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Running
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Zero
File size: 5,200 Bytes
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import spaces
import gradio as gr
import os
import torch
import matplotlib.pyplot as plt
import numpy as np
import matplotlib
from PIL import Image
from transformers import AutoModelForCausalLM
matplotlib.use("Agg") # Use Agg backend for non-interactive plotting
os.environ["HF_TOKEN"] = os.environ.get("TOKEN_FROM_SECRET") or True
model = AutoModelForCausalLM.from_pretrained(
"vikhyatk/moondream-next",
trust_remote_code=True,
torch_dtype=torch.float16,
device_map={"": "cuda"},
revision="69420e0c6596863b4f0059e365fadc5cb388e8fd"
)
def visualize_gaze_multi(face_boxes, gaze_points, image=None, show_plot=True):
"""Visualization function with reduced whitespace"""
# Calculate figure size based on image aspect ratio
if image is not None:
height, width = image.shape[:2]
aspect_ratio = width / height
fig_height = 6 # Base height
fig_width = fig_height * aspect_ratio
else:
width, height = 800, 600
fig_width, fig_height = 10, 8
# Create figure with tight layout
fig = plt.figure(figsize=(fig_width, fig_height))
ax = fig.add_subplot(111)
if image is not None:
ax.imshow(image)
else:
ax.set_facecolor("#1a1a1a")
fig.patch.set_facecolor("#1a1a1a")
colors = plt.cm.rainbow(np.linspace(0, 1, len(face_boxes)))
for face_box, gaze_point, color in zip(face_boxes, gaze_points, colors):
hex_color = "#{:02x}{:02x}{:02x}".format(
int(color[0] * 255), int(color[1] * 255), int(color[2] * 255)
)
x, y, width_box, height_box = face_box
gaze_x, gaze_y = gaze_point
face_center_x = x + width_box / 2
face_center_y = y + height_box / 2
face_rect = plt.Rectangle(
(x, y), width_box, height_box, fill=False, color=hex_color, linewidth=2
)
ax.add_patch(face_rect)
points = 50
alphas = np.linspace(0.8, 0, points)
x_points = np.linspace(face_center_x, gaze_x, points)
y_points = np.linspace(face_center_y, gaze_y, points)
for i in range(points - 1):
ax.plot(
[x_points[i], x_points[i + 1]],
[y_points[i], y_points[i + 1]],
color=hex_color,
alpha=alphas[i],
linewidth=4,
)
ax.scatter(gaze_x, gaze_y, color=hex_color, s=100, zorder=5)
ax.scatter(gaze_x, gaze_y, color="white", s=50, zorder=6)
# Set plot limits and remove axes
ax.set_xlim(0, width)
ax.set_ylim(height, 0)
ax.set_aspect("equal")
ax.set_xticks([])
ax.set_yticks([])
# Remove padding around the plot
plt.subplots_adjust(left=0, right=1, bottom=0, top=1)
return fig
@spaces.GPU(duration=15)
def process_image(input_image):
try:
# Convert to PIL Image if needed
if isinstance(input_image, np.ndarray):
pil_image = Image.fromarray(input_image)
else:
pil_image = input_image
# Get image encoding
enc_image = model.encode_image(pil_image)
# Detect faces
faces = model.detect(enc_image, "face")["objects"]
if not faces:
return None, "No faces detected in the image."
# Process each face
face_boxes = []
gaze_points = []
for face in faces:
face_center = (
(face["x_min"] + face["x_max"]) / 2,
(face["y_min"] + face["y_max"]) / 2,
)
gaze = model.detect_gaze(enc_image, face_center)
if gaze is None:
continue
face_box = (
face["x_min"] * pil_image.width,
face["y_min"] * pil_image.height,
(face["x_max"] - face["x_min"]) * pil_image.width,
(face["y_max"] - face["y_min"]) * pil_image.height,
)
gaze_point = (
gaze["x"] * pil_image.width,
gaze["y"] * pil_image.height,
)
face_boxes.append(face_box)
gaze_points.append(gaze_point)
# Create visualization
image_array = np.array(pil_image)
fig = visualize_gaze_multi(
face_boxes, gaze_points, image=image_array, show_plot=False
)
return fig, f"Detected {len(faces)} faces."
except Exception as e:
return None, f"Error processing image: {str(e)}"
with gr.Blocks(title="Moondream Gaze Detection") as app:
gr.Markdown("# 🌔 Moondream Gaze Detection")
gr.Markdown("Upload an image to detect faces and visualize their gaze directions.")
with gr.Row():
with gr.Column():
input_image = gr.Image(label="Input Image", type="pil")
with gr.Column():
output_text = gr.Textbox(label="Status")
output_plot = gr.Plot(label="Visualization")
input_image.change(
fn=process_image, inputs=[input_image], outputs=[output_plot, output_text]
)
gr.Examples(
examples=["demo1.jpg", "demo2.jpg", "demo3.jpg", "demo4.jpg", "demo5.jpg"],
inputs=input_image,
)
if __name__ == "__main__":
app.launch()
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