Merge remote-tracking branch 'origin/efe' into yigit

server_side/api/modules/car_crash.py

@@ -75,26 +75,18 @@ def find_name(image):
 		return None
 
 def rotate_img(img, angle):
-	(h, w) = img.shape[:2]
-	x = h if h > w else w
-	y = h if h > w else w
-	square = np.zeros((x, y, 3), np.uint8)
-	square[int((y-h)/2):int(y-(y-h)/2), int((x-w)/2):int(x-(x-w)/2)] = img
-
-	(h, w) = square.shape[:2]
-	center = (w / 2, h / 2)
-	M = cv2.getRotationMatrix2D(center, angle, 1.0)
-	rotated = cv2.warpAffine(square, M, (h, w))
-	return rotated
+    if angle == 90:
+        return np.rot90(img)
+    elif angle == 270:
+        return np.rot90(np.rot90(np.rot90(img)))
 
 def process_img(img_base64):
-
-	url = 'https://%s:5001/ai' % AI_IP # Set destination URL here
-	post_fields = {'img': img_base64,"type":"coco"}     # Set POST fields here
-	request = Request(url, urlencode(post_fields).encode())
-	data = urlopen(request, context=context).read().decode("ascii")
-	output_dict = json.loads(json.loads(data))
-	image_np = cv2.cvtColor(load_image_into_numpy_array(Image.open(io.BytesIO(base64.b64decode(img_base64)))),cv2.COLOR_RGB2BGR)
+    url = 'https://192.168.2.238:5001/ai' # Set destination URL here
+    post_fields = {'img': img_base64,"type":"coco"}     # Set POST fields here
+    request = Request(url, urlencode(post_fields).encode())
+    data = urlopen(request, context=context).read().decode("ascii")
+    output_dict = json.loads(json.loads(data))
+    image_np = cv2.cvtColor(load_image_into_numpy_array(Image.open(io.BytesIO(base64.b64decode(img_base64)))),cv2.COLOR_RGB2BGR)
 
 	output_dict_processed = {"detection_classes":[], "detection_scores":[], "detection_boxes":[]}
 	im_height, im_width, _ = image_np.shape
@@ -124,55 +116,56 @@ def process_img(img_base64):
 		output_dict_processed["detection_scores"].append(output_dict["detection_scores"][box.index])
 		output_dict_processed["detection_boxes"].append(output_dict["detection_boxes"][box.index])
 
-	people = []
-	for index, i in enumerate(output_dict_processed['detection_classes']):
-		score = output_dict_processed['detection_scores'][index]
-		if score > MIN_SCORE_THRESH:
-			if i in VEHICLE_CLASSES:
-				box = output_dict_processed['detection_boxes'][index]
-				(left, right, top, bottom) = (box[1] * im_width, box[3] * im_width,
-											  box[0] * im_height, box[2] * im_height)
-				avg_x = left+right/2
-				avg_y = top+bottom/2
-				same = False
-				for prev_x, prev_y in prev_cars:
-					if abs(prev_x-avg_x) < 130 and abs(prev_y-avg_y) < 130:
-						same = True
-						break
-				if not same:
-					cars_involved += 1
-					prev_cars.append((avg_x, avg_y))
-			elif i == 1:
-				box = output_dict_processed['detection_boxes'][index]
-				(left, right, top, bottom) = (box[1] * im_width, box[3] * im_width,
-				                              box[0] * im_height, box[2] * im_height)
-				#(left, top, right, bottom) = box
-				person = image_np[int(top):int(bottom),int(left):int(right)]
-				if right-left > bottom-top:
-					rotated = rotate_img(person, 90)
-					name = None
-					try:
-						face_locs = face_recognition.face_locations(rotated)[0]
-						name = find_name(rotated)
-					except Exception:
-						pass
-					(height_person,width_person)=person.shape[:2]
-					excess=(width_person-height_person)/2
-
-					if name is None:
-						rotated = rotate_img(person, 270)
-						face_locs = face_recognition.face_locations(rotated)[0]
-						name = find_name(rotated)
-						face_locs_processed = (top + face_locs[1]-excess,left+face_locs[2],top+face_locs[3]-excess,left+face_locs[0])
-					else:
-						face_locs_processed = (top + face_locs[3]-excess,right-face_locs[2],top+face_locs[1]-excess,right-face_locs[0])
-					cv2.destroyAllWindows()
-					people.append([0, face_locs_processed, name])
-				else:
-					face_locs = face_recognition.face_locations(person)[0]
-					face_locs_processed = (top+face_locs[0],left+face_locs[1],top+face_locs[2],left+face_locs[3])
-					name = find_name(person)
-					people.append([1, face_locs_processed, name])
+    people = {}
+    for index, i in enumerate(output_dict['detection_classes']):
+        score = output_dict['detection_scores'][index]
+        if score > MIN_SCORE_THRESH:
+            if i in VEHICLE_CLASSES:
+                box = output_dict['detection_boxes'][index]
+                (left, right, top, bottom) = (box[1] * im_width, box[3] * im_width,
+                                              box[0] * im_height, box[2] * im_height)
+                avg_x = left+right/2
+                avg_y = top+bottom/2
+                same = False
+                for prev_x, prev_y in prev_cars:
+                    if abs(prev_x-avg_x) < 130 and abs(prev_y-avg_y) < 130:
+                        same = True
+                        break
+                if not same:
+                    cars_involved += 1
+                    prev_cars.append((avg_x, avg_y))
+            elif i == 1:
+                box = output_dict['detection_boxes'][index]
+                (left, right, top, bottom) = tuple(map(int, (box[1] * im_width, box[3] * im_width,
+                                              box[0] * im_height, box[2] * im_height)))
+                person = image_np[int(top):int(bottom),int(left):int(right)]
+                if right-left > bottom-top:
+                    rotated = rotate_img(person, 90)
+                    name = None
+                    try:
+                        face_locs = face_recognition.face_locations(rotated)[0]
+                        name = find_name(rotated)
+                    except Exception:
+                        pass
+                    (height_person,width_person) = person.shape[:2]
+
+                    if name is None:
+                        rotated = rotate_img(person, 270)
+                        face_locs = face_recognition.face_locations(rotated)[0]
+                        name = find_name(rotated)
+                        (top_face, right_face, bottom_face, left_face) = face_locs
+                        face_locs_processed = (top + height_person - right_face,left+bottom_face,top + height_person - left_face,left+top_face)
+                    else:
+                        (top_face, right_face, bottom_face, left_face) = face_locs
+                        person = cv2.rectangle(person, (width_person - bottom_face, left_face), (width_person - top_face, right_face), (0, 255, 0), 3)
+                        face_locs_processed = (top + left_face,left + width_person - top_face,top + right_face,left + width_person - bottom_face)
+                    people[index] = [0, face_locs_processed, name]
+                else:
+                    face_locs = face_recognition.face_locations(person)[0]
+                    (top_face, right_face, bottom_face, left_face) = face_locs
+                    face_locs_processed = (top+face_locs[0],left+face_locs[1],top+face_locs[2],left+face_locs[3])
+                    name = find_name(person)
+                    people[index] = [1, face_locs_processed, name]
 
 
 
@@ -194,17 +187,21 @@ class Crash(Resource):
 		id = request.form['id']
 		lat, long = request.form['lat'], request.form['long']
 
-		image, car_count, injured,out,people = process_img(base64_img)
-		(top, right, bottom, left) = people[0][1]
-		top = int(top)
-		right = int(right)
-		left = int(left)
-		bottom = int(bottom)
-		img = load_image_into_numpy_array(Image.open(io.BytesIO(base64.b64decode(base64_img))))
-		print(people)
-		priority = car_count + injured
-		if priority > 10:
-			priority = 10
+        image, car_count, injured,out,people = process_img(base64_img)
+        (top, right, bottom, left) = people[0][1]     
+        top = int(top)
+        right = int(right)
+        left = int(left)
+        bottom = int(bottom)
+        img = load_image_into_numpy_array(Image.open(io.BytesIO(base64.b64decode(base64_img))))
+        cv2.rectangle(img,(left,top),(right,bottom),(0,255,0),3)
+        cv2.imshow('test.jpg', img)
+        cv2.waitKey(0)
+        cv2.destroyAllWindows()
+        print(people)
+        priority = car_count + injured
+        if priority > 10:
+            priority = 10
 
 		crash = {
 			'img': image,