changes for windows compatibility

face_recognition/comparison.py

@@ -10,12 +10,21 @@ from PIL import ImageFont, ImageDraw, Image
 import os
 
 draw_colors = {
-    'hog': (255,0,0),
+    'hog': (198,65,124),
-    'haar': (0,255,0),
+    'haar': (255,255,255),
-    'dnn': (0,0,255),
+    'dnn': (251,212,36),
 }
 
-font = ImageFont.truetype("/home/ruben/Documents/Projecten/2018/PATH/presentation/lib/font/source-sans-pro/source-sans-pro-regular.ttf", 30)
+titles = {
+    '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):
@@ -34,13 +43,17 @@ class Result():
         })
         return self
     
-    def draw_detections(self):
+    def draw_detections(self, include_title = False):
         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):
@@ -94,6 +107,9 @@ 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):
@@ -324,7 +340,17 @@ 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")
@@ -400,62 +426,65 @@ def process3_haar(in_q, out_q, cascade_file):
         # print(img)
         out_q.put(result)
 
+def draw_stats(image, results):
+    pil_im = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+    draw = ImageDraw.Draw(pil_im, 'RGBA')
+
+    for i, result in enumerate(results):
+        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)
-    prev_image1 = None
+
-    prev_result2 = None
+    results = [None, None, None]
-    prev_result3 = None
+    result_queues = [q2, q3, q4]
-    prev_result4 = None
+    images = [empty_image, empty_image, empty_image, empty_image]
+
+    override_image = None
+    override_until = None
 
     if fullscreen:
         cv2.namedWindow("output", cv2.WND_PROP_FULLSCREEN)
         cv2.setWindowProperty("output",cv2.WND_PROP_FULLSCREEN,cv2.WINDOW_FULLSCREEN)
 
-    override_image = None
-    override_until = None
-
     while True:
         logging.debug('r')
         try:
-            image1 = q1.get_nowait()
+            image = q1.get_nowait()
-            image1 = cv2.resize(image1, (image_res[0], image_res[1]))
+            images[0] = cv2.resize(image, (image_res[0], image_res[1]))
-            prev_image1 = image1
         except Empty as e:
-            image1 = prev_image1 if prev_image1 is not None else empty_image
+            pass
-        try:
+        
-            result2 = q2.get_nowait()
+        for idx, queue in enumerate(result_queues):
-            result2 = result2.resize(image_res[0], image_res[1])
+            try:
-            prev_result2 = result2
+                result = queue.get_nowait()
-        except Empty as e:
+                results[idx] = result.resize(image_res[0], image_res[1])
-            result2 = prev_result2
+                images[idx+1] = results[idx].draw_detections(include_title=True)
-        finally:
+            except Empty as e:
-            image2 = result2.draw_detections() if result2 is not None else empty_image
+                pass
-        try:
+            finally:
-            result3 = q3.get_nowait()
+                 pass
-            result3 = result3.resize(image_res[0], image_res[1])
+        
-            prev_result3 = result3
-        except Empty as e:
-            result3 = prev_result3
-        finally:
-            image3 = result3.draw_detections() if result3 is not None else empty_image
-        try:
-            result4 = q4.get_nowait()
-            result4 = result4.resize(image_res[0], image_res[1])
-            prev_result4 = result4
-        except Empty as e:
-            result4 = prev_result4
-        finally:
-            image4 = result4.draw_detections() if result4 is not None else empty_image
-
         if override_image is not None and override_until > time.time():
             cv2.imshow("output", override_image)
         else:
             override_image = None
 
-            img_concate_Verti1 = np.concatenate((image1,image2),axis=0)
+            images[0] = draw_stats(images[0], results)
-            img_concate_Verti2 = np.concatenate((image3,image4),axis=0)
+
+            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)
     
@@ -466,18 +495,16 @@ def display(image_res, q1, q2, q3, q4, fullscreen, output_dir):
         if key == ord(' '):
             # TODO wait for frame to be processed. Eg. if I move and make a pic, it should use the last frame...
             output_res = (image_res[0] *2, image_res[1] * 2)
-            pil_im = Image.fromarray(cv2.cvtColor(image1, cv2.COLOR_BGR2RGB))
+            pil_im = Image.fromarray(cv2.cvtColor(images[0], cv2.COLOR_BGR2RGB))
             pil_im = pil_im.resize(output_res)
             draw = ImageDraw.Draw(pil_im, 'RGBA')
             
-            if result2 is not None:
+            for result in results:
-                result2.resize(output_res[0], output_res[1]).draw_detections_on(draw)
+                if result is None:
-            if result3 is not None:
+                    continue
-                result3.resize(output_res[0], output_res[1]).draw_detections_on(draw)
+                
-            if result4 is not None:
+                result.resize(output_res[0], output_res[1]).draw_detections_on(draw)
-                result4.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)
@@ -485,9 +512,9 @@ def display(image_res, q1, q2, q3, q4, fullscreen, output_dir):
             # save images:
             name = datetime.datetime.now().isoformat(timespec='seconds')
             cv2.imwrite(os.path.join(output_dir, f'{name}.png'),override_image)
-            cv2.imwrite(os.path.join(output_dir, f'{name}-hog.png'),result2.visualisation)
+            for result in results:
-            cv2.imwrite(os.path.join(output_dir, f'{name}-dnn.png'),result3.visualisation)
+                cv2.imwrite(os.path.join(output_dir, f'{name}-{result.algorithm}.png'),result.visualisation)
-            cv2.imwrite(os.path.join(output_dir, f'{name}-haar.png'),result4.visualisation)
+            
 
 def main(camera_id, rotate, fullscreen, cascade_file, output_dir):
     image_size = (int(1920/2), int(1080/2))

requirements.txt

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

visualhaar

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