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b6aa8ff | 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 | import cv2
import numpy as np
from tensorflow.keras.models import load_model
# 🔹 Load trained model
model = load_model("my_image_model.h5")
# 🔹 Your class labels
class_indices = {"Gir_cow": 0, "Murrah_buffalo": 1, "Red_Sindhi_cow": 2, "Sahiwal_cow": 3, "Tharparkar_cow": 4, "amritmahal_cow": 5, "banni_buffalo": 6, "bhadwari_buffalo": 7, "dharwadi_buffalo": 8, "jafarabadi_buffalo": 9}# Update with your classes
# 🔹 Preprocessing function
def preprocess_frame(frame, target_size=(224, 224)):
img = cv2.resize(frame, target_size) # Resize
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # BGR -> RGB
img = img.astype("float32") / 255.0 # Normalize
img = np.expand_dims(img, axis=0) # Add batch dimension
return img
# 🔹 Start webcam
cap = cv2.VideoCapture(0)
while True:
ret, frame = cap.read()
if not ret:
break
h, w, _ = frame.shape
# Define square region (centered)
size = min(h, w) // 2
x1 = w // 2 - size // 2
y1 = h // 2 - size // 2
x2 = w // 2 + size // 2
y2 = h // 2 + size // 2
# Draw square
cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
# Extract ROI inside square
roi = frame[y1:y2, x1:x2]
# Preprocess ROI
processed = preprocess_frame(roi, target_size=(224, 224))
# Predict
preds = model.predict(processed)
class_id = np.argmax(preds, axis=1)[0]
confidence = np.max(preds)
# Label
label = class_indices.get(class_id, str(class_id))
text = f"{label} ({confidence:.2f})"
# Put label inside square (centered)
text_size = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, 0.9, 2)[0]
text_x = x1 + (size - text_size[0]) // 2
text_y = y1 + (size + text_size[1]) // 2
cv2.putText(frame, text, (text_x, text_y),
cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2, cv2.LINE_AA)
# Show frame
cv2.imshow("Detection with Square ROI", frame)
# Quit with 'q'
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
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