File size: 3,657 Bytes
8535875 678fc41 8535875 678fc41 8535875 678fc41 8535875 678fc41 8535875 678fc41 8535875 678fc41 8535875 678fc41 8535875 0f519d6 8535875 0f519d6 8535875 678fc41 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 |
import gradio as gr
import numpy as np
import cv2
def create_dot_effect(image, dot_size=10, spacing=2):
# Convert to grayscale if image is color
if len(image.shape) == 3:
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
else:
gray = image
# Create a blank canvas
height, width = gray.shape
canvas = np.zeros_like(gray)
# Calculate number of dots based on spacing
y_dots = range(0, height, dot_size + spacing)
x_dots = range(0, width, dot_size + spacing)
# Create dots based on brightness
for y in y_dots:
for x in x_dots:
# Get the average brightness of the region
region = gray[y:min(y+dot_size, height), x:min(x+dot_size, width)]
if region.size > 0:
brightness = np.mean(region)
# Draw circle if the region is bright enough
if brightness > 30: # Threshold can be adjusted
cv2.circle(canvas,
(x + dot_size//2, y + dot_size//2),
dot_size//2,
(255),
-1)
return canvas
def process_video(video_path, dot_size=10, spacing=2):
# Read the video
cap = cv2.VideoCapture(video_path)
if not cap.isOpened():
return None
# Get video properties
fps = int(cap.get(cv2.CAP_PROP_FPS))
frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
# Create temporary output file with mp4v codec
output_path = "temp_output.mp4"
fourcc = cv2.VideoWriter_fourcc(*'avc1') # Changed from 'mp4v' to 'avc1' (h264 codec)
out = cv2.VideoWriter(output_path, fourcc, fps, (frame_width, frame_height), False)
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
# Convert BGR to RGB for processing
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# Apply dot effect
dotted_frame = create_dot_effect(frame_rgb, dot_size, spacing)
out.write(dotted_frame)
cap.release()
out.release()
return output_path
# Create Gradio interface
with gr.Blocks(title="ChatGPT Ad Maker") as iface:
gr.Markdown("# ChatGPT Ad Maker")
gr.Markdown("Convert your image or video into a dotted pattern. Adjust dot size and spacing using the sliders.")
with gr.Tab("Image"):
image_input = gr.Image(label="Input Image")
with gr.Row():
img_dot_size = gr.Slider(minimum=2, maximum=20, value=10, step=1, label="Dot Size")
img_spacing = gr.Slider(minimum=0, maximum=10, value=2, step=1, label="Dot Spacing")
image_output = gr.Image(label="Dotted Output")
image_button = gr.Button("Process Image")
image_button.click(
fn=create_dot_effect,
inputs=[image_input, img_dot_size, img_spacing],
outputs=image_output
)
with gr.Tab("Video"):
video_input = gr.Video(label="Input Video")
with gr.Row():
vid_dot_size = gr.Slider(minimum=2, maximum=20, value=10, step=1, label="Dot Size")
vid_spacing = gr.Slider(minimum=0, maximum=10, value=2, step=1, label="Dot Spacing")
video_output = gr.Video(label="Dotted Output", format="mp4")
video_button = gr.Button("Process Video")
video_button.click(
fn=process_video,
inputs=[video_input, vid_dot_size, vid_spacing],
outputs=video_output
)
if __name__ == "__main__":
iface.launch()
|