# Suppress warnings to keep our output clean
import warnings; warnings.filterwarnings("ignore")
import numpy as np
np.random.seed(42)
 

# Data setup
import cv2
import numpy as np
img = np.zeros((200, 200, 3), dtype=np.uint8)
cv2.circle(img, (50, 50), 20, (255, 255, 255), -1)
cv2.rectangle(img, (120, 30), (170, 80), (255, 255, 255), -1)
print(img.shape)

# Show the image using matplotlib
import matplotlib.pyplot as plt
plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
plt.title("Synthetic Objects Image")
plt.axis("off")
plt.show()

# Convert the image to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# Show the grayscale image
plt.imshow(gray, cmap="gray")
plt.title("Grayscale Image")
plt.axis("off")
plt.show()

# Apply thresholding to get a binary image
ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)

# Show the thresholded image
plt.imshow(thresh, cmap="gray")
plt.title("Thresholded Image")
plt.axis("off")
plt.show()

# Find contours in the thresholded image
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
print("Number of contours found:", len(contours))

# Draw contours on a copy of the image for visualization
img_with_contours = img.copy()
cv2.drawContours(img_with_contours, contours, -1, (0,255,0), 3)
plt.imshow(cv2.cvtColor(img_with_contours, cv2.COLOR_BGR2RGB))
plt.title("Image with Contours")
plt.axis("off")
plt.show()

# Print the total number of objects found
num_objects = len(contours)
print("Number of objects detected:", num_objects)

# Try it yourself: Add another shape and count again
# This will draw an ellipse as a third object
img_more = np.zeros((200, 200, 3), dtype=np.uint8)
cv2.circle(img_more, (50, 50), 20, (255,255,255), -1)
cv2.rectangle(img_more, (120,30), (170,80), (255,255,255), -1)
cv2.ellipse(img_more, (150,150), (20,10), 0, 0, 360, (255,255,255), -1)
gray_more = cv2.cvtColor(img_more, cv2.COLOR_BGR2GRAY)
ret, thresh_more = cv2.threshold(gray_more, 127, 255, cv2.THRESH_BINARY)
contours_more, _ = cv2.findContours(thresh_more, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
num_objects_more = len(contours_more)
print("After adding, number of objects detected:", num_objects_more)

# Challenge: Try With Your Own Images
path = input("Enter a path to your own image file, or leave blank to skip: ")
if path.strip():
    user_img = cv2.imread(path)
    if user_img is None:
        print("Sorry, could not read the image. Check the path or file type.")
    else:
        gray_u = cv2.cvtColor(user_img, cv2.COLOR_BGR2GRAY)
        _, thresh_u = cv2.threshold(gray_u, 127, 255, cv2.THRESH_BINARY)
        contours_u, _ = cv2.findContours(thresh_u, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        print("Objects detected in your image:", len(contours_u))
        plt.imshow(cv2.cvtColor(user_img, cv2.COLOR_BGR2RGB))
        plt.title("Your Image")
        plt.axis("off")
        plt.show()
else:
    print("Skipped. You can try with your own images later.")