0
Jump into concepts and practice - no test required
Recommended
import cv2 import numpy as np from sklearn.model_selection import train_test_split from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense from tensorflow.keras.utils import to_categorical # Dummy dataset creation: 100 images of 64x64 with 3 channels (RGB) np.random.seed(42) X_rgb = np.random.randint(0, 256, (100, 64, 64, 3), dtype=np.uint8) y = np.random.randint(0, 2, 100) # Binary classification def convert_to_grayscale(images): return np.array([cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) for img in images]) def convert_to_hsv(images): return np.array([cv2.cvtColor(img, cv2.COLOR_RGB2HSV) for img in images]) # Convert images X_gray = convert_to_grayscale(X_rgb) X_hsv = convert_to_hsv(X_rgb) # Reshape grayscale to add channel dimension X_gray = X_gray.reshape(-1, 64, 64, 1) # Normalize all images X_rgb = X_rgb / 255.0 X_gray = X_gray / 255.0 X_hsv = X_hsv / 255.0 # Prepare labels y_cat = to_categorical(y, 2) # Split data X_train_rgb, X_val_rgb, y_train, y_val = train_test_split(X_rgb, y_cat, test_size=0.2, random_state=42) X_train_gray, X_val_gray, _, _ = train_test_split(X_gray, y_cat, test_size=0.2, random_state=42) X_train_hsv, X_val_hsv, _, _ = train_test_split(X_hsv, y_cat, test_size=0.2, random_state=42) # Define simple CNN model def create_model(input_shape): model = Sequential([ Conv2D(16, (3,3), activation='relu', input_shape=input_shape), MaxPooling2D((2,2)), Flatten(), Dense(32, activation='relu'), Dense(2, activation='softmax') ]) model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) return model # Train and evaluate on RGB model_rgb = create_model((64,64,3)) history_rgb = model_rgb.fit(X_train_rgb, y_train, epochs=5, batch_size=16, validation_data=(X_val_rgb, y_val), verbose=0) # Train and evaluate on Grayscale model_gray = create_model((64,64,1)) history_gray = model_gray.fit(X_train_gray, y_train, epochs=5, batch_size=16, validation_data=(X_val_gray, y_val), verbose=0) # Train and evaluate on HSV model_hsv = create_model((64,64,3)) history_hsv = model_hsv.fit(X_train_hsv, y_train, epochs=5, batch_size=16, validation_data=(X_val_hsv, y_val), verbose=0) # Collect final validation accuracies val_acc_rgb = history_rgb.history['val_accuracy'][-1] * 100 val_acc_gray = history_gray.history['val_accuracy'][-1] * 100 val_acc_hsv = history_hsv.history['val_accuracy'][-1] * 100 print(f"Validation Accuracy RGB: {val_acc_rgb:.2f}%") print(f"Validation Accuracy Grayscale: {val_acc_gray:.2f}%") print(f"Validation Accuracy HSV: {val_acc_hsv:.2f}%")
Before: Model trained only on RGB images without color space conversion.
After: Model trained separately on RGB, grayscale, and HSV images with color space conversion.
import cv2
img = cv2.imread('image.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)