Main files for the Image Recognition

combined.py

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+import os
+from keras.models import load_model
+import cv2
+import numpy as np
+from PIL import Image
+import time
+
+# Set the environment variable to disable oneDNN custom operations
+os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
+
+# Load the model
+model = load_model("keras_Model.h5", compile=False)
+
+# Load the labels
+class_names = open("labels.txt", "r").readlines()
+
+# Specify the output directory
+output_directory = "C:/Users/Pubudu Madusith/Pictures/Camera Roll/Cropped"
+os.makedirs(output_directory, exist_ok=True)  # Create the directory if it doesn't exist
+
+# Specify the index of the external webcam (you may need to adjust this)
+external_webcam_index = 1  # Change this to the correct index for your external webcam
+
+# Open the external webcam
+cap = cv2.VideoCapture(external_webcam_index)
+
+while True:
+    # Capture frame from the external webcam
+    ret, frame = cap.read()
+
+    # Display the captured frame
+    cv2.imshow("External Webcam", frame)
+
+    # Convert the frame to a PIL image
+    im = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
+
+    # Perform cropping
+    width, height = im.size
+    left_crop = 100  # Adjust the left crop value as needed
+    right_crop = width - 100  # Adjust the right crop value as needed
+    top = 0
+    bottom = height
+    im_cropped = im.crop((left_crop, top, right_crop, bottom))
+
+    # Resize the cropped image to match the model's input shape
+    im_cropped = im_cropped.resize((224, 224))
+
+    # Construct the full path for the cropped image
+    output_path = os.path.join(output_directory, f"Cropped_frame.jpg")
+
+    # Save the cropped image to the output directory
+    im_cropped.save(output_path)
+
+    # Convert the cropped image to a numpy array
+    cropped_image = np.asarray(im_cropped, dtype=np.float32).reshape(1, 224, 224, 3)
+
+    # Normalize the image array
+    cropped_image = (cropped_image / 127.5) - 1
+
+    # Predict the model
+    prediction = model.predict(cropped_image)
+    index = np.argmax(prediction)
+    class_name = class_names[index].strip()
+    confidence_score = prediction[0][index]
+
+    # Print prediction and confidence score
+    print(f"Class: {class_name[2:]}, Confidence Score: {str(np.round(confidence_score * 100))[:-2]}%")
+
+    # Wait for 2 seconds before processing the next frame
+    time.sleep(2)
+
+    # Check for the escape key (27 is the ASCII for the esc key)
+    keyboard_input = cv2.waitKey(1)
+    if keyboard_input == 27:
+        break
+
+# Release the external webcam and destroy the window
+cap.release()
+cv2.destroyAllWindows()

cropping.py

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+from PIL import Image
+import os
+
+# Input directory path
+input_directory = "C:/Users/Pubudu Madusith/Pictures/Camera Roll"
+
+# Output directory path for cropped images
+output_directory = "C:/Users/Pubudu Madusith/Pictures/Camera Roll/Cropped"
+os.makedirs(output_directory, exist_ok=True)  # Create the directory if it doesn't exist
+
+# List all image files in the input directory
+image_files = [f for f in os.listdir(input_directory) if f.lower().endswith(('.png', '.jpg', '.jpeg', '.gif', '.bmp'))]
+
+# Loop through each image file and crop
+for image_file in image_files:
+    # Construct the full path for the image file
+    image_path = os.path.join(input_directory, image_file)
+
+    # Opens an image in RGB mode
+    im = Image.open(image_path)
+
+    # Size of the image in pixels (size of the original image)
+    width, height = im.size
+
+    # Setting the points for cropped image
+    left_crop = 100  # Adjust the left crop value as needed
+    right_crop = width - 100  # Adjust the right crop value as needed
+    top = 0
+    bottom = height
+
+    # Cropped image of the specified dimensions
+    im_cropped = im.crop((left_crop, top, right_crop, bottom))
+
+    # Construct the full path for the cropped image
+    output_path = os.path.join(output_directory, f"Cropped_{image_file}")
+
+    # Save the cropped image to the output directory
+    im_cropped.save(output_path)
+
+    # Optionally, show the cropped image
+    im_cropped.show()
+
+print("Cropping and saving complete.")

labels.txt

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+0 SCENT
+1 Purse
+2 Colongne
+3 No Object

main.py

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+import os
+from keras.models import load_model
+import cv2
+import numpy as np
+import tensorflow as tf
+
+# Set the environment variable to disable oneDNN custom operations
+os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
+
+# Load the model
+model = load_model("keras_Model.h5", compile=False)
+
+# Load the labels
+class_names = open("labels.txt", "r").readlines()
+
+# Specify the input and output directories
+input_directory = "C:/Users/Pubudu Madusith/Pictures/Camera Roll"  # Change this to your image directory
+# output_directory = "D:/3yp_Project/ESP32/PythonTensorFlow/Processed"
+
+# Create the output directory if it doesn't exist
+# os.makedirs(output_directory, exist_ok=True)
+
+# List all files in the input directory
+image_files = [f for f in os.listdir(input_directory) if f.endswith(('.jpg', '.jpeg', '.png'))]
+
+for image_file in image_files:
+    image_path = os.path.join(input_directory, image_file)
+
+    # Read the image from the file
+    image = cv2.imread(image_path)
+
+    # Resize the image
+    image = cv2.resize(image, (224, 224), interpolation=cv2.INTER_AREA)
+
+    # Show the image in a window
+    cv2.imshow("Image from Directory", image)
+
+    # Make the image a numpy array and reshape it to the model's input shape
+    image = np.asarray(image, dtype=np.float32).reshape(1, 224, 224, 3)
+
+    # Normalize the image array
+    image = (image / 127.5) - 1
+
+    # Predict the model
+    prediction = model.predict(image)
+    index = np.argmax(prediction)
+    class_name = class_names[index].strip()
+    confidence_score = prediction[0][index]
+
+    # Print prediction and confidence score
+    # print(f"Image: {image_file}, Class: {class_name[2:]}, Confidence Score: {str(np.round(confidence_score * 100))[:-2]}%")
+    print(class_name[2:])
+
+    # os.remove(image_path)
+    # print(f"Original image {image_file} deleted.")
+
+    # Wait for a key press to continue processing the next image
+    keyboard_input = cv2.waitKey(1)
+
+    # 27 is the ASCII for the esc key on your keyboard.
+    if keyboard_input == 27:
+        break
+
+# Destroy the window after processing all images
+cv2.destroyAllWindows()
+