computer vision project

README.md

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+# Project for the subject of computer vision
+
+## Task
+
+The goal of the project is to create a system that recognizes emotions in real time.
+
+## Start system
+
+- unpack input.zip file
+- <code>python project.py</code>
+
+## Learn and use model
+- go to Google Colab
+- import and run `modelTrainer.ipynb` file
+- copy `model.json` and `model_weights.h5` to `input` dir
+- run: <code>python project.py</code>

project.py

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+import numpy as np
+import cv2
+from tensorflow.keras.models import model_from_json
+
+model_json_file = './input/model.json'
+model_weights_file = './input/model_weights.h5'
+with open(model_json_file, "r") as json_file:
+    loaded_model_json = json_file.read()
+    loaded_model = model_from_json(loaded_model_json)
+    loaded_model.load_weights(model_weights_file)
+
+face_cascade = cv2.CascadeClassifier('./input/haarcascade_frontalface_default.xml')
+img_size = 64
+cap = cv2.VideoCapture(0)
+import copy
+
+while True:
+    
+    ret, frame = cap.read()
+    img = copy.deepcopy(frame)
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
+    for (x,y,w,h) in faces:
+        fc = gray[y:y+h, x:x+w]
+        
+        roi = cv2.resize(fc, (img_size,img_size))
+        pred = loaded_model.predict(roi[np.newaxis, :, :, np.newaxis])
+        text_idx=np.argmax(pred)
+        text_list = ['Angry', 'Disgust', 'Fear', 'Happy', 'Neutral', 'Sad', 'Surprise']
+        if text_idx == 0:
+            text= text_list[0]
+        if text_idx == 1:
+            text= text_list[1]
+        elif text_idx == 2:
+            text= text_list[2]
+        elif text_idx == 3:
+            text= text_list[3]
+        elif text_idx == 4:
+            text= text_list[4]
+        elif text_idx == 5:
+            text= text_list[5]
+        elif text_idx == 6:
+            text= text_list[6]
+        cv2.putText(img, text, (x, y-5),
+           cv2.FONT_HERSHEY_SIMPLEX, 0.45, (255, 0, 255), 2)
+        img = cv2.rectangle(img, (x,y), (x+w, y+h), (0,0,255), 2)
+            
+    
+    cv2.imshow("frame", img)
+    key = cv2.waitKey(1) & 0xFF
+    if key== ord('q'):
+        break
+    
+cap.release()
+cv2.destroyAllWindows()