer_image.jpg'
+        )
+
+        st.write(odo_confidence)
+
+        if odo_confidence == 0:
+            display_text = "An odometer is not detected in the image!!!"
+            st.image('odometer_image.jpg', caption=f"{display_text}", use_column_width=True)
+        else:
+            cropped_image = gray[y1:y2, x1:x2]
+            cv2.imwrite('odometer_number_image.jpg', cropped_image)
+            display_text = 'Here is the zoomed odometer value'
+            st.image('odometer_number_image.jpg', caption=f"{display_text}", use_column_width=True)
+
+            image = Image.open('odometer_image.jpg')
+            image_resized = image.resize((640, 640))
+            draw = ImageDraw.Draw(image_resized)
+            draw.rectangle([x1, y1, x2, y2], outline="red", width=2)
+            class_name = 'odometer'
+            text = f"Class: {class_name}, Confidence: {odo_confidence:.2f}"
+            draw.text((x1, y1), text, fill="red")
+            # Saving Images
+            image_resized.save('odometer_highlighted_image.jpg')
+            display_text = 'Here is the odometer on the image.'
+            st.image('odometer_highlighted_image.jpg', caption=f"{display_text}", use_column_width=True)
+
         # detect(
         #     weights='yolov5\runs\train\exp\weights\best.pt',
         #     source='odometer_image.jpg',
@@ -44,83 +76,83 @@ def main():
         #     exist_ok=True
         # )
 
-        # os.system('wandb disabled')
-
-        os.chdir(YOLO_PATH)
+        # # os.system('wandb disabled')
+
+        # os.chdir(YOLO_PATH)
+
+        # # try:
+        # #     shutil.rmtree('runs/detect/temp_exp')
+        # # except:
+        # #     pass
+
+        # image_path = "../odometer_image.jpg"
+        # # command = f"python detect.py --weights {MODEL_PATH} --source {image_path} --img 640 --conf 0.4 --name 'temp_exp' --hide-labels --hide-conf --save-txt --exist-ok"
+        # command = f'''
+        # python detect.py \
+        #     --weights {MODEL_PATH} \
+        #     --source {image_path} \
+        #     --img 640 \
+        #     --conf 0.4 \
+        #     --name temp_exp \
+        #     --hide-labels \
+        #     --hide-conf \
+        #     --save-txt \
+        #     --exist-ok \
+        #     --save-conf
+        # '''
+
+        # # Run the command
+        # os.system(command)
+
+        # # st.write('The detection is completed!!!')
+
+        # os.chdir(CUR_DIR)
+
+        # # st.write(os.path.exists('yolov5/runs/detect/temp_exp'))
+
+        # if os.path.exists('yolov5/runs/detect/temp_exp'):
+        #     processed_image = cv2.imread('yolov5/runs/detect/temp_exp/odometer_image.jpg')
+        #     # st.write('Image boxed and loaded')
+        #     text_files = os.listdir('yolov5/runs/detect/temp_exp/labels')
+        #     original_img = cv2.imread('odometer_image.jpg')
+        #     gray = cv2.cvtColor(original_img, cv2.COLOR_BGR2GRAY)
+
+        #     if len(text_files) == 0:
+        #         display_text = "An odometer is not detected in the image!!!"
+        #     else:
+        #         text_file_path = f'yolov5/runs/detect/temp_exp/labels/{text_files[0]}'
+        #         x1, y1, x2, y2 = yolov5_to_image_coordinates(text_file_path)
+        #         # st.write(x1, y1, x2, y2)
+        #         cropped_image = gray[x1:x2, y1:y2]
+
+        #         reader = easyocr.Reader(['en'])
+        #         result = reader.readtext(cropped_image)
+
+        #         if len(result) != 0:
+        #             odometer_value = sorted(result, key=lambda x: x[2], reverse=True)[0][1]
+        #             display_text = f"Odometer value: {odometer_value}"
+        #         else:
+        #             odometer_value = 'not detected'
+        #             display_text = f"The odometer value is {odometer_value}!!!"
+        # else:
+        #     display_text = "An odometer is not detected in the image!!!"
+        #     processed_image = cv2.imread('odometer_image.jpg')
 
         # try:
+        #     shutil.rmtree('odometer_image.jpg')
         #     shutil.rmtree('runs/detect/temp_exp')
         # except:
         #     pass
 
-        image_path = "../odometer_image.jpg"
-        # command = f"python detect.py --weights {MODEL_PATH} --source {image_path} --img 640 --conf 0.4 --name 'temp_exp' --hide-labels --hide-conf --save-txt --exist-ok"
-        command = f'''
-        python detect.py \
-            --weights {MODEL_PATH} \
-            --source {image_path} \
-            --img 640 \
-            --conf 0.4 \
-            --name temp_exp \
-            --hide-labels \
-            --hide-conf \
-            --save-txt \
-            --exist-ok \
-            --save-conf
-        '''
-
-        # Run the command
-        os.system(command)
-
-        # st.write('The detection is completed!!!')
-
-        os.chdir(CUR_DIR)
-
-        # st.write(os.path.exists('yolov5/runs/detect/temp_exp'))
-
-        if os.path.exists('yolov5/runs/detect/temp_exp'):
-            processed_image = cv2.imread('yolov5/runs/detect/temp_exp/odometer_image.jpg')
-            # st.write('Image boxed and loaded')
-            text_files = os.listdir('yolov5/runs/detect/temp_exp/labels')
-            original_img = cv2.imread('odometer_image.jpg')
-            gray = cv2.cvtColor(original_img, cv2.COLOR_BGR2GRAY)
-
-            if len(text_files) == 0:
-                display_text = "An odometer is not detected in the image!!!"
-            else:
-                text_file_path = f'yolov5/runs/detect/temp_exp/labels/{text_files[0]}'
-                x1, y1, x2, y2 = yolov5_to_image_coordinates(text_file_path)
-                # st.write(x1, y1, x2, y2)
-                cropped_image = gray[x1:x2, y1:y2]
-
-                reader = easyocr.Reader(['en'])
-                result = reader.readtext(cropped_image)
-
-                if len(result) != 0:
-                    odometer_value = sorted(result, key=lambda x: x[2], reverse=True)[0][1]
-                    display_text = f"Odometer value: {odometer_value}"
-                else:
-                    odometer_value = 'not detected'
-                    display_text = f"The odometer value is {odometer_value}!!!"
-        else:
-            display_text = "An odometer is not detected in the image!!!"
-            processed_image = cv2.imread('odometer_image.jpg')
-
-        try:
-            shutil.rmtree('odometer_image.jpg')
-            shutil.rmtree('runs/detect/temp_exp')
-        except:
-            pass
-
-        # Resize or preprocess the image as needed for your model
-        # For example, resizing to a specific input size
-        # processed_image = cv2.resize(image_np, (224, 224))
+        # # Resize or preprocess the image as needed for your model
+        # # For example, resizing to a specific input size
+        # # processed_image = cv2.resize(image_np, (224, 224))
         
-        # Process the image using your deep learning model
-        # processed_image = process_image(image_np)
+        # # Process the image using your deep learning model
+        # # processed_image = process_image(image_np)
 
-        # Display the processed image
-        st.image(processed_image, caption=f"{display_text}", use_column_width=True)
+        # # Display the processed image
+        # st.image(processed_image, caption=f"{display_text}", use_column_width=True)
 
         st.session_state.pop("odometer")
 

app_v1.py

@@ -0,0 +1,128 @@
+import streamlit as st
+import cv2
+import numpy as np
+from PIL import Image
+import imutils
+import easyocr
+import os
+import pathlib
+import platform
+from xyxy_converter import yolov5_to_image_coordinates
+import shutil
+
+system_platform = platform.system()
+if system_platform == 'Windows': pathlib.PosixPath = pathlib.WindowsPath
+
+CUR_DIR = os.getcwd()
+YOLO_PATH = f"{CUR_DIR}/yolov5"
+MODEL_PATH = "runs/train/exp/weights/best.pt"
+
+def main():
+    st.title("Odometer value extractor with Streamlit")
+
+    # Use st.camera to capture images from the user's camera
+    img_file_buffer = st.camera_input(label='Please, take a photo of odometer', key="odometer")
+
+    # Check if an image is captured
+    if img_file_buffer is not None:
+        # Convert the image to a NumPy array
+        image = Image.open(img_file_buffer)
+        image_np = np.array(image)
+        resized_image = cv2.resize(image_np, (640, 640))
+        resized_image = resized_image.astype(np.uint8)
+        cv2.imwrite('odometer_image.jpg', resized_image)
+
+        # detect(
+        #     weights='yolov5\runs\train\exp\weights\best.pt',
+        #     source='odometer_image.jpg',
+        #     img=640,
+        #     conf=0.4,
+        #     name='temp_exp',
+        #     hide_labels=True,
+        #     hide_conf=True,
+        #     save_txt=True,
+        #     exist_ok=True
+        # )
+
+        # os.system('wandb disabled')
+
+        os.chdir(YOLO_PATH)
+
+        # try:
+        #     shutil.rmtree('runs/detect/temp_exp')
+        # except:
+        #     pass
+
+        image_path = "../odometer_image.jpg"
+        # command = f"python detect.py --weights {MODEL_PATH} --source {image_path} --img 640 --conf 0.4 --name 'temp_exp' --hide-labels --hide-conf --save-txt --exist-ok"
+        command = f'''
+        python detect.py \
+            --weights {MODEL_PATH} \
+            --source {image_path} \
+            --img 640 \
+            --conf 0.4 \
+            --name temp_exp \
+            --hide-labels \
+            --hide-conf \
+            --save-txt \
+            --exist-ok \
+            --save-conf
+        '''
+
+        # Run the command
+        os.system(command)
+
+        # st.write('The detection is completed!!!')
+
+        os.chdir(CUR_DIR)
+
+        # st.write(os.path.exists('yolov5/runs/detect/temp_exp'))
+
+        if os.path.exists('yolov5/runs/detect/temp_exp'):
+            processed_image = cv2.imread('yolov5/runs/detect/temp_exp/odometer_image.jpg')
+            # st.write('Image boxed and loaded')
+            text_files = os.listdir('yolov5/runs/detect/temp_exp/labels')
+            original_img = cv2.imread('odometer_image.jpg')
+            gray = cv2.cvtColor(original_img, cv2.COLOR_BGR2GRAY)
+
+            if len(text_files) == 0:
+                display_text = "An odometer is not detected in the image!!!"
+            else:
+                text_file_path = f'yolov5/runs/detect/temp_exp/labels/{text_files[0]}'
+                x1, y1, x2, y2 = yolov5_to_image_coordinates(text_file_path)
+                # st.write(x1, y1, x2, y2)
+                cropped_image = gray[x1:x2, y1:y2]
+
+                reader = easyocr.Reader(['en'])
+                result = reader.readtext(cropped_image)
+
+                if len(result) != 0:
+                    odometer_value = sorted(result, key=lambda x: x[2], reverse=True)[0][1]
+                    display_text = f"Odometer value: {odometer_value}"
+                else:
+                    odometer_value = 'not detected'
+                    display_text = f"The odometer value is {odometer_value}!!!"
+        else:
+            display_text = "An odometer is not detected in the image!!!"
+            processed_image = cv2.imread('odometer_image.jpg')
+
+        try:
+            shutil.rmtree('odometer_image.jpg')
+            shutil.rmtree('runs/detect/temp_exp')
+        except:
+            pass
+
+        # Resize or preprocess the image as needed for your model
+        # For example, resizing to a specific input size
+        # processed_image = cv2.resize(image_np, (224, 224))
+        
+        # Process the image using your deep learning model
+        # processed_image = process_image(image_np)
+
+        # Display the processed image
+        st.image(processed_image, caption=f"{display_text}", use_column_width=True)
+
+        st.session_state.pop("odometer")
+
+if __name__ == "__main__":
+    main()

models.py

@@ -0,0 +1,70 @@
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+def get_odometer_xy(model_path, image_path):
+    #model_path = 'odo_detector.tflite'
+    interpreter = tf.lite.Interpreter(model_path=model_path)
+    interpreter.allocate_tensors()
+
+    input_details = interpreter.get_input_details()
+    output_details = interpreter.get_output_details()
+
+    # Obtain the height and width of the corresponding image from the input tensor
+    image_height = input_details[0]['shape'][1] # 640
+    image_width = input_details[0]['shape'][2] # 640
+
+    # Image Preparation
+    # image_name = 'car.jpg'
+    image = Image.open(image_path)
+    image_resized = image.resize((image_width, image_height)) # Resize the image to the corresponding size of the input tensor and store it in a new variable
+
+    image_np = np.array(image_resized) #
+    image_np = np.true_divide(image_np, 255, dtype=np.float32) 
+    image_np = image_np[np.newaxis, :]
+
+    # inference
+    interpreter.set_tensor(input_details[0]['index'], image_np)
+    interpreter.invoke()
+
+    # Obtaining output results
+    output = interpreter.get_tensor(output_details[0]['index'])
+    output = output[0]
+    output = output.T
+
+    boxes_xywh = output[:, :4] #Get coordinates of bounding box, first 4 columns of output tensor
+    scores = output[:, 4]#np.max(output[..., 5:], axis=1) #Get score value, 5th column of output tensor
+    classes = np.zeros(len(scores))#np.argmax(output[..., 5:], axis=1) # Get the class value, get the 6th and subsequent columns of the output tensor, and store the largest value in the output tensor.
+
+    # Threshold Setting
+    # threshold = 0.7
+    final_score = 0
+    x_center, y_center, width, height = 0, 0, 0, 0
+    class_name = 'odometer'
+
+    # Bounding boxes, scores, and classes are drawn on the image
+    # draw = ImageDraw.Draw(image_resized)
+
+    for box, score, cls in zip(boxes_xywh, scores, classes):
+        if score >= final_score:
+            x_center, y_center, width, height = box
+            final_score = score
+            class_name = cls
+        else:
+            pass
+            
+    x1 = int((x_center - width / 2) * image_width)
+    y1 = int((y_center - height / 2) * image_height)
+    x2 = int((x_center + width / 2) * image_width)
+    y2 = int((y_center + height / 2) * image_height)
+
+    # draw.rectangle([x1, y1, x2, y2], outline="red", width=2)
+    # text = f"Class: {class_name}, Score: {final_score:.2f}"
+    # draw.text((x1, y1), text, fill="red")
+
+     # Saving Images
+    # image_resized.save('test_img.jpg')
+
+    return x1, y1, x2, y2, final_score
+
+   

odo_detector.tflite

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:98eee28ce3552c3e780150c7461ca0975236b82cf4884e67d484eb9daa98473b
+size 6191514

requirements.txt

@@ -1,79 +1,13 @@
-altair==5.2.0
-attrs==23.2.0
-blinker==1.7.0
-cachetools==5.3.2
-certifi==2023.11.17
-charset-normalizer==3.3.2
-click==8.1.7
-colorama==0.4.6
-contourpy==1.2.0
-cycler==0.12.1
-easyocr==1.7.1
-filelock==3.13.1
-fonttools==4.47.0
-fsspec==2023.12.2
-gitdb==4.0.11
-GitPython==3.1.40
-idna==3.6
 imageio==2.33.1
-importlib-metadata==6.11.0
-imutils==0.5.4
-Jinja2==3.1.2
-jsonschema==4.20.0
-jsonschema-specifications==2023.12.1
-kiwisolver==1.4.5
-lazy_loader==0.3
-markdown-it-py==3.0.0
-MarkupSafe==2.1.3
 matplotlib==3.8.2
-mdurl==0.1.2
-mpmath==1.3.0
-networkx==3.2.1
-ninja==1.11.1.1
 numpy==1.26.3
 opencv-python==4.9.0.80
 opencv-python-headless==4.9.0.80
-packaging==23.2
-pandas==2.1.4
 pillow==10.2.0
-protobuf==4.25.1
-psutil==5.9.7
-py-cpuinfo==9.0.0
-pyarrow==14.0.2
-pyclipper==1.3.0.post5
-pydeck==0.8.1b0
-Pygments==2.17.2
-pyparsing==3.1.1
-python-bidi==0.4.2
-python-dateutil==2.8.2
-pytz==2023.3.post1
-PyYAML==6.0.1
-referencing==0.32.0
-requests==2.31.0
-rich==13.7.0
-rpds-py==0.16.2
-scikit-image==0.22.0
-scipy==1.11.4
-seaborn==0.13.1
-shapely==2.0.2
-six==1.16.0
-smmap==5.0.1
 streamlit==1.29.0
-sympy==1.12
-tenacity==8.2.3
-thop==0.1.1.post2209072238
-tifffile==2023.12.9
-toml==0.10.2
-toolz==0.12.0
-torch==2.1.2
-torchvision==0.16.2
-tornado==6.4
-tqdm==4.66.1
-typing_extensions==4.9.0
-tzdata==2023.4
-tzlocal==5.2
-ultralytics==8.0.234
-urllib3==2.1.0
-validators==0.22.0
-watchdog==3.0.0
-zipp==3.17.0
+tensorboard==2.15.1
+tensorboard-data-server==0.7.2
+tensorflow==2.15.0
+tensorflow-estimator==2.15.0
+tensorflow-intel==2.15.0
+tensorflow-io-gcs-filesystem==0.31.0