Merge pull request #424 from adityagandhamal/facial-expressions-detection

Add facial expressions detection application

Author: AdityaJ7

Scripts/Miscellaneous/Facial Expressions Detection/README.md

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+# Facial expressions detection
+This project detects facial expressions of humans using Deep Learning and Computer Vision with the help of Python.
+The facial features for detection are extracted using [OpenCV](https://github.com/opencv/opencv) with the help of the Haar Cascade Classifier `haarcascade_frontalface_default.xml`. You can downlaod the .xml file [here.](https://github.com/opencv/opencv/blob/master/data/haarcascades/haarcascade_frontalface_default.xml)
+
+### Prerequisites
+
+  - #### TensorFlow 2.0
+  
+  - #### Keras
+  
+  - #### OpenCV
+  
+  - #### Python>=3.7
+  
+  - #### Numpy
+
+
+### Here's what you can do to use this project to detect your facial expressions using the webcam:
+  
+  1. [Clone](https://docs.github.com/en/github/creating-cloning-and-archiving-repositories/cloning-a-repository) this repo or download the zip folder.
+
+  2. Go to the directory of this repo cloned or downloaded onto your local machine and open cmd.
+
+  3. Install the prerequisites by running `pip install requirements.txt` in the cmd.
+
+  4. Run `detection_on_vid.py` file and try making different expressions like Happy, Sad, Angry and Surprised faces and notice the detections made. 
+
+
+### Here's a snippet
+
+![expression_detection](https://user-images.githubusercontent.com/61016383/94587769-afa85680-02a0-11eb-897f-ffe8d88becbe.gif)
+
+
+## *Author Name*
+ADITYA GANDHAMAL

Scripts/Miscellaneous/Facial Expressions Detection/detection_model.h5

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+
+# IMPORTS
+
+import cv2
+import numpy as np
+from tensorflow.keras.preprocessing import image
+from tensorflow.keras.models import load_model
+
+
+# LOADING THE MODEL
+
+model = load_model("detection_model.h5")
+
+
+
+
+def face_extraction(frame):
+
+    ''' Detect faces in a frame and extract them '''
+
+    faces = cascade_model.detectMultiScale(frame, 1.1, 5)
+
+    for x, y, w, h in faces:
+        cropped_img = frame[y:y+h, x:x+w]
+
+    return cropped_img
+    
+
+
+
+
+def image_processing(frame):
+
+    ''' Preprocessing of the image for predictions ''' 
+    
+    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    frame = cv2.resize(frame, (48, 48))
+    frame = image.img_to_array(frame)
+    frame = frame/255
+    frame = np.expand_dims(frame, axis=0)
+
+    return frame
+
+
+
+
+
+def detect_expressions(frame, detection_model):
+
+    ''' Detect final expressions and return the predictions
+        done by the detection_model '''
+
+    cropped_frame = face_extraction(frame)
+
+    test_frame = image_processing(cropped_frame)
+
+    prediction = np.argmax(model.predict(test_frame), axis=-1)
+
+    return prediction
+
+
+
+
+# LOAD IMAGE
+
+img = cv2.imread("./test_images/Swift2.jpg")
+
+
+
+# LOADING HAAR CASCADE CLASSIFIER
+
+cascade_model = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
+faces = cascade_model.detectMultiScale(img, 1.1, 10)        
+
+
+
+font = cv2.FONT_ITALIC
+for x, y, w, h in faces:
+    cv2.rectangle(img, (x, y), (x+w, y+h), (0,255,0), 2)
+    
+    prediction = detect_expressions(img, model)
+    
+    if prediction == [0]:
+        cv2.putText(img, "Angry", (x, y), font, 2, (0, 0, 255), 2)
+
+    elif prediction == [1]:
+        cv2.putText(img, "Happy", (x, y), font, 2, (0, 0, 255), 2)
+
+    elif prediction == [2]:
+        cv2.putText(img, "Sad", (x, y), font, 2, (0, 0, 255), 2)
+
+    else:
+        cv2.putText(img, "Surprised", (x, y), font, 2, (0, 0, 255), 2)
+
+
+    cv2.imshow("img", img)
+
+
+# Cleaning
+
+cv2.waitKey(0)
+cv2.destroyAllWindows()

Scripts/Miscellaneous/Facial Expressions Detection/detection_on_img.py

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+# IMPORTS
+
+import cv2
+import numpy as np
+from tensorflow.keras.preprocessing import image
+from tensorflow.keras.models import load_model
+
+
+# LOADING THE MODEL
+
+model = load_model("detection_model.h5")
+
+
+
+
+def face_extraction(frame):
+
+    ''' Detect faces in a frame and extract them '''
+
+    faces = cascade_model.detectMultiScale(frame, 1.1, 5)
+
+    for x, y, w, h in faces:
+        cropped_img = frame[y:y+h, x:x+w]
+
+    return cropped_img
+
+
+
+
+
+def image_processing(frame):
+
+    ''' Preprocessing of the image for predictions '''
+    
+    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    frame = cv2.resize(frame, (48, 48))
+    frame = image.img_to_array(frame)
+    frame = frame/255
+    frame = np.expand_dims(frame, axis=0)
+
+    return frame
+
+
+
+
+def detect_expressions(frame, detection_model):
+
+    ''' Detect final expressions and return the predictions
+        done by the detection_model '''
+
+    cropped_frame = face_extraction(frame)
+
+    test_frame = image_processing(cropped_frame)
+
+    prediction = np.argmax(model.predict(test_frame), axis=-1)
+
+    return prediction
+
+
+
+
+# LOADING HAAR CASCADE CLASSIFIER
+
+cascade_model = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
+
+
+
+
+# CAPTURE VIDEO (ON WEBCAM)
+
+cap = cv2.VideoCapture(0)
+
+while cap.isOpened():
+    
+    _, frame = cap.read()
+
+  
+    try:
+
+        faces = cascade_model.detectMultiScale(frame, 1.1, 5)   # Detect faces in a frame
+        
+        for x, y, w, h in faces:
+
+            cv2.rectangle(frame, (x, y), (x+w, y+h), (0,255,0), 2)    
+            
+            prediction = detect_expressions(frame, model)
+            
+            font = cv2.FONT_ITALIC
+
+            if prediction == [0]:
+                cv2.putText(frame, "Angry", (x, y), font, 1, (0, 0, 255), 2)
+
+            elif prediction == [1]:
+                cv2.putText(frame, "Happy", (x, y), font, 1, (0, 0, 255), 2)
+
+            elif prediction == [2]:
+                cv2.putText(frame, "Sad", (x, y), font, 1, (0, 0, 255), 2)
+
+            else:
+                cv2.putText(frame, "Surprised", (x, y), font, 1, (0, 0, 255), 2)
+
+        
+            cv2.imshow("frame", frame)
+        
+
+        if cv2.waitKey(1) & 0xFF == 27:
+            break
+
+    except Exception:
+        break
+        
+
+# Cleaning
+cap.release()        
+cv2.destroyAllWindows()