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| import cv2 | |
| from matplotlib.pyplot import hsv | |
| from PIL import ImageOps | |
| import numpy as np | |
| import gradio as gr | |
| import warnings | |
| # Filter out user warnings to avoid any unnecessary clutter in the output | |
| warnings.filterwarnings("ignore", category=UserWarning) | |
| font = cv2.FONT_HERSHEY_SIMPLEX | |
| font_scale = 1 | |
| color_search = np.zeros((200, 200, 3), np.uint8) | |
| color_selected = np.zeros((200, 200, 3), np.uint8) | |
| hue = 0 | |
| def search_contours(mask, frame, source): | |
| contours_count = 0 | |
| contours, hierarchy = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) | |
| pip_count = 0 | |
| for contour in contours: | |
| area = cv2.contourArea(contour) | |
| if 200 < area < 10000: | |
| cv2.drawContours(frame, [contour], -1, (0, 255, 0), 2) | |
| contours_count += 1 | |
| M = cv2.moments(contour) | |
| if M["m00"] != 0: | |
| cX = int(M["m10"] / M["m00"]) | |
| cY = int(M["m01"] / M["m00"]) | |
| else: | |
| cX, cY = 0, 0 | |
| cv2.circle(frame, (cX, cY), 3, (255, 255, 255), -1) | |
| pip_count += 1 | |
| cv2.putText(frame, str(pip_count), (cX - 16, cY + 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1) | |
| return pip_count, frame | |
| def center_crop_with_padding(image): | |
| im = image.fromarray(image) | |
| width, height = im.size | |
| new_size = min(width, height) | |
| im = im.crop(((width - new_size) // 2, (height - new_size) // 2, (width + new_size) // 2, (height + new_size) // 2)) | |
| im = im.crop((25, 25, new_size - 25, new_size - 25)) | |
| im_with_border = ImageOps.expand(im, border=3, fill=(255,255,255)) | |
| return np.array(im_with_border) | |
| sample_images = { | |
| "23.jpg": "23.jpg", | |
| "28.png": "28.png", | |
| "35.jpg": "35.jpg", | |
| "36w.jpg": "36w.jpg", | |
| "46.jpg": "46.jpg", | |
| "64.jpg": "64.jpg", | |
| "86.jpg": "86.jpg", | |
| } | |
| def detect_pips(uploaded_image, sample_image_selection, hue_threshold): | |
| if uploaded_image is not None: | |
| image = uploaded_image[:, :, :3] | |
| else: | |
| image = cv2.imread(sample_image_selection) | |
| image = cv2.resize(image, (512, 512)) | |
| hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) | |
| hue = 60 | |
| lower_hue, upper_hue = max(0, hue - hue_threshold), min(179, hue + hue_threshold) | |
| lower_hsv = np.array([lower_hue, 50, 20]) | |
| upper_hsv = np.array([upper_hue, 255, 255]) | |
| mask = cv2.inRange(hsv, lower_hsv, upper_hsv) | |
| count, result_frame = search_contours(mask, image, source="image") | |
| cv2.putText(result_frame, f'Total pip count is: {count}', (10, result_frame.shape[0] - 30), font, 1, (0, 255, 255), 2, cv2.LINE_AA) | |
| return result_frame | |
| iface = gr.Interface( | |
| detect_pips, | |
| inputs=[ | |
| gr.inputs.Image(type="numpy", label="Upload your Domino Image (jpg or png)", source="upload", optional=True), # Image upload | |
| gr.inputs.Dropdown(choices=list(sample_images.keys()), label="Use 'Clear' to remove any image above and select an example from the drop down and 'Submit' it", default=None), # Dropdown for sample images | |
| gr.inputs.Slider(label="Hue Threshold - Use this to adjust the sensitvity on what it counts", minimum=0, maximum=500, step=1, default=250) | |
| ], | |
| outputs=gr.outputs.Image(type="numpy", label="Result"), | |
| title='<b><font size="4">🁫Image Processing demonstration using OpenCV and Python:🁫</b>', | |
| description='<div style="text-align:center;"><b style="font-size:20px;">App to help you keep score when playing Dominos</b><br>Use your iPad to take a photo and upload to calculate a score<br> Please leave a ❤️ if you enjoyed this :)</div>', | |
| allow_flagging=False, | |
| live=False, | |
| theme="dark", | |
| ) | |
| # Launch the Gradio interface if the script is run as the main program | |
| if __name__ == '__main__': | |
| iface.launch() | |