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Testemotion.py
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Testemotion.py
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# import cv2
# import numpy as np
# from keras.models import model_from_json
# emotion_dict = {0: "Angry", 1: "Disgusted", 2: "Fearful", 3: "Happy", 4: "Neutral", 5: "Sad", 6: "Surprised"}
# # load json and create model
# json_file = open('emotion_model.json', 'r')
# loaded_model_json = json_file.read()
# json_file.close()
# emotion_model = model_from_json(loaded_model_json)
# # load weights into new model
# emotion_model.load_weights("emotion_model.h5")
# print("Loaded model from disk")
# # start the webcam feed
# #cap = cv2.VideoCapture(0)
# # pass here your video path
# # you may download one from here : https://www.pexels.com/video/three-girls-laughing-5273028/
# cap = cv2.VideoCapture(r"C:\Users\Admin\emotiontrain\WIN_20240630_00_58_04_Pro.mp4")
# while True:
# # Find haar cascade to draw bounding box around face
# ret, frame = cap.read()
# frame = cv2.resize(frame, (1280, 720))
# if not ret:
# break
# face_detector = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
# gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# # detect faces available on camera
# num_faces = face_detector.detectMultiScale(gray_frame, scaleFactor=1.3, minNeighbors=5)
# # take each face available on the camera and Preprocess it
# for (x, y, w, h) in num_faces:
# cv2.rectangle(frame, (x, y-50), (x+w, y+h+10), (0, 255, 0), 4)
# roi_gray_frame = gray_frame[y:y + h, x:x + w]
# cropped_img = np.expand_dims(np.expand_dims(cv2.resize(roi_gray_frame, (48, 48)), -1), 0)
# # predict the emotions
# emotion_prediction = emotion_model.predict(cropped_img)
# maxindex = int(np.argmax(emotion_prediction))
# cv2.putText(frame, emotion_dict[maxindex], (x+5, y-20), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2, cv2.LINE_AA)
# cv2.imshow('Emotion Detection', frame)
# if cv2.waitKey(1) & 0xFF == ord('q'):
# break
# cap.release()
# cv2.destroyAllWindows()
import cv2
import numpy as np
from keras.models import model_from_json
emotion_dict = {0: "Angry", 1: "Disgusted", 2: "Fearful", 3: "Happy", 4: "Neutral", 5: "Sad", 6: "Surprised"}
# Load json and create model
json_file = open('emotion_model.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
emotion_model = model_from_json(loaded_model_json)
# Load weights into new model
emotion_model.load_weights("final_train.h5")
print("Loaded model from disk")
# Start the webcam feed
cap = cv2.VideoCapture(0)
# Pass here your video path
#cap = cv2.VideoCapture(r"C:\Users\Admin\emotiontrain\WIN_20240630_00_58_04_Pro.mp4")
while True:
# Find Haar cascade to draw bounding box around face
ret, frame = cap.read()
frame = cv2.resize(frame, (1280, 720))
if not ret:
break
face_detector = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# Detect faces available on camera
num_faces = face_detector.detectMultiScale(gray_frame, scaleFactor=1.3, minNeighbors=5)
# Take each face available on the camera and preprocess it
for (x, y, w, h) in num_faces:
# Adjust the rectangle to make it slightly larger
padding = 20
cv2.rectangle(frame, (x - padding, y - 40 - padding), (x + w + padding, y + h + 10 + padding), (0, 255, 0), 4)
roi_gray_frame = gray_frame[y:y + h, x:x + w]
cropped_img = np.expand_dims(np.expand_dims(cv2.resize(roi_gray_frame, (48, 48)), -1), 0)
# Predict the emotions
emotion_prediction = emotion_model.predict(cropped_img)
maxindex = int(np.argmax(emotion_prediction))
# Adjust the text position to come out of the box
cv2.putText(frame, emotion_dict[maxindex], (x + 5, y - 80), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2, cv2.LINE_AA)
cv2.imshow('Emotion Detection', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
# import cv2
# import numpy as np
# from keras.models import model_from_json
# # Load the emotion recognition model
# emotion_dict = {0: "Angry", 1: "Disgusted", 2: "Fearful", 3: "Happy", 4: "Neutral", 5: "Sad", 6: "Surprised"}
# # Load json and create model
# json_file = open('emotion_model.json', 'r')
# loaded_model_json = json_file.read()
# json_file.close()
# emotion_model = model_from_json(loaded_model_json)
# # Load weights into new model
# emotion_model.load_weights("emotion_model.h5")
# print("Loaded model from disk")
# # Start the webcam feed or video file
# # cap = cv2.VideoCapture(0) # For webcam
# cap = cv2.VideoCapture(r"C:\Users\Admin\emotiontrain\WIN_20240630_00_58_04_Pro.mp4") # For video file
# while True:
# # Read frame from video
# ret, frame = cap.read()
# if not ret:
# break
# frame = cv2.resize(frame, (640, 360)) # Resize for display
# # Convert frame to grayscale for face detection
# gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# face_detector = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
# # Detect faces in the frame
# num_faces = face_detector.detectMultiScale(gray_frame, scaleFactor=1.3, minNeighbors=5)
# # Create a copy of the frame for segmentation display
# segmented_frame = frame.copy()
# # Process each detected face
# for (x, y, w, h) in num_faces:
# padding = 20
# # Draw rectangle on the original frame
# cv2.rectangle(frame, (x - padding, y - 40 - padding), (x + w + padding, y + h + 10 + padding), (0, 255, 0), 2)
# roi_gray_frame = gray_frame[y:y + h, x:x + w]
# cropped_img = np.expand_dims(np.expand_dims(cv2.resize(roi_gray_frame, (48, 48)), -1), 0)
# # Predict emotion
# emotion_prediction = emotion_model.predict(cropped_img)
# maxindex = int(np.argmax(emotion_prediction))
# emotion_label = emotion_dict[maxindex]
# # Draw emotion label on both frames
# cv2.putText(frame, emotion_label, (x + 5, y - 80), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2, cv2.LINE_AA)
# cv2.putText(segmented_frame, emotion_label, (x + 5, y - 80), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2, cv2.LINE_AA)
# # Segment the detected face region
# face_region = segmented_frame[y:y+h, x:x+w]
# lab_face_region = cv2.cvtColor(face_region, cv2.COLOR_BGR2LAB)
# pixel_values = lab_face_region.reshape((-1, 3))
# pixel_values = np.float32(pixel_values)
# # K-means clustering
# criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.2)
# k = 3 # Number of clusters
# _, labels, (centers) = cv2.kmeans(pixel_values, k, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)
# centers = np.uint8(centers)
# segmented_face_region = centers[labels.flatten()]
# segmented_face_region = segmented_face_region.reshape(lab_face_region.shape)
# segmented_face_region_bgr = cv2.cvtColor(segmented_face_region, cv2.COLOR_LAB2BGR)
# # Replace the original face region with the segmented face region
# segmented_frame[y:y+h, x:x+w] = segmented_face_region_bgr
# # Display the results in two separate windows
# cv2.imshow('Original Emotion Detection', frame)
# cv2.imshow('Segmented Emotion Detection', segmented_frame)
# # Position the windows next to each other
# cv2.moveWindow('Original Emotion Detection', 0, 0)
# cv2.moveWindow('Segmented Emotion Detection', 650, 0) # Adjust the x-coordinate for positioning
# if cv2.waitKey(1) & 0xFF == ord('q'):
# break
# cap.release()
# cv2.destroyAllWindows()