run_face_detect.py

# read the saved model
def setup_hard_hat_detector():
    return load_model("res/vgg16_model/ninety2_percent_just_vgg16.h5")
hh_model=setup_hard_hat_detector()

location="raw_yolo_keras/end_end_test"
#location where the images downloaded from the internet are placed

dim=(76,76)

head_taller_parameter=0.5
chin_taller_parameter=0.1
# parameters to extend the cropped faces

file_list=os.listdir(location)
for file in file_list:
    frame=cv2.imread(os.path.join(location,file))                
    (h, w) = frame.shape[:2]
    # Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
    image = frame[:, :, ::-1]
    face_locations = face_recognition.face_locations(image)
    all_wearing_hats=True
    colors=[]
    for f_loc in face_locations:
        startY, endX, endY, startX=f_loc
        height=endY-startY
        #increase height by some %
        taller_head=int(head_taller_parameter*height)
        taller_chin=int(chin_taller_parameter*height)
        startY=max(0,startY-taller_head)
        endY=min(h,endY+taller_chin)
        cropped_image = frame[startY:endY, startX:endX]
        resized = cv2.resize(cropped_image, dim, interpolation = cv2.INTER_AREA)
        lst=np.array([resized])
        preds = hh_model.predict(lst)
        all_wearing_hats=all_wearing_hats&(preds[0][1]>0.5)
        if preds[0][1]>0.5:
            colors.append((0,255,0))
        else:
            colors.append((255,0,0))

    for i in range(len(face_locations)):
        startY, endX, endY, startX=face_locations[i]
        color=colors[i]
        cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)
    plt.imshow(frame)
    plt.show()