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from fastai.vision.all import *
import gradio as gr
import cv2
learn = load_learner("model.pkl")
print("vocab", learn.dls.vocab)
def preprocess_webcam_image(img_array):
# Convert to grayscale
gray = cv2.cvtColor(img_array, cv2.COLOR_BGR2GRAY)
# Face detection using Haar cascades
face_cascade = cv2.CascadeClassifier(
cv2.data.haarcascades + "haarcascade_frontalface_default.xml"
)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
# If no face is detected, return the original image (or handle this case appropriately)
if len(faces) == 0:
print("No face detected")
return gray / 255.0
# Assuming only one face is detected, crop it
x, y, w, h = faces[0]
cropped_face = gray[y : y + h, x : x + w]
# Resize to match training data dimensions (e.g., 224x224)
resized_face = cv2.resize(cropped_face, (224, 224))
# Normalize if necessary (e.g., scale pixel values to [0, 1])
normalized_face = resized_face / 255.0
# Convert back to a 3-channel image
processed_img_rgb = np.stack([normalized_face] * 3, axis=-1)
processed_img_rgb = (processed_img_rgb * 255).astype(np.uint8)
return processed_img_rgb
def classify_img(img):
processed_img = preprocess_webcam_image(img)
pred, idx, probs = learn.predict(processed_img)
parsed_probs = dict(zip(learn.dls.vocab, map(float, probs)))
# Debugging print statement
print(parsed_probs)
# Return both the prediction dictionary and the processed image
return (
processed_img,
parsed_probs,
)
# image = gr.inputs.Image(shape=(192, 192))
image = gr.Image(source="webcam", shape=(224, 224), streaming=True, interactive=True)
label = gr.outputs.Label()
examples = ["angry.jpg", "happy.jpg"]
iface = gr.Interface(
fn=classify_img,
inputs=image,
outputs=[
gr.outputs.Image(type="numpy", label="Processed Image").style(height=224),
gr.outputs.Label(),
],
examples=examples,
)
iface.dependencies[0]["show_progress"] = False
iface.launch(inline=False)
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