.gitignore

@@ -1,3 +1,2 @@
 venv/
 *.pyc
-saves/

face_recognition/comparison.py

@@ -5,26 +5,16 @@ import logging
 import argparse
 import numpy as np
 import time
-import datetime
 from PIL import ImageFont, ImageDraw, Image
 import os
 
 draw_colors = {
-    'hog': (198,65,124),
+    'hog': (255,0,0),
-    'haar': (255,255,255),
+    'haar': (0,255,0),
-    'dnn': (251,212,36),
+    'dnn': (0,0,255),
 }
 
-titles = {
+font = ImageFont.truetype("/home/ruben/Documents/Projecten/2018/PATH/presentation/lib/font/source-sans-pro/source-sans-pro-regular.ttf", 30)
-    'hog' : "Histogram of oriented gradients",
-    'haar' : "Haar cascades",
-    'dnn' : "Neural network",
-}
-
-fontfile = "SourceSansPro-Regular.ttf"
-
-font = ImageFont.truetype(fontfile, 30)
-font_s = ImageFont.truetype(fontfile, 20)
 
 class Result():
     def __init__(self, algorithm, image, confidence_threshold = 0.5):
@@ -43,27 +33,18 @@ class Result():
         })
         return self
     
-    def draw_detections(self, include_title = False):
+    def draw_detections(self):
+        color = draw_colors[self.algorithm]  
         cv2_im_rgb = cv2.cvtColor(self.visualisation,cv2.COLOR_BGR2RGB)
         # Pass the image to PIL
         pil_im = Image.fromarray(cv2_im_rgb)
         draw = ImageDraw.Draw(pil_im, 'RGBA')
 
-        self.draw_detections_on(draw)
-
-        if include_title:
-            draw.text((10,10), titles[self.algorithm], fill=draw_colors[self.algorithm], font=font)
-
-        return cv2.cvtColor(np.array(pil_im), cv2.COLOR_RGB2BGR)
-    
-    def draw_detections_on(self, draw: ImageDraw):
-        '''
-        Draw on a specified canvas
-        '''
-        color = draw_colors[self.algorithm]  
         for detection in self.detections:
             self.draw_detection(draw, detection, color)
 
+        self.visualisation = cv2.cvtColor(np.array(pil_im), cv2.COLOR_RGB2BGR)
+
     def draw_detection(self, draw: ImageDraw, detection: dict, color: tuple):
         width = 2
 
@@ -87,8 +68,51 @@ class Result():
         color = tuple(color)
         
         draw.rectangle((detection['startX'], detection['startY'], detection['endX'], detection['endY']), outline=color, width=width)
+        # cv2.rectangle(rect_img, (0, 0),
+        #         (sub_img.shape[1]-int(width/2), sub_img.shape[0]-int(width/2)),
+        #         color, width)
         
 
+
+    def draw_detections_cv2(self):
+        color = draw_colors[self.algorithm]
+        for detection in self.detections:
+            self.draw_detection(detection, color)
+    
+    def draw_detection_cv2(self, detection, color=(0,0,255)):
+
+        # First we crop the sub-rect from the image
+        sub_img = self.visualisation[detection['startY']:detection['endY'], detection['startX']:detection['endX']]
+        rect_img = sub_img.copy()
+        width = 2
+        cv2.rectangle(rect_img, (0, 0),
+                (sub_img.shape[1]-int(width/2), sub_img.shape[0]-int(width/2)),
+                color, width)
+        # white_rect = np.ones(sub_img.shape, dtype=np.uint8) * 255
+
+
+        # filter out weak detections by ensuring the `confidence` is
+        # greater than the minimum confidence
+        if detection['confidence'] > self.confidence_threshold:
+            # draw the bounding box of the face along with the associated
+            # probability
+            text = "{:.2f}%".format(detection['confidence'] * 100)
+            y = detection['startY'] - 10 if detection['startY'] - 10 > 10 else detection['startY'] + 10
+            # cv2.rectangle(image, (startX, startY), (endX, endY),
+            #     color, 2)
+            cv2.putText(self.visualisation, text, (detection['startX'], y),
+                cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2, lineType = cv2.LINE_AA)
+
+            alpha = 1
+        else:
+            # At least 10% opacity
+            alpha = max(.3, detection['confidence'])
+
+        res = cv2.addWeighted(sub_img, 1-alpha, rect_img, alpha, 1.0)
+
+        # Putting the image back to its position
+        self.visualisation[detection['startY']:detection['endY'], detection['startX']:detection['endX']] = res
+    
     def resize(self, width, height):
         # TODO resize to new target incl all detections
         img = self.visualisation
@@ -107,9 +131,6 @@ class Result():
                 )
         return result
 
-    def count_detections(self):
-        detections = [d for d in self.detections if d['confidence'] > self.confidence_threshold]
-        return len(detections)
 
 
 def record(device_id, q1,q2, q3, q4, resolution, rotate):
@@ -340,17 +361,7 @@ def process3_haar(in_q, out_q, cascade_file):
     """)
 
     dir_path = os.path.dirname(os.path.realpath(__file__))
-
+    C = ffi.dlopen(os.path.join(dir_path,"../visualhaar/target/debug/libvisual_haarcascades_lib.so"))
-    lib_path = os.path.join(dir_path, "..", "visualhaar", "target", "debug")
-    so_path = os.path.join(lib_path, "libvisual_haarcascades_lib.so")
-    dll_path = os.path.join(lib_path, "visual_haarcascades_lib.dll")
-
-    if os.path.exists(so_path):
-        C = ffi.dlopen(so_path)
-    elif os.path.exists(dll_path):
-        C = ffi.dlopen(dll_path)
-    else:
-        raise RuntimeException("Visual haarcascades library is not found")
 
     # print(C.test(9))
     # i = Image.open("Marjo.jpg")
@@ -426,33 +437,11 @@ def process3_haar(in_q, out_q, cascade_file):
         # print(img)
         out_q.put(result)
 
-def draw_stats(image, results):
+def display(image_res, q1, q2, q3, q4, fullscreen = False):
-    pil_im = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+    prev_image1 = np.zeros((image_res[1],image_res[0],3), np.uint8)
-    draw = ImageDraw.Draw(pil_im, 'RGBA')
+    prev_image2 = np.zeros((image_res[1],image_res[0],3), np.uint8)
-
+    prev_image3 = np.zeros((image_res[1],image_res[0],3), np.uint8)
-    for i, result in enumerate(results):
+    prev_image4 = np.zeros((image_res[1],image_res[0],3), np.uint8)
-        if result is None:
-            continue
-
-        c = result.count_detections()
-        txt = "face" if c == 1 else "faces"
-        txt = f"{result.algorithm.ljust(5)} {c} {txt}"
-        draw.text((10, pil_im.size[1] - i*25 - 50), txt, fill=draw_colors[result.algorithm], font=font_s, stroke_width=1, stroke_fill=(0,0,0))
-
-    return cv2.cvtColor(np.array(pil_im), cv2.COLOR_RGB2BGR)
-    
-
-def display(image_res, q1, q2, q3, q4, fullscreen, output_dir):
-    logger = logging.getLogger('display')
-
-    empty_image = np.zeros((image_res[1],image_res[0],3), np.uint8)
-
-    results = [None, None, None]
-    result_queues = [q2, q3, q4]
-    images = [empty_image, empty_image, empty_image, empty_image]
-
-    override_image = None
-    override_until = None
 
     if fullscreen:
         cv2.namedWindow("output", cv2.WND_PROP_FULLSCREEN)
@@ -461,68 +450,57 @@ def display(image_res, q1, q2, q3, q4, fullscreen, output_dir):
     while True:
         logging.debug('r')
         try:
-            image = q1.get_nowait()
+            image1 = q1.get_nowait()
-            images[0] = cv2.resize(image, (image_res[0], image_res[1]))
+            image1 = cv2.resize(image1, (image_res[0], image_res[1]))
+            prev_image1 = image1
         except Empty as e:
-            pass
+            image1 = prev_image1
+        try:
+            result2 = q2.get_nowait()
+            result2 = result2.resize(image_res[0], image_res[1])
+            result2.draw_detections()
+            image2 = result2.visualisation
+            # image2 = cv2.resize(image2, (image_res[0], image_res[1]))
+            prev_image2 = image2
+        except Empty as e:
+            image2 = prev_image2
+        try:
+            result3 = q3.get_nowait()
+            result3 = result3.resize(image_res[0], image_res[1])
+            result3.draw_detections()
+            image3 = result3.visualisation
+            # image3 = cv2.resize(image3, (image_res[0], image_res[1]))
+            prev_image3 = image3
+        except Empty as e:
+            image3 = prev_image3
+        try:
+            result4 = q4.get_nowait()
+            result4 = result4.resize(image_res[0], image_res[1])
+            result4.draw_detections()
+            image4 = result4.visualisation
+            # image4 = cv2.resize(image4, (image_res[0], image_res[1]))
+            prev_image4 = image4
+        except Empty as e:
+            image4 = prev_image4
 
-        for idx, queue in enumerate(result_queues):
+        img_concate_Verti1 = np.concatenate((image1,image2),axis=0)
-            try:
+        img_concate_Verti2 = np.concatenate((image3,image4),axis=0)
-                result = queue.get_nowait()
+        grid_img = np.concatenate((img_concate_Verti1,img_concate_Verti2),axis=1)
-                results[idx] = result.resize(image_res[0], image_res[1])
+        cv2.imshow("output", grid_img)
-                images[idx+1] = results[idx].draw_detections(include_title=True)
-            except Empty as e:
-                pass
-            finally:
-                 pass
-        
-        if override_image is not None and override_until > time.time():
-            cv2.imshow("output", override_image)
-        else:
-            override_image = None
-
-            images[0] = draw_stats(images[0], results)
-
-            img_concate_Verti1 = np.concatenate((images[0],images[1]),axis=0)
-            img_concate_Verti2 = np.concatenate((images[2],images[3]),axis=0)
-            grid_img = np.concatenate((img_concate_Verti1,img_concate_Verti2),axis=1)
-            cv2.imshow("output", grid_img)
     
         # Hit 'q' on the keyboard to quit!
         key = cv2.waitKey(1) & 0xFF
         if key  == ord('q'):
             break
         if key == ord(' '):
-            # TODO wait for frame to be processed. Eg. if I move and make a pic, it should use the last frame...
+            # TODO save frame
-            output_res = (image_res[0] *2, image_res[1] * 2)
+            pass
-            pil_im = Image.fromarray(cv2.cvtColor(images[0], cv2.COLOR_BGR2RGB))
-            pil_im = pil_im.resize(output_res)
-            draw = ImageDraw.Draw(pil_im, 'RGBA')
 
-            for result in results:
+def main(camera_id, rotate, fullscreen, cascade_file):
-                if result is None:
-                    continue
-                
-                result.resize(output_res[0], output_res[1]).draw_detections_on(draw)
-            
-            override_image = cv2.cvtColor(np.array(pil_im), cv2.COLOR_RGB2BGR)
-            override_until = time.time() + 5
-            logger.info("Show frame until %f", override_until)
-
-            # save images:
-            name = datetime.datetime.now().isoformat(timespec='seconds')
-            cv2.imwrite(os.path.join(output_dir, f'{name}.png'),override_image)
-            for result in results:
-                cv2.imwrite(os.path.join(output_dir, f'{name}-{result.algorithm}.png'),result.visualisation)
-            
-
-def main(camera_id, rotate, fullscreen, cascade_file, output_dir):
     image_size = (int(1920/2), int(1080/2))
 
     if not os.path.exists(cascade_file):
         raise RuntimeError(f"Cannot load OpenCV haar-cascade file '{cascade_file}'")
-    if not os.path.isdir(output_dir):
-        raise RuntimeError(f"Non-existent directory to store files '{output_dir}'")
 
     is_rotated_90 = rotate in [cv2.ROTATE_90_CLOCKWISE, cv2.ROTATE_90_COUNTERCLOCKWISE]
 
@@ -541,7 +519,7 @@ def main(camera_id, rotate, fullscreen, cascade_file, output_dir):
     q_process3 = Queue(maxsize=1)
 
     p1 = Process(target=record, args=(camera_id, q_webcam1, q_webcam2,q_webcam3,q_webcam4, image_size, rotate))
-    p2 = Process(target=display, args=(image_size, q_webcam1, q_process1, q_process2, q_process3, fullscreen, output_dir ))
+    p2 = Process(target=display, args=(image_size, q_webcam1, q_process1, q_process2, q_process3, fullscreen ))
     p3 = Process(target=process1_hog, args=(q_webcam2, q_process1,))
     p4 = Process(target=process2_dnn, args=(q_webcam3, q_process2,))
     p5 = Process(target=process3_haar, args=(q_webcam4, q_process3,cascade_file))

mirror.py

@@ -15,8 +15,6 @@ if __name__ == '__main__':
                         help='Rotate counter clockwise')
     parser.add_argument('--cascade', default='haarcascade_frontalface_alt2.xml',
                         help='Cascade XML file to use (opencv format)')
-    parser.add_argument('--output', default='saves',
-                        help='Directory to store images (after pressing spacebar)')
 
     args = parser.parse_args()
 
@@ -26,4 +24,4 @@ if __name__ == '__main__':
     if args.counter_clockwise:
         rotate = cv2.ROTATE_90_COUNTERCLOCKWISE
 
-    face_recognition.comparison.main(args.camera, rotate, args.fullscreen, args.cascade, args.output)
+    face_recognition.comparison.main(args.camera, rotate, args.fullscreen, args.cascade)

requirements.txt

@@ -2,6 +2,3 @@ scipy
 numpy
 dlib
 Pillow
-opencv-python
-cffi
-scikit-image

visualhaar

@@ -1 +1 @@
-Subproject commit 928da82d24de1ae2cef268c140f9992b0614806b
+Subproject commit 7de5440484842c147944ae123fa689333846dde7